Search

Data Availability Statement: The code to load and explore the dataset is publicly available from this GitHub repository: https://github.com/alina-miron/intellirehabds (accessed on 29 April 2021). The code was tested on Mac OS, Ubuntu and Windows using python 3.6. This dataset is available from the following Zenodo repository: https://zenodo.org/record/4610859 (accessed on 29 April 2021). Acknowledgments: We thank Pusat Rehabilitasi Perkeso Melaka centre for their support in recording the data and the volunteers who performed the movements: https://rehab.perkeso.gov.my/one/ (accessed on 29 April 2021) . Copyright: © 2021 by the authors. In this article, we present a dataset that comprises different physical rehabilitation movements. The dataset was captured as part of a research project intended to provide automatic feedback on the execution of rehabilitation exercises, even in the absence of a physiotherapist. A Kinect motion sensor camera was used to record gestures. The dataset contains repetitions of nine gestures performed by 29 subjects, out of which 15 were patients and 14 were healthy controls. The data are presented in an easily accessible format, provided as 3D coordinates of 25 body joints along with the corresponding depth map for each frame. Each movement was annotated with the gesture type, the position of the person performing the gesture (sitting or standing) as well as a correctness label. The data are publicly available and were released with to provide a comprehensive dataset that can be used for assessing the performance of different patients while performing simple movements in a rehabilitation setting and for comparing these movements with a control group of healthy individuals Innovate UK grant number 102719 and MIGHT (Malaysia Industry-Government for High Technology), application No (62873-455315).

© The Author(s) 2019. The goal of quantitative structure activity relationship (QSAR) learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound. We employed multi-task learning (MTL) to exploit commonalities in drug targets and assays. We used datasets containing curated records about the activity of specific compounds on drug targets provided by ChEMBL. Totally, 1091 assays have been analysed. As a baseline, a single task learning approach that trains random forest to predict drug activity for each drug target individually was considered. We then carried out feature-based and instance-based MTL to predict drug activities. We introduced a natural metric of evolutionary distance between drug targets as a measure of tasks relatedness. Instance-based MTL significantly outperformed both, feature-based MTL and the base learner, on 741 drug targets out of 1091. Feature-based MTL won on 179 occasions and the base learner performed best on 171 drug targets. We conclude that MTL QSAR is improved by incorporating the evolutionary distance between targets. These results indicate that QSAR learning can be performed effectively, even if little data is available for specific drug targets, by leveraging what is known about similar drug targets. This research was funded by the Engineering and Physical Sciences Research Council (EPSRC) grant EP/K030469/1. NS would like to thank the EU PhenoM-eNal project (Horizon 2020, 654241)

This paper presents an effective product design and manufacturing process based ontology for manufacturing knowledge reuse. While a number of related efforts exist in the literature, there lacks a granular, interconnected product design and manufacturing process based ontology that can lead to greater industry adoption and knowledge reuse. In particular, the proposed ontology leverages an established industry standard to connect product design and manufacturing process knowledge in a logical and effective manner. The resulting effort is a general ontological framework that can be widely employed by the manufacturing industry. Additionally, the feasibility of implementing the ontology enabled knowledge reuse framework is demonstrated through a real-world case study.

One of the classical problems in computer vision is the automatic identification of different gestures or motions from a sequence of frames, captured via cameras or sensors, that usually contain angles or positions of many body joints. A large body of research addressing this problem exists in the literature. However, an interesting variant is not just to identify the gesture, but to decide whether a certain gesture is correctly or incorrectly executed. This can be in the context of physical exercises for rehabilitation in healthcare, robotics, sports, sign language and more. In this work we present our research on the automatic classification of gestures into correct or incorrect categories using the following three different but powerful techniques: convolutional neural networks (CNNs), long short-term memory (LSTM) neural networks and a technique based on rough path theory to extract unique sequences (i.e. path signatures) from gesture data and to use them as inputs to classical classifiers such as RandomForest (RF) and k-Nearest Neighbor (kNN). We collected the data we used as part of a collaborative project from Perkeso Tun Razak Rehabilitation Centre, Malacca, Malaysia. We recorded 17 patients performing nine types of gestures. For controls, we recorded 14 healthy individuals performing the same gestures. Our cross subject evaluation yields high accuracy for several subjects and gestures. CNNs and LSTMs achieve accuracy values up to 100% for several subjects in many gestures with the highest average accuracy for some gestures reaching 92%. The best overall classifier when using the rough paths representation was kNN as its average accuracy can reach 96.74% (100% accuracy for a subset of subjects in some gestures). For the three methods, the average accuracy across all gestures is 85.24%, 85.63% and 94.67% respectively. Overall, our research shows a great potential in identifying the right learning algorithm in motion correctness identification for real data.

Motivation Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. Results We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science. Availability and implementation The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. Supplementary information Supplementary data are available at Bioinformatics online.

International audience; Background Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. Findings PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. Conclusions PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and omics research domains.

Summary As large-scale metabolic phenotyping studies become increasingly common, the need for systemic methods for pre-processing and quality control (QC) of analytical data prior to statistical analysis has become increasingly important, both within a study, and to allow meaningful inter-study comparisons. The nPYc-Toolbox provides software for the import, pre-processing, QC and visualization of metabolic phenotyping datasets, either interactively, or in automated pipelines. Availability and implementation The nPYc-Toolbox is implemented in Python, and is freely available from the Python package index https://pypi.org/project/nPYc/, source is available at https://github.com/phenomecentre/nPYc-Toolbox. Full documentation can be found at http://npyc-toolbox.readthedocs.io/ and exemplar datasets and tutorials at https://github.com/phenomecentre/nPYc-toolbox-tutorials.

Metabolic phenotyping is entering the era of Big Data, leading to new opportunities and challenges. Cloud computing has been proposed as a novel paradigm, but as yet is not widely understood or used. In this chapter we introduce the concepts of Big Data and cloud computing, and discuss how they might change the landscape of metabolic phenotyping and analysis. We highlight some of the reasons for the increase in data size and explain advantages and disadvantages of large-scale computing in this context. We illustrate the area with a survey of software tools and databases currently available, and describe the newly developed cloud infrastructure “PhenoMeNal,” which will enable widespread use of these approaches. We conclude the chapter with a discussion of the important ethical, legal, and social implications (ELSI) of large-scale computing in this rapidly developing field.

The computational demands for scientific applications are continuously increasing. The emergence of cloud computing has enabled on-demand resource allocation. However, relying solely on infrastructure as a service does not achieve the degree of flexibility required by the scientific community. Here we present a microservice-oriented methodology, where scientific applications run in a distributed orchestration platform as software containers, referred to as on-demand, virtual research environments. The methodology is vendor agnostic and we provide an open source implementation that supports the major cloud providers, offering scalable management of scientific pipelines. We demonstrate applicability and scalability of our methodology in life science applications, but the methodology is general and can be applied to other scientific domains.

High-resolution mass spectrometry permits simultaneous detection of thousands of different metabolites in biological samples; however, their automated annotation still presents a challenge due to the limited number of tailored computational solutions freely available to the scientific community.Here, we introduce ChemDistiller, a customizable engine that combines automated large-scale annotation of metabolites using tandem MS data with a compiled database containing tens of millions of compounds with pre-calculated 'fingerprints' and fragmentation patterns. Our tests using publicly and commercially available tandem MS spectra for reference compounds show retrievals rates comparable to or exceeding the ones obtainable by the current state-of-the-art solutions in the field while offering higher throughput, scalability and processing speed.Source code freely available for download at https://bitbucket.org/iAnalytica/chemdistillerpython.Supplementary data are available at Bioinformatics online.

We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study of meta-learning. This application area is of the highest societal importance, as it is a key step in the development of new medicines. The standard QSAR learning problem is: given a target (usually a protein) and a set of chemical compounds (small molecules) with associated bioactivities (e.g. inhibition of the target), learn a predictive mapping from molecular representation to activity. Although almost every type of machine learning method has been applied to QSAR learning there is no agreed single best way of learning QSARs, and therefore the problem area is well-suited to meta-learning. We first carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 6 molecular representations, applied to more than 2,700 QSAR problems. (These results have been made publicly available on OpenML and represent a valuable resource for testing novel meta-learning methods.) We then investigated the utility of algorithm selection for QSAR problems. We found that this meta-learning approach outperformed the best individual QSAR learning method (random forests using a molecular fingerprint representation) by up to 13%, on average. We conclude that meta-learning outperforms base-learning methods for QSAR learning, and as this investigation is one of the most extensive ever comparisons of base and meta-learning methods ever made, it provides evidence for the general effectiveness of meta-learning over base-learning., Comment: 33 pages and 15 figures. Manuscript accepted for publication in Machine Learning Journal. This is the author's pre-print version

In chemical literature much information is given in the form of diagrams depicting molecules. In order to access this information diagrams have to be recognised and translated into a processable format. We present an approach that models the principal recognition steps for molecule diagrams in a strictly rule based system, providing rules to identify the main components - atoms and bonds - as well as to resolve possible ambiguities. The result of the process is a translation into a graph representation that can be used for further processing. We show the effectiveness of our approach by describing its embedding into a full recognition system and present an experimental evaluation that demonstrates how our current implementation outperforms the leading open source system currently available.