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Path Planning problems with Unmanned Aerial Vehicles (UAVs) are among the most studied knowledge areas in the related literature. However, few of them have been applied to groups of UAVs. The use of swarms allows to speed up the flight time and, thus, reducing the operational costs. When combined with Artificial Intelligence (AI) algorithms, a single system or operator can control all aircraft while optimal paths for each one can be computed. In order to introduce the current situation of these AI-based systems, a review of the most novel and relevant articles was carried out. This review was performed in two steps: first, a summary of the found articles; second, a quantitative analysis of the publications found based on different factors, such as the temporal evolution or the number of articles found based on different criteria. Therefore, this review provides not only a summary of the most recent work but it gives an overview of the trend in the use of AI algorithms in UAV swarms for Path Planning problems. The AI techniques of the articles found can be separated into four main groups based on their technique: reinforcement Learning techniques, Evolutive Computing techniques, Swarm Intelligence techniques, and, Graph Neural Networks. The final results show an increase in publications in recent years and that there is a change in the predominance of the most widely used techniques.

Graphical abstract, Highlights • Machine Learning in drug discovery has greatly benefited the pharmaceutical industry. • Application of machine algorithms must entail a robust design in real clinical tasks. • Trending machine learning algorithms in drug design: NB, SVM, RF and ANN., Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

This article belongs to the Special Issue Applied Machine Learning in NIR Technology [Abstract] Among the bovine diseases, mastitis causes high economic losses in the dairy production system. Nowadays, detection under field conditions is mainly performed by the California Mastitis Test, which is considered the de facto standard. However, this method presents with problems of slowness and the expensiveness of the chemical-reactive process, which is deeply dependent on an expert’s trained eye and, consequently, is highly imprecise. The aim of this work is to propose a new method for bovine mastitis detection under field conditions. The proposed method uses a low-cost, smartphone-connected NIR spectrometer which solves the aforementioned problems of slowness, expert dependency and disposability of the chemical methods. This method uses spectra in combination with two k-Nearest Neighbors models. The first model is used to detect the presence of mastitis while the second model classifies the positive cases into weak and strong. The resulting method was validated by using a leave-one-out technique where the ground truth was obtained by the California Mastitis Test. The detection model achieved an accuracy of 92.4%, while the one classifying the severity showed an accuracy of 95%. This work is part of DINTA-UTMACH and RNASA-UDC research groups. This work is partially supported by Instituto de Salud Carlos III, grant number PI17/01826. It was also partially supported by different grants and projects from the Xunta de Galicia [ED431D 2017/23; ED431D 2017/16; ED431G/01; ED431C 2018/49; IN845D-2020/03]. Another source of support was the CYTED network (PCI2018\_093284) funded by the Spanish Ministry of Innovation and Science Xunta de Galicia; ED431D 2017/23 Xunta de Galicia; ED431D 2017/16 Xunta de Galicia; ED431G/01 Xunta de Galicia; ED431C 2018/49 Xunta de Galicia; IN845D-2020/03

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021. [Abstract] During the last few years, some of the most relevant IT companies have started to develop new authentication solutions which are not vulnerable to attacks like phishing. WebAuthn and FIDO authentication standards were designed to replace or complement the de facto and ubiquitous authentication method: username and password. This paper performs an analysis of the current implementations of these standards while testing and comparing these solutions in a high-level analysis, drawing the context of the adoption of these new standards and their integration with the existing systems, from web applications and services to different use cases on desktop and server operating systems. CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80% through ERDF, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2019/01). This project was also supported by the “Consellería de Cultura, Educación e Ordenación Universitaria” via the Consolidation and Structuring of Competitive Research Units—Competitive Reference Groups (ED431C 2018/49). Xunta de Galicia; ED431G 2019/01 Xunta de Galicia; ED431C 2018/49

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021. [Abstract] In the field of computer security, the possibility of knowing which specific version of an operating system is running behind a machine can be useful, to assist in a penetration test or monitor the devices connected to a specific network. One of the most widespread tools that better provides this functionality is Nmap, which follows a rule-based approach for this process. In this context, applying machine learning techniques seems to be a good option for addressing this task. The present work explores the strengths of different machine learning algorithms to perform operating system fingerprinting, using for that, the Nmap reference database. Moreover, some optimizations were applied to the method which brought the best results, random forest, obtaining an accuracy higher than 96%. CITIC, as a research center accredited by the Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported—80% through ERDF, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaría Xeral de Universidades (Grant ED431G 2019/01). This project was also supported by the “Consellería de Cultura, Educación e Ordenación Universitaria” via the Consolidation and Structuring of Competitive Research Units–Competitive Reference Groups (ED431C 2018/49) and the COST Action 17124 DigForAsp, supported by COST (European Cooperation in Science and Technology, www.cost.eu, (accessed on 25 October 2021)). Xunta de Galicia; ED431G 2019/01 Xunta de Galicia; ED431C 2018/49

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021 [Abstract] The past and current situation of the SARS-CoV-2 pandemic has put the entire society, and especially all hospital systems, worldwide to the test. It is essential that health system managers and decision makers optimize the management of resources, even being forced to improvise new units, divert resources usually destined to other functions and/or change the usual care modality by considerably enhancing aspects of telemedicine. Artificial Intelligence (AI) techniques and procedures are of great help in decision making in emergency environments due to severe pandemics because of their predictive capacity. This paper presents the PRACTICUM DIRECT project, which proposes the design and implementation of a tool to assist health system managers in making decisions on the early management of hospital resources. It makes use of AI techniques to identify the most critical variables in each case and build models capable of showing the possibilities and consequences of the decisions taken on resources at each moment of the emergency. It includes a simulator that shows how they would affect management. The current status is that of the selection of the most appropriate variables, taking into account those affected during the SARS-CoV-2 pandemic: infectious diseases, cardio-neuro-circulatory diseases, metabolic diseases and rehabilitative medicine. This research was funded by the General Directorate of Culture, Education and University Management of Xunta de Galicia “PRACTICUM DIRECT” Ref. IN845D-2020/03 and the GRANT FOR THE PROGRAM FOR CONSOLIDATION AND STRUCTURING OF COMPETITIVE RESEARCH UNITS Ref. ED431C 2018/49 Xunta de Galicia; IN845D-2020/03 Xunta de Galicia; ED431C 2018/49

The process of refining the research question in a medical study depends greatly on the current background of the investigated subject. The information found in prior works can directly impact several stages of the study, namely the cohort definition stage. Besides previous published methods, researchers could also leverage on other materials, such as the output of cohort selection tools, to enrich and to accelerate their own work. However, this kind of information is not always captured by search engines. In this paper, we present a methodology, based on a combination of content-based retrieval and text annotation techniques, to identify relevant scientific publications related to a research question and to the selected data sources.

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021. [Abstract] The number of applications using unmanned aerial vehicles (UAVs) is increasing. The use of UAVs in swarms makes many operators see more advantages than the individual use of UAVs, thus reducing operational time and costs. The main objective of this work is to design a system that, using Reinforcement Learning (RL) and Artificial Neural Networks (ANNs) techniques, can obtain a good path for each UAV in the swarm and distribute the flight environment in such a way that the combination of the captured images is as simple as possible. To determine whether it is better to use a global ANN or multiple local ANNs, experiments have been done over the same map and with different numbers of UAVs at different altitudes. The results are measured based on the time taken to find a solution. The results show that the system works with any number of UAVs if the map is correctly partitioned. On the other hand, using local ANNs seems to be the option that can find solutions faster, ensuring better trajectories than using a single global network. There is no need to use additional map information other than the current state of the environment, like targets or distance maps. This research received no external funding.

Personalised treatment is usually needed for hospitalised patients afflicted by secondary illnesses that demand daily medication. Even though clinical guidelines were designed to consider those circumstances exist, current decision-support features fail to assimilate detailed relevant patient information. This creates opportunities for the development of systems capable of performing a real-time evaluation of such data against existing knowledge and providing recommendations during clinical treatments. Herein, we describe a proposal for a new feature to be integrated with electronic health record (EHR) systems which can enrich the health treatment process through the automatic extraction of information from patient medical notes and the aggregation of this novel information in clinical protocols. The purpose of this work is to exploit the historical component of the patient trajectory to improve the performance of clinical decision support systems.

[Abstract] In recent years, machine learning (ML) researchers have changed their focus towards biological problems that are difficult to analyse with standard approaches. Large initiatives such as The Cancer Genome Atlas (TCGA) have allowed the use of omic data for the training of these algorithms. In order to study the state of the art, this review is provided to cover the main works that have used ML with TCGA data. Firstly, the principal discoveries made by the TCGA consortium are presented. Once these bases have been established, we begin with the main objective of this study, the identification and discussion of those works that have used the TCGA data for the training of different ML approaches. After a review of more than 100 different papers, it has been possible to make a classification according to following three pillars: the type of tumour, the type of algorithm and the predicted biological problem. One of the conclusions drawn in this work shows a high density of studies based on two major algorithms: Random Forest and Support Vector Machines. We also observe the rise in the use of deep artificial neural networks. It is worth emphasizing, the increase of integrative models of multi-omic data analysis. The different biological conditions are a consequence of molecular homeostasis, driven by both protein coding regions, regulatory elements and the surrounding environment. It is notable that a large number of works make use of genetic expression data, which has been found to be the preferred method by researchers when training the different models. The biological problems addressed have been classified into five types: prognosis prediction, tumour subtypes, microsatellite instability (MSI), immunological aspects and certain pathways of interest. A clear trend was detected in the prediction of these conditions according to the type of tumour. That is the reason for which a greater number of works have focused on the BRCA cohort, while specific works for survival, for example, were centred on the GBM cohort, due to its large number of events. Throughout this review, it will be possible to go in depth into the works and the methodologies used to study TCGA cancer data. Finally, it is intended that this work will serve as a basis for future research in this field of study. This work was supported by the “Collaborative Project in Genomic Data Integration (CICLOGEN)” PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—“A way to build Europe.” and the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431D 2017/16), the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23) and Competitive Reference Groups (Ref. ED431C 2018/49). CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidades from Xunta de Galicia”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2019/01). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript Xunta de Galicia; ED431D 2017/16 Xunta de Galicia; ED431D 2017/23 Xunta de Galicia; ED431C 2018/49 Xunta de Galicia; ED431G 2019/01

Background Currently, existing biomedical literature repositories do not commonly provide users with specific means to locate and remotely access biomedical databases. Objective To address this issue, we developed the Biomedical Database Inventory (BiDI), a repository linking to biomedical databases automatically extracted from the scientific literature. BiDI provides an index of data resources and a path to access them seamlessly. Methods We designed an ensemble of deep learning methods to extract database mentions. To train the system, we annotated a set of 1242 articles that included mentions of database publications. Such a data set was used along with transfer learning techniques to train an ensemble of deep learning natural language processing models targeted at database publication detection. Results The system obtained an F1 score of 0.929 on database detection, showing high precision and recall values. When applying this model to the PubMed and PubMed Central databases, we identified over 10,000 unique databases. The ensemble model also extracted the weblinks to the reported databases and discarded irrelevant links. For the extraction of weblinks, the model achieved a cross-validated F1 score of 0.908. We show two use cases: one related to “omics” and the other related to the COVID-19 pandemic. Conclusions BiDI enables access to biomedical resources over the internet and facilitates data-driven research and other scientific initiatives. The repository is openly available online and will be regularly updated with an automatic text processing pipeline. The approach can be reused to create repositories of different types (ie, biomedical and others).

[Abstract] The study of the intestinal microbiota is one of the greatest challenges in today’s clinical environment. Thus, probiotics have been established as a focus for its stability, as they play a key role in its regulation. The development of an automated technique that allows the practitioners the smooth search for the optimal probiotic is postulated as the main objective of this study. Despite the existence of previous attempts at applications for this purpose, they have only been carried out for the countries of origin, preventing them from being used in others such as Spain. Therefore, a system has been developed with open, multi-platform, and free technologies, which manages to locate the optimal probiotic for each pathology. Xunta de Galicia; ED431G/01 Xunta de Galicia; ED431D 2017/16 Xunta de Galicia; ED431D 2017/23

Cohort studies have been playing a key role in helping our understanding of diseases, health conditions, and treatments. These cohorts are often composed of a small number of subjects, especially in rare diseases studies, which reduces the statistical power of the results. One solution that can strengthen the scientific findings is to combine distinct studies and perform then multi-cohort analysis. However, even studies conducted for the same purpose in distinct research groups can have different scopes and medical observations, which preclude across-cohort exploration. In this paper, we propose a recommendation system to automatically discover cohorts of interest. This methodology uses context-based retrieval techniques combined with collaborative filtering to find relevant cohorts and scientific literature about a specific clinical investigation. The system was validated in a community focused on the study of Alzheimer's diseases, which includes 62 cohorts.

[Abstract] Nowadays, among the Deep Learning works, there is a tendency to develop networks with millions of trainable parameters. However, this tendency has two main drawbacks: overfitting and resource consumption due to the low-quality features extracted by those networks. This paper presents a study focused on the scoring of sleeping EEG signals to measure if the increase of the pressure on the features due to a reduction of the number though different techniques results in a benefit. The work also studies the convenience of increasing the number of input signals in order to allow the network to extract better features. Additionally, it might be highlighted that the presented model achieves comparable results to the state-of-the-art with 1000 times less trainable and the presented model uses the whole dataset instead of the simplified versions in the published literature. This work has been partially funded by the Carlos III Health Institute and the European Regional Development Funds (FEDER) [PI17/01826]. It was also partially supported by different grants and projects from the Xunta de Galicia [ED431D 2017/23; ED431D 2017/16; ED431G/01; ED431C 2018/49] Xunta de Galicia; ED431D 2017/23 Xunta de Galicia; ED431D 2017/16 Xunta de Galicia; ED431G/01 Xunta de Galicia; ED431C 2018/49

[Abstract] Early detection is crucial to prevent the progression of Alzheimer’s disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the diagnosis of AD in the earliest and hardest to detect stages. The main objective of this work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI), which are not generally used. Sagittal MRIs from ADNI and OASIS data sets were employed. Experiments were conducted using Transfer Learning (TL) techniques in order to achieve more accurate results. There are two main conclusions to be drawn from this work: first, the damages related to AD and its stages can be distinguished in sagittal MRI and, second, the results obtained using DL models with sagittal MRIs are similar to the state-of-the-art, which uses the horizontal-plane MRI. Although sagittal-plane MRIs are not commonly used, this work proved that they were, at least, as effective as MRI from other planes at identifying AD in early stages. This could pave the way for further research. Finally, one should bear in mind that in certain fields, obtaining the examples for a data set can be very expensive. This study proved that DL models could be built in these fields, whereas TL is an essential tool for completing the task with fewer examples. This work is supported by the “Collaborative Project in Genomic Data Integration (CICLOGEN)” PI17/01826 funded by the Carlos III Health Institute in the context of the Spanish National Plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—”A way to build Europe”. This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23), Competitive Reference Groups (Ref. ED431C 2018/49) and the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) Xunta de Galicia; ED431G/01 Xunta de Galicia; ED431D 2017/16 Xunta de Galicia; ED431D 2017/23 Xunta de Galicia; ED431C 2018/49

Brain Connectome Networks (BCNs) are defined by brain cortex regions (nodes) interacting with others by electrophysiological co-activation (edges). The experimental prediction of new interactions in BCNs represents a difficult task due to the large number of edges and the complex connectivity patterns. Fortunately, we can use another special type of networks to achieve this goal&mdash, Artificial Neural Networks (ANNs). Thus, ANNs could use node descriptors such as Shannon Entropies (Sh) to predict node connectivity for large datasets including complex systems such as BCN. However, the training of a high number of ANNs for BCNs is a time-consuming task. In this work, we propose the use of a method to automatically determine which ANN topology is more efficient for the BCN prediction. Since a network (ANN) is used to predict the connectivity in another network (BCN), this method was entitled Net-Net AutoML. The algorithm uses Sh descriptors for pairs of nodes in BCNs and for ANN predictors of BCNs. Therefore, it is able to predict the efficiency of new ANN topologies to predict BCNs. The current study used a set of 500,470 examples from 10 different ANNs to predict node connectivity in BCNs and 20 features. After testing five Machine Learning classifiers, the best classification model to predict the ability of an ANN to evaluate node interactions in BCNs was provided by Random Forest (mean test AUROC of 0.9991 &plusmn, 0.0001, 10-fold cross-validation). Net-Net AutoML algorithms based on entropy descriptors may become a useful tool in the design of automatic expert systems to select ANN topologies for complex biological systems. The scripts and dataset for this project are available in an open GitHub repository.

[Abstract] Hospitalised patients suffering from secondary illnesses that require daily medication typically need personalised treatment. Although clinical guidelines were designed considering those circumstances, existing decision-support features fail in assimilating detailed relevant patient information, which opens up opportunities for systems capable of performing a real-time evaluation of such data against existing knowledge and providing recommendations during clinical treatments. In this paper, we present a proposal for a new feature to integrate with electronic health record (EHR) systems that enriches the health treatment process by automatically extracting information from patient medical notes and aggregating it in clinical protocols. Our goal is to leverage the historical component of the patient trajectory to improve clinical decision support systems performance. EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking; 806968 NETDIAMOND project; POCI-01-0145-FEDER-016385 Foundation for Science and Technology; PD/BD/142878/2018 Foundation for Science and Technology; SFRH/BD/147837/2019

[Abstract] Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19–95.25% for training and 89.22–95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds. Ministerio de Economía y Competitividad; CTQ2016-74881-P Ministerio de Economía y Competitividad; UNLC08-1E-002 Ministerio de Economía y Competitividad; UNLC13-13-3503 Xunta de Galicia; ED431C 2018/49 Xunta de Galicia; ED431D 2017/16 Xunta de Galicia; ED431G/01 Xunta de Galicia; ED431D 2017/23 Gobierno Vasco; IT1045-16 Instituto de Salud Carlos III; PI17/01826

[Abstract] Cardiovascular diseases, particularly severe stenosis, are the main cause of death in the western world. The primary method of diagnosis, considered to be the standard in the detection and quantification of stenotic lesions, is a coronary angiography. This article proposes a new automatic multiscale segmentation algorithm for the study of coronary trees that offers results comparable to the best existing semi-automatic method. According to the state-of-the-art, a representative number of coronary angiography images that ensures the generalisation capacity of the algorithm has been used. All these images were selected by clinics from an Haemodynamics Unit. An exhaustive statistical analysis was performed in terms of sensitivity, specificity and Jaccard. Algorithm improvements imply that the clinician can perform tests on the patient and, bypassing the images through the system, can verify, in that moment, the intervention of existing differences in a coronary tree from a previous test, in such a way that it could change its clinical intra-intervention criteria. Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049 Ministerio de Economía y Competitividad; TIN2015-70648-P

[Abstract] The measurements of Near-Infrared (NIR) Spectroscopy, combined with data analysis techniques, are widely used for quality control in food production processes. This paper presents a methodology to optimize the calibration models of NIR spectra in four different stages in a sugar factory. The models were designed for quality monitoring, particularly °Brix and Sucrose, both common parameters in the sugar industry. A three stage optimization methodology, including pre-processing selection, feature selection and support vector machines regression metaparameters tuning, were applied to the spectral data divided by repeated cross-validation. Global models were optimized while endeavoring to ensure they are able to estimate both quality parameters with a single calibration, for the four steps of the process. The proposed models improve the prediction for the test set (unseen data) compared to previously published models, resulting in a more accurate quality assessment of the intermediate products of the process in the sugar industry.

BackgroundDruggable proteins are a trending topic in drug design. The druggable proteome can be defined as the percentage of proteins that have the capacity to bind an antibody or small molecule with adequate chemical properties and affinity. The screening and in silico modeling are critical activities for the reduction of experimental costs.MethodsThe current work proposes a unique prediction model for druggable proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. After feature selection, the best classifier was obtained using the support vector machine method and 200 tri-amino acid composition descriptors.ResultsThe high performance of the model is determined by an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and accuracy of 0.929 ± 0.006 (3-fold cross-validation). Regarding the prediction of cancer-associated proteins using this model, the best ranked druggable predicted proteins in the breast cancer protein set were CDK4, AP1S1, POLE, HMMR, RPL5, PALB2, TIMP1, RPL22, NFKB1 and TOP2A; in the cancer-driving protein set were TLL2, FAM47C, SAGE1, HTR1E, MACC1, ZFR2, VMA21, DUSP9, CTNNA3 and GABRG1; and in the RNA-binding protein set were PLA2G1B, CPEB2, NOL6, LRRC47, CTTN, CORO1A, SCAF11, KCTD12, DDX43 and TMPO.ConclusionsThis powerful model predicts several druggable proteins which should be deeply studied to find better therapeutic targets and thus improve clinical trials. The scripts are freely available at https://github.com/muntisa/machine-learning-for-druggable-proteins.

[Abstract] The artificial neural networks used in a multitude of fields are achieving good results. However, these systems are inspired in the vision of classical neuroscience where neurons are the only elements that process information in the brain. Advances in neuroscience have shown that there is a type of glial cell called astrocytes that collaborate with neurons to process information. In this work, a connectionist system formed by neurons and artificial astrocytes is presented. The astrocytes can have different configurations to achieve a biologically more realistic behaviour. This work indicates that the use of different artificial astrocytes behaviours is beneficial. Xunta de Galicia; ED431G/01 Xunta de Galicia; ED431D 2017/16 Xunta de Galicia; ED431D 2017/23 Ministerio de Economía y Competitividad; UNLC08-1E-002 Ministerio de Economía y Competitividad; UNLC13-13-3503

Sleeping problems have become one of the major diseases all over the world. To tackle this issue, the basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep. After its recording, the specialists have to score the different signals according to one of the standard guidelines. This process is carried out manually, which can be highly time-consuming and very prone to annotation errors. Therefore, over the years, many approaches have been explored in an attempt to support the specialists in this task. In this paper, an approach based on convolutional neural networks is presented, where an in-depth comparison is performed in order to determine the convenience of using more than one signal simultaneously as input. Additionally, the models were also used as parts of an ensemble model to check whether any useful information can be extracted from signal processing a single signal at a time which the dual-signal model cannot identify. Tests have been performed by using a well-known dataset called expanded sleep-EDF, which is the most commonly used dataset as the benchmark for this problem. The tests were carried out with a leave-one-out cross-validation over the patients, which ensures that there is no possible contamination between training and testing. The resulting proposal is a network smaller than previously published ones, but which overcomes the results of any previous models on the same dataset. The best result shows an accuracy of 92.67\% and a Cohen's Kappa value over 0.84 compared to human experts., Comment: 20 pages, 4 figures, 4 tables

Biomedical informatics has skyrocketed in the last years by reducing sequencing costs with next-generation sequencing techniques. Thus, the amount of available data to study is increasing excessively, and more recently, is even open access for researchers. Due to this, biomedical informatics researchers, with different profiles, are using machine learning algorithms for knowledge extraction and, despite the great amount of benefits this entails, it also requires to take into account a series of particularities of inexcusable compliance in order to achieve a solution which is real.

This article reviews some recent achievements reported in the area of Translational Bioinformatics (TBI), which has evolved rapidly as result of the Human Genome Project and subsequent -omic projects. Our goal is to support the understanding and enhancement of informatics research and applications at the intersection between medicine and the -omics fields. We discuss current progress and directions in the road ahead for this field, which already involves a significant number of dedicated professionals in research projects and conferences. Through a literature review, a list of topics of informatics research in TBI has been created, including decision support systems, natural language processing, standards, information retrieval, data, text and opinion mining, electronic health records (EHRs), and data integration. We also describe examples of the most challenging categories for research, such as discovery in EHRs, pharmacogenomics, drug repurposing, and genomic testing for individuals. We conclude with an overview of some of the challenges and opportunities presented by this field for research and education, particularly from the perspective of precision medicine.

In this work, we improved a previous model used for the prediction of proteomes as new B-cell epitopes in vaccine design. The predicted epitope activity of a queried peptide is based on its sequence, a known reference epitope sequence under specific experimental conditions. The peptide sequences were transformed into molecular descriptors of sequence recurrence networks and were mixed under experimental conditions. The new models were generated using 709,100 instances of pair descriptors for query and reference peptide sequences. Using perturbations of the initial descriptors under sequence or assay conditions, 10 transformed features were used as inputs for seven Machine Learning methods. The best model was obtained with random forest classifiers with an Area Under the Receiver Operating Characteristics (AUROC) of 0.981 &plusmn, 0.0005 for the external validation series (five-fold cross-validation). The database included information about 83,683 peptides sequences, 1448 epitope organisms, 323 host organisms, 15 types of in vivo processes, 28 experimental techniques, and 505 adjuvant additives. The current model could improve the in silico predictions of epitopes for vaccine design. The script and results are available as a free repository.

Traditionally, signal classification is a process in which previous knowledge of the signals is needed. Human experts decide which features are extracted from the signals, and used as inputs to the classification system. This requirement can make significant unknown information of the signal be missed by the experts and not be included in the features. This paper proposes a new method that automatically analyses the signals and extracts the features without any human participation. Therefore, there is no need for previous knowledge about the signals to be classified. The proposed method is based on Genetic Programming and, in order to test this method, it has been applied to a well-known EEG database related to epilepsy, a disease suffered by millions of people. As the results section shows, high accuracies in classification are obtained, Comment: Conference paper, doble-column, 7 pages, 1 figure, 3 tables

[Abstract] Screening and in silico modeling are critical activities for the reduction of experimental costs. They also speed up research notably and strengthen the theoretical framework, thus allowing researchers to numerically quantify the importance of a particular subset of information. For example, in fields such as cancer and other highly prevalent diseases, having a reliable prediction method is crucial. The objective of this paper is to classify peptide sequences according to their anti-angiogenic activity to understand the underlying principles via machine learning. First, the peptide sequences were converted into three types of numerical molecular descriptors based on the amino acid composition. We performed different experiments with the descriptors and merged them to obtain baseline results for the performance of the models, particularly of each molecular descriptor subset. A feature selection process was applied to reduce the dimensionality of the problem and remove noisy features – which are highly present in biological problems. After a robust machine learning experimental design under equal conditions (nested resampling, cross-validation, hyperparameter tuning and different runs), we statistically and significantly outperformed the best previously published anti-angiogenic model with a generalized linear model via coordinate descent (glmnet), achieving a mean AUC value greater than 0.96 and with an accuracy of 0.86 with 200 molecular descriptors, mixed from the three groups. A final analysis with the top-40 discriminative anti-angiogenic activity peptides is presented along with a discussion of the feature selection process and the individual importance of each molecular descriptors According to our findings, anti-angiogenic activity peptides are strongly associated with amino acid sequences SP, LSL, PF, DIT, PC, GH, RQ, QD, TC, SC, AS, CLD, ST, MF, GRE, IQ, CQ and HG. Instituto de Salud Carlos III; PI17/01826 Xunta de Galiciia; ED431G/01 Xunta de Galicia; ED431D 2017/16 Xunta de Galicia; ED431D 2017/23 Ministerio de Economía Y Competitividad; UNLC08-1E-002 Ministerio de Economía y Competitividad; UNLC13-13-3503

La informacion mas relevante de las angiografias coronarias se extrae empleando tecnicas de segmentacion, que pueden ser automaticas, semiautomaticas o manuales. Existen numerosos algoritmos de segmentacion vascular, obteniendo mejores resultados, por normal general, aquellos que emplean multiples tecnicas e imagenes a diferentes escalas para proporcionar los resultados requeridos. Se presenta un nuevo metodo de segmentacion automatizado basado en el metodo manual de Hamarneh incluyendo un estudio estadistico que demuestra su idoneidad para el problema.

Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relationships (links). Experimental confirmation of all possible links is difficult and generates a huge volume of information. Consequently, computational prediction becomes an important goal. Artificial Neural Networks (ANNs) are Machine Learning (ML) algorithms that may be used to predict BENs, using as input Shannon entropy information measures (Sh(k)) of known ecosystems to train them. However, it is difficult to select a priori which ANN topology will have a higher accuracy. Interestingly, Auto Machine Learning (AutoML) methods focus on the automatic selection of the more efficient ML algorithms for specific problems. In this work, a preliminary study of a new approach to AutoML selection of ANNs is proposed for the prediction of BENs. We call it the Net-Net AutoML approach, because it uses for the first time Shk values of both networks involving BENs (networks to be predicted) and ANN topologies (networks to be tested). Twelve types of classifiers have been tested for the Net-Net model including linear, Bayesian, trees-based methods, multilayer perceptrons and deep neuronal networks. The best Net-Net AutoML model for 338,050 outputs of 10 ANN topologies for links of 69 BENs was obtained with a deep fully connected neuronal network, characterized by a test accuracy of 0.866 and a test AUROC of 0.935. This work paves the way for the application of Net-Net AutoML to other systems or ML algorithms. The authors acknowledge Basque Government (Eusko Jaurlaritza) grant (IT1045-16) - 2016-2021 for consolidated research groups. This work was supported by the "Collaborative Project in Genomic Data Integration (CICLOGEN)" PI17/01826 funded by the Carlos III Health Institute, as part of the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER). This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and "Drug Discovery Galician Network" Ref. ED431G/01 and the "Galician Network for Colorectal Cancer Research" (Ref. ED431D 2017/23), and finally by the Spanish Ministry of Economy and Competitiveness for its support through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union. CR Munteanu acknowledges the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

[Abstract] Predicting drug–protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex big data sets of preclinical assays reported in public databases. This includes multiple conditions of assays, such as different experimental parameters, biological assays, target proteins, cell lines, organism of the target, or organism of assay. On the other hand, perturbation theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previously known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL data set of preclinical assays of compounds targeting dopamine pathway proteins. The best PTML model found predicts 50000 cases with accuracy of 70–91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical–experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition, we performed a molecular docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound in new assays. This PTML method focuses on the prediction with a simple linear model of multiple pharmacological parameters (IC50, EC50, Ki, etc.) for compounds in assays involving different cell lines used, organisms of the protein target, or organism of assay for proteins in the dopamine pathway. Ministerio de Economía y Competitividad; CTQ2016-74881-P Gobierno Vasco; IT1045-16 Xunta de Galicia; GPC2014/003 Xunta de Galicia; CN 2012/069 Xunta de Galicia; ED431D 2017/16 Xunta de Galicia; ED431D 2017/23 Xunta de Galicia; GRC2014/049 Xunta de Galicia; ED431D 2017/23

[Abstract] 1. The purpose of this work was to support decision-making in poultry farms by performing automatic early detection of anomalies in egg production. 2. Unprocessed data were collected from a commercial egg farm on a daily basis over 7 years. Records from a total of 24 flocks, each with approximately 20 000 laying hens, were studied. 3. Other similar works have required a prior feature extraction by a poultry expert, and this method is dependent on time and expert knowledge. 4. The present approach reduces the dependency on time and expert knowledge because of the automatic selection of relevant features and the use of artificial neural networks capable of cost-sensitive learning. 5. The optimum configuration of features and parameters in the proposed model was evaluated on unseen test data obtained by a repeated cross-validation technique. 6. The accuracy, sensitivity, specificity and positive predictive value are presented and discussed at 5 forecasting intervals. The accuracy of the proposed model was 0.9896 for the day before a problem occurs. Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049

We can represent the brain as a Brain Connectome Network (BCN) formed by ni brain cortex regions interacting with others (Lij = 1) or not (Lij = 0). The large number of links to be study and their complex connectivity patterns made difficult to select the appropriate topology in order to predict them with Artificial Neural Networks (ANNs) algorithms. In this context, Automated Machine Learning (AutoML) techniques may help non-experts to select, train, validate, and use automatically the correct algorithms. In this work, we developed a new AutoML Screening Model for ANN (SMANN) algorithm to solve this problem. We can quantify topological (connectivity) information of both the complex networks under study and a set of ANNs trained using Shannon measures. The SMANN model presented >85% of accuracy for 52690 outputs of 10 different ANNs for 52690 BCN links.

[Abstract] Introduction. The introduction of omics data and advances in technologies involved in clinical treatment has led to a broad range of approaches to represent clinical information. Within this context, patient stratification across health institutions due to omic profiling presents a complex scenario to carry out multi-center clinical trials. Methods. This paper presents a standards-based approach to ensure semantic integration required to facilitate the analysis of clinico-genomic clinical trials. To ensure interoperability across different institutions, we have developed a Semantic Interoperability Layer (SIL) to facilitate homogeneous access to clinical and genetic information, based on different well-established biomedical standards and following International Health (IHE) recommendations. Results. The SIL has shown suitability for integrating biomedical knowledge and technologies to match the latest clinical advances in healthcare and the use of genomic information. This genomic data integration in the SIL has been tested with a diagnostic classifier tool that takes advantage of harmonized multi-center clinico-genomic data for training statistical predictive models. Conclusions. The SIL has been adopted in national and international research initiatives, such as the EURECA-EU research project and the CIMED collaborative Spanish project, where the proposed solution has been applied and evaluated by clinical experts focused on clinico-genomic studies. Instituto de Salud Carlos III, PI13/02020 Instituto de Salud Carlos III, PI13/00280

Biomedical researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies. It also can be customized to fit the needs of different scenarios. Ontology Recommender 2.0 combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available., Comment: 29 pages, 8 figures, 11 tables

Lectins (Ls) play an important role in many diseases such as different types of cancer, parasitic infections and other diseases. Interestingly, the Protein Data Bank (PDB) contains +3000 protein 3D structures with unknown function. Thus, we can in principle, discover new Ls mining non-annotated structures from PDB or other sources. However, there are no general models to predict new biologically relevant Ls based on 3D chemical structures. We used the MARCH-INSIDE software to calculate the Markov-Shannon 3D electrostatic entropy parameters for the complex networks of protein structure of 2200 different protein 3D structures, including 1200Ls. We have performed a Linear Discriminant Analysis (LDA) using these parameters as inputs in order to seek a new Quantitative Structure-Activity Relationship (QSAR) model, which is able to discriminate 3D structure of Ls from other proteins. We implemented this predictor in the web server named LECTINPred, freely available at http://bio-aims.udc.es/LECTINPred.php. This web server showed the following goodness-of-fit statistics: Sensitivity=96.7% (for Ls), Specificity=87.6% (non-active proteins), and Accuracy=92.5% (for all proteins), considering altogether both the training and external prediction series. In mode 2, users can carry out an automatic retrieval of protein structures from PDB. We illustrated the use of this server, in operation mode 1, performing a data mining of PDB. We predicted Ls scores for +2000 proteins with unknown function and selected the top-scored ones as possible lectins. In operation mode 2, LECTINPred can also upload 3D structural models generated with structure-prediction tools like LOMETS or PHYRE2. The new Ls are expected to be of relevance as cancer biomarkers or useful in parasite vaccine design.

[Abstract] The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order kth (Wk). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the Wk(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated Wk(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation).

Research article [Abstract] Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected.

[Abstract In recent years, in the post genomic era, more and more data is being generated by biological high throughput technologies, such as proteomics and transcriptomics. This omics data can be very useful, but the real challenge is to analyze all this data, as a whole, after integrating it. Biomedical data integration enables making queries to different, heterogeneous and distributed biomedical data sources. Data integration solutions can be very useful not only in the context of drug design, but also in biomedical information retrieval, clinical diagnosis, system biology, etc. In this review, we analyze the most common approaches to biomedical data integration, such as federated databases, data warehousing, multi-agent systems and semantic technology, as well as the solutions developed using these approaches in the past few years. Red Gallega de Investigación sobre Cáncer Colorrectal; Ref. 2009/58 Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT- 0366 Instituto de Salud Carlos III; PIO52048 Instituto de Salud Carlos III; RD07/0067/0005 Ministerio de Industria, Turismo y Comercio; TSI-020110-2009-5

In this work, we introduce by the first time a new type of algorithm aimed to predict the more promising topology of one ANN to be trained in order to model a given dataset of complex system. In so doing, we can quantify topological (connectivity) information of both the complex networks under study and a set of ANNs trained using Shannon measures. Using information parameters as inputs, we developed one scheduler for 338050 outputs of 10 different ANNs for the respective 33805 pair of nodes in 73 Biological Networks. The overall accuracy of the SANN-HPC schedulers found was of >72% for Biological Networks; in training and validation series.

The International Classification of Functioning, Disability and Health (ICF) (WHO, 2001) provides the term disability as a part of the multi-aspects of the interaction between the individual and social environmental context in which it operates. Therefore a disabled person is a person who has impairment, activity limitation or participation restriction. A person in this situation can present difficulties in occupational performance. It is necessary to develop a set of resources, technological or otherwise, to offset these difficulties, decrease the distance between exclusion and participation and contribute to the integration of people with functional diversity in society. These resources are called support products or technology support, but do not eliminate the deficits, they can eliminate the limitation of the performance of persons with disabilities.

[Abstract] The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. Ministerio de Ciencia e Innovación; TIN2009—07707 Red Gallega de Investigación sobre Cáncer Colorrectal; 2009/58 Xunta de Galicia; 08SIN010105PR Xunta de Galicia; 10SIN105004PR Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; Ibero-NBIC Network 209RT0366

The peptide biological screening represents a difficult task due to the complexity of the amino-acid sequences. One solution is the encoding of the molecular information using complex networks or graphs of the peptides into QSAR-like models in Web tools. Bio-AIMS contains free Web tools on an Artificial Intelligence Model Server in Biosciences: http://bio- aims.udc.es/TargetPred.php. These in silico peptide screening tools are implementing models to predict different protein activities, drug - protein and protein - protein interactions. The inputs are using 3D protein structures or 1D peptide amino acid sequences and the SMILES formulas for drugs, and the classification models are based on Machine Learning techniques. The Web tools are implemented using Python, PHP and XHTML programming languages. SciForum

Introduction: It was observed that technology developers expressed a clear interest to design programs that meet the needs of individuals with Autism Spectrum Disorder (ASD). Several authors indicate that any software designed for people with ASD has to include special requirements in the design. Methodology: This research study describes a software for children with ASD named Module ASD and describes the interactive process of design that it was followed. This research focuses on a software development and the design process, based on scientific evidence study, consultation and tests done by specialists, children with ASD and their families. The techniques used to formalize the collection of information from different groups of participants were: observation, interview, group discussions and field book. Results: The ASD Module is the result of the study and it is a free technological application that is made up of a set of virtual keyboards (or adapted interfaces), digital schedules and activities, especially designed and tested by and for children with ASD. The application is included in the In-TIC PC software. The software is available for the Windows operating system and was implemented with the Visual Studio development tool, .NET environment, C# programming language and Windows Form technology. Other materials used for content development were the Interactive Books Multimedia (LIM according to the Spanish acronym) software and the ARASAAC pictograms. Discussion and conclusions: The results show that the digital content can be oriented to promote independence in several daily activities (ADL, education, leisure and social participation). This technology design provides useful information for researchers, developers, social and healthcare professionals and families, with the aim of offering alternatives for children with ASD and facilitating the understanding of daily life. SciForum

Different research projects around the world are trying to emulate the human brain. They employ diverse types of computational models: digital models, analog models and hybrid models. This communication includes a summary of some main projects, as well as future trends in this subject. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). In addition, given the proven importance of glial cells in information processing, the importance of considering astrocytes into the brain computational models is pointed out.

Neuroimaging data from magnetic resonance techniques are widely used as non- invasive biomarkers for the evaluation and early diagnosis of Alzheimer's Disease (AD). Alzheimer Prediction by Spectroscopy Voxel Volume is a free Web tool to predict the AD diagnosis (AlzPred-SVV: http://bio-aims.udc.es/AlzPredSVV.php). The inputs are two variables related to a voxel acquired in the left hippocampus: The total volume and the volume of CSF contained in the voxel. The classification method is based on Machine Learning techniques. The tool is based on an HTML/PHP user interface with a Python/Java implementation of the model.

This paper applies genetic programming (GP) to perform automatic feature extraction from original feature database with the aim of improving the discriminatory performance of a classifier and reducing the input feature dimensionality at the same time. The tree structure of GP naturally represents the features, and a new function generated in this work automatically decides the number of the features extracted. In experiments on two common epileptic EEG detection problems, the classification accuracy on the GP-based features is significant higher than on the original features. Simultaneously, the dimension of the input features for the classifier is much smaller than that of the original features.

The development of Artificial Neural Networks (ANNs) is traditionally a slow process in which human experts are needed to experiment on different architectural procedures until they find the one that presents the correct results that solve a specific problem. This work describes a new technique that uses Genetic Programming (GP) in order to automatically develop simple ANNs, with a low number of neurons and connections. Experiments have been carried out in order to measure the behavior of the system and also to compare the results obtained using other ANN generation and training methods with evolutionary computation (EC) tools. The obtained results are, in the worst case, at least comparable to existing techniques and, in many cases, substantially better. As explained herein, the system has other important features such as variable discrimination, which provides new information on the problems to be solved.

Epilepsy is the most prevalent neurological disorder in humans after stroke. Recurrent seizure is the main characteristic of the epilepsy. Electroencephalogram (EEG) is the recording of brain electrical activity and it contains valuable information related to the different physiological states of the brain. Thus, EEG is considered an indispensable tool for diagnosing epilepsy in clinic applications. Since epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recordings is highly required. Multiwavelets, which contain several scaling and wavelet functions, offer orthogonality, symmetry and short support simultaneously, which is not possible for scalar wavelet. With these properties, recently multiwavelets have become promising in signal processing applications. Approximate entropy is a measure that quantifies the complexity or irregularity of the signal. This paper presents a novel method for automatic epileptic seizure detection, which uses approximate entropy features derived from multiwavelet transform and combines with an artificial neural network to classify the EEG signals regarding the existence or absence of seizure. To the best knowledge of the authors, there exists no similar work in the literature. A well-known public dataset was used to evaluate the proposed method. The high accuracy obtained for two different classification problems verified the success of the method.