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Multifactorial diseases demand therapeutics that can modulate multiple targets for enhanced safety and efficacy, yet the clinical approval of multitarget drugs remains rare. The integration of machine learning (ML) and deep learning (DL) in drug discovery has revolutionized virtual screening. This study investigates the synergy between ML/DL methodologies, molecular representations, and data augmentation strategies. Notably, we found that SVM can match or even surpass the performance of state-of-the-art DL methods. However, conventional data augmentation often involves a trade-off between the true positive rate and false positive rate. To address this, we introduce Negative-Augmented PU-bagging (NAPU-bagging) SVM, a novel semi-supervised learning framework. By leveraging ensemble SVM classifiers trained on resampled bags containing positive, negative, and unlabeled data, our approach is capable of managing false positive rates while maintaining high recall rates. We applied this method to the identification of multitarget-directed ligands (MTDLs), where high recall rates are critical for compiling a list of interaction candidate compounds. Case studies demonstrate that NAPU-bagging SVM can identify structurally novel MTDL hits for ALK-EGFR with favorable docking scores and binding modes, as well as pan-agonists for dopamine receptors. The NAPU-bagging SVM methodology should serve as a promising avenue to virtual screening, especially for the discovery of MTDLs. [ABSTRACT FROM AUTHOR]

In this study, a novel approach for solving the PU learning problem is proposed based on an anomaly detection strategy. A Convolutional Autoencoder (CAE) is used to extract latent encodings from positive-labeled data, which are then linearly combined to acquire new samples that lie between them. These new samples were used as embeddings to define a boundary that approximates the positive class. Data points that were significantly different from the majority of the data were assumed to be negative samples. Once a set of negative samples is obtained, the problem can be treated as a typical binary-classification problem. This approach was evaluated using benchmark image datasets, CIFAR-10 and Fashion-MNIST, yielding F1-scores of 91.96% and 94.80% on the two datasets respectively. These results demonstrate the efficacy of Dense-PU in enhancing classification performance in identifying negative samples in unlabeled data.

Multifactorial diseases demand therapeutics that can modulate multiple targets for enhanced safety and efficacy, yet the clinical approval of multitarget drugs remains rare. The integration of machine learning (ML) and deep learning (DL) in drug discovery has revolutionized virtual screening. This study investigates the synergy between ML/DL methodologies, molecular representations, and data augmentation strategies. Notably, we found that SVM can match or even surpass the performance of state-of-the-art DL methods. However, conventional data augmentation often involves a trade-off between the true positive rate and false positive rate. To address this, we introduce Negative-Augmented PU-bagging (NAPU-bagging) SVM, a novel semi-supervised learning framework. By leveraging ensemble SVM classifiers trained on resampled bags containing positive, negative, and unlabeled data, our approach is capable of managing false positive rates while maintaining high recall rates. We applied this method to the identification of multitarget-directed ligands (MTDLs), where high recall rates are critical for compiling a list of interaction candidate compounds. Case studies demonstrate that NAPU-bagging SVM can identify structurally novel MTDL hits for ALK-EGFR with favorable docking scores and binding modes, as well as pan-agonists for dopamine receptors. The NAPU-bagging SVM methodology should serve as a promising avenue to virtual screening, especially for the discovery of MTDLs.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Constructing a bibliographic dataset is fundamental for domain analysis in bibliometric research. However, irrelevant documents(so-called "impurities") in the initial domain dataset are inevitable and difficult to identify, requiring considerable human efforts to eliminate. To solve this problem, we propose a weak-supervised noise reduction approach based on the Positive-Unlabeled Learning (PU-Learning) algorithm to clean the initial bibliographic dataset automatically. The basic idea is to use a batch of "absolutely positive sample sets" already available in the dataset to obtain a collection of "reliable negative sample sets," based on which a training set can be constructed for the downstream supervised classification. This paper conducted a comparative experiment using the Artificial Intelligence (AI) domain of the US National Technical Reports Library (NTIS) report as an example. We compared schemes with different variables to explore the influence of various technical aspects on the final noise reduction performance. Our approach achieved significant improvements compared with the similarity-based unsupervised baseline; the recall rose from 0.3742 to 0.8103, and the precision rose from 0.6621 to 0.7383. We found that the impact of document representation algorithms is crucial while classification strategies and s_ratio in PU-Learning are not. Our approach needs no manual annotation data and thus can provide powerful help for bibliometric researchers to construct high-quality bibliographic datasets. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed solution provides a quick approach for the annotation of the necessary training data to create an application-specific machine learning model that can be used to annotate medical image studies. A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious and error-prone manual labeling with respect to both tumor and non-tumor areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled tumor areas. This is achieved by using positive and unlabeled learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach. [ABSTRACT FROM AUTHOR]

A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious and error-prone manual labeling with respect to both tumor and non-tumor areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled tumor areas. This is achieved by using positive and unlabeled learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach.

Wildfire is one of the most common natural hazards in the world. Fire risk estimation for the purposes of risk reduction is an important aspect in disaster studies around the world. The aim of this research was to develop a machine learning workflow process for South East China to monitor fire risks over a large region by learning from a grid file database containing a time series of several of the important environmental parameters largely extracted from remote sensing data products, and highlight areas as fire risk or non-fire risk over a couple of weeks in the future. The study employed fire threshold and the transductive PU learning method to identify reliable non-fire/negative training samples from the grid file database using fire/positive training samples, labeled using the MODIS MCD14ML fire location product. Different models were trained for the three natural vegetation land covers, namely evergreen broadleaf forest, mixed forest, and woody savannas in the study area. On the test dataset, the three models exhibited high sensitivity (>80%) by identifying the majority of fires in the test dataset for all land covers. The use of the reliable negatives identified though the fire threshold and PU learning process resulted in low precision and accuracy. During the model verification process, the model for the mixed forest land cover performed the best with 70% of verification fires falling within the classified fire zone. It was found that the better representation of mixed forest in the training samples made this model perform more reliably as compared to others. Improving the individual models constructed for different land covers and combining them can provide fire classification for a larger region. There is room to improve the spatial precision of fire cell classification. Introducing finer scale features that have higher correlation with fire activity and exhibit high spatial variability seems a viable way forward.

Complex diseases seriously affect people's physical and mental health. The discovery of disease-causing genes has become a target of research. With the emergence of bioinformatics and the rapid development of biotechnology, to overcome the inherent difficulties of the long experimental period and high cost of traditional biomedical methods, researchers have proposed many gene prioritization algorithms that use a large amount of biological data to mine pathogenic genes. However, because the currently known gene–disease association matrix is still very sparse and lacks evidence that genes and diseases are unrelated, there are limits to the predictive performance of gene prioritization algorithms. Based on the hypothesis that functionally related gene mutations may lead to similar disease phenotypes, this paper proposes a PU induction matrix completion algorithm based on heterogeneous information fusion (PUIMCHIF) to predict candidate genes involved in the pathogenicity of human diseases. On the one hand, PUIMCHIF uses different compact feature learning methods to extract features of genes and diseases from multiple data sources, making up for the lack of sparse data. On the other hand, based on the prior knowledge that most of the unknown gene–disease associations are unrelated, we use the PU-Learning strategy to treat the unknown unlabeled data as negative examples for biased learning. The experimental results of the PUIMCHIF algorithm regarding the three indexes of precision, recall, and mean percentile ranking (MPR) were significantly better than those of other algorithms. In the top 100 global prediction analysis of multiple genes and multiple diseases, the probability of recovering true gene associations using PUIMCHIF reached 50% and the MPR value was 10.94%. The PUIMCHIF algorithm has higher priority than those from other methods, such as IMC and CATAPULT.

We study the problem of estimation and testing in logistic regression with class-conditional noise in the observed labels, which has an important implication in the Positive-Unlabeled (PU) learning setting. With the key observation that the label noise problem belongs to a special sub-class of generalized linear models (GLM), we discuss convex and non-convex approaches that address this problem. A non-convex approach based on the maximum likelihood estimation produces an estimator with several optimal properties, but a convex approach has an obvious advantage in optimization. We demonstrate that in the lowdimensional setting, both estimators are consistent and asymptotically normal, where the asymptotic variance of the non-convex estimator is smaller than the convex counterpart. We also quantify the efficiency gap which provides insight into when the two methods are comparable. In the high-dimensional setting, we show that both estimation procedures achieve ℓ2-consistency at the minimax optimal √s logp/n rates under mild conditions. Finally, we propose an inference procedure using a de-biasing approach. We validate our theoretical findings through simulations and a real-data example. [ABSTRACT FROM AUTHOR]

In various real-world problems, we are presented with classification problems with positive and unlabeled data, referred to as presence-only responses. In this article we study variable selection in the context of presence only responses where the number of features or covariates p is large. The combination of presence-only responses and high dimensionality presents both statistical and computational challenges. In this article, we develop the PUlasso algorithm for variable selection and classification with positive and unlabeled responses. Our algorithm involves using the majorization-minimization framework which is a generalization of the well-known expectation-maximization (EM) algorithm. In particular to make our algorithm scalable, we provide two computational speed-ups to the standard EM algorithm. We provide a theoretical guarantee where we first show that our algorithm converges to a stationary point, and then prove that any stationary point within a local neighborhood of the true parameter achieves the minimax optimal mean-squared error under both strict sparsity and group sparsity assumptions. We also demonstrate through simulations that our algorithm outperforms state-of-the-art algorithms in the moderate p settings in terms of classification performance. Finally, we demonstrate that our PUlasso algorithm performs well on a biochemistry example. for this article are available online. [ABSTRACT FROM AUTHOR]

Copyright of Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• The PU-learning method based on semi-supervised learning is proposed for carbonate log facies identification. • PU-learning converts traditional "all in one" pattern to better "one by one" pattern. • The weighted loss function of the classifier ANN is used to optimize machine learning models. • The effect of our semi-supervised learning strategy is compared with other supervised learning methods. Facies identification using conventional log data is one of the most important contents for reservoir fine characterization, where some machine learning methods have been applied due to the advantages of automatic interpretation. The application effect of commonly used supervised learning methods depends on whether there are large amounts of precisely labeled samples, but it is time-consuming and laborious to manually label enough log facies samples. Based on the idea of semi-supervised learning, we have proposed the multiclass positive and unlabeled machine learning (PU-learning) which uses labeled data and unlabeled data simultaneously to identify 5 types of carbonate log facies including carbonate bedrock, collapsed cave, cave-filling of sand and mud, dissolution fracture and unfilled cave. This method can further use the feature information of a large number of unlabeled samples to alleviate the overfitting caused by insufficient labeled samples without adding any additional labeling costs for model construction. An actual application for two carbonate well blocks in the Tahe Oilfield shows that when using only limited labeled log facies data, PU-learning outperforms supervised support vector machine (Supervised-SVM) and supervised artificial neural network (Supervised-ANN) in general. Especially for minority classes like cave-filling of sand and mud in the imbalanced dataset, the evaluation indexes Recall and F 1 significantly improved by PU-learning show that the semi-supervised learning strategy is of great meaning to the actual automatic log interpretation work. [ABSTRACT FROM AUTHOR]

Complex diseases seriously affect people's physical and mental health. The discovery of disease-causing genes has become a target of research. With the emergence of bioinformatics and the rapid development of biotechnology, to overcome the inherent difficulties of the long experimental period and high cost of traditional biomedical methods, researchers have proposed many gene prioritization algorithms that use a large amount of biological data to mine pathogenic genes. However, because the currently known gene-disease association matrix is still very sparse and lacks evidence that genes and diseases are unrelated, there are limits to the predictive performance of gene prioritization algorithms. Based on the hypothesis that functionally related gene mutations may lead to similar disease phenotypes, this paper proposes a PU induction matrix completion algorithm based on heterogeneous information fusion (PUIMCHIF) to predict candidate genes involved in the pathogenicity of human diseases. On the one hand, PUIMCHIF uses different compact feature learning methods to extract features of genes and diseases from multiple data sources, making up for the lack of sparse data. On the other hand, based on the prior knowledge that most of the unknown gene-disease associations are unrelated, we use the PU-Learning strategy to treat the unknown unlabeled data as negative examples for biased learning. The experimental results of the PUIMCHIF algorithm regarding the three indexes of precision, recall, and mean percentile ranking (MPR) were significantly better than those of other algorithms. In the top 100 global prediction analysis of multiple genes and multiple diseases, the probability of recovering true gene associations using PUIMCHIF reached 50% and the MPR value was 10.94%. The PUIMCHIF algorithm has higher priority than those from other methods, such as IMC and CATAPULT., (Copyright © 2020 Wang, Zhang, Wang, Han and Guo.)