Computational Drug Discovery with Dyadic Positive-Unlabeled Learning

Liu, Yashu; Qiu, Shuang; Zhang, Ping; Gong, Pinghua; Wang, Fei; Xue, Guoliang; Ye, Jieping

doi:10.1137/1.9781611974973.6

Cited by 18 publications

(6 citation statements)

References 25 publications

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“…In the field of drug discovery, the tasks of drug repositioning, which looks for interactions between drugs and diseases, and drug-drug-interactions are very important. To find these interactions, a pairwise scoring function can be trained so that known interactions score higher than pairs which are not known to interact [64]. The rationale behind this method is similar to RSVM [92].…”

Section: Applicationsmentioning

confidence: 99%

Learning from positive and unlabeled data: a survey

2020

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Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.

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Section: Applicationsmentioning

confidence: 99%

Learning from positive and unlabeled data: a survey

2020

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“…where E is the set of positive hyperlinks, F is the set of negative hyperlinks, and σðÁÞ = log 1 + expðÁÞ ð Þis the logistic function 22,54 . We chose the above loss function since it offers a better performance compared to traditional classification loss functions such as cross entropy loss.…”

Section: Training Algorithmmentioning

confidence: 99%

Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning

2023

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GEnome-scale Metabolic models (GEMs) are powerful tools to predict cellular metabolism and physiological states in living organisms. However, due to our imperfect knowledge of metabolic processes, even highly curated GEMs have knowledge gaps (e.g., missing reactions). Existing gap-filling methods typically require phenotypic data as input to tease out missing reactions. We still lack a computational method for rapid and accurate gap-filling of metabolic networks before experimental data is available. Here we present a deep learning-based method — CHEbyshev Spectral HyperlInk pREdictor (CHESHIRE) — to predict missing reactions in GEMs purely from metabolic network topology. We demonstrate that CHESHIRE outperforms other topology-based methods in predicting artificially removed reactions over 926 high- and intermediate-quality GEMs. Furthermore, CHESHIRE is able to improve the phenotypic predictions of 49 draft GEMs for fermentation products and amino acids secretions. Both types of validation suggest that CHESHIRE is a powerful tool for GEM curation to reveal unknown links between reactions and observed metabolic phenotypes.

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“…The set of unknown relationships i.e., 2 V − E may, in fact, contain undiscovered hyperlinks and belong to the existing ones. Following prior work [26], we rely on a ranking objective as follows:…”

Section: Hyperlink Scoring Layermentioning

confidence: 99%

“…The problem of link prediction in graphs has numerous applications [20] in the fields of social network analysis [25], knowledge bases [29], bioinformatics [26] to name a few. However, in many realworld problems relationships go beyond pairwise associations.…”

Section: Introductionmentioning

confidence: 99%

NHP: Neural Hypergraph Link Prediction

Yadati

Ray

Nimishakavi

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Link prediction in simple graphs is a fundamental problem in which new links between vertices are predicted based on the observed structure of the graph. However, in many real-world applications, there is need to model relationships among vertices which go beyond pairwise associations. For example, in a chemical reaction, relationship among the reactants and products is inherently higherorder. Additionally, there is need to represent the direction from reactants to products. Hypergraphs provide a natural way to represent such complex higher-order relationships. Graph Convolutional Networks (GCN) have recently emerged as a powerful deep learning-based approach for link prediction over simple graphs. However, their suitability for link prediction in hypergraphs is underexplored-we fill this gap in this paper and propose Neural Hyperlink Predictor (NHP). NHP adapts GCNs for link prediction in hypergraphs. We propose two variants of NHP-NHP-U and NHP-D-for link prediction over undirected and directed hypergraphs, respectively. To the best of our knowledge, NHP-D is the first ever method for link prediction over directed hypergraphs. An important feature of NHP is that it can also be used for hyperlinks in which dissimilar vertices interact (e.g. acids reacting with bases). Another attractive feature of NHP is that it can be used to predict unseen hyperlinks at test time (inductive hyperlink prediction). Through extensive experiments on multiple real-world datasets, we show NHP's effectiveness. CCS CONCEPTS • Computing methodologies → Neural networks; Unsupervised learning.

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Computational Drug Discovery with Dyadic Positive-Unlabeled Learning

Cited by 18 publications

References 25 publications

Learning from positive and unlabeled data: a survey

Learning from positive and unlabeled data: a survey

Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning

NHP: Neural Hypergraph Link Prediction

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