Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces

By Zheng Xia, Ling-Yun Wu, Xiaobo Zhou, Stephen T. C. Wong. BMC Systems Biology, 2010

356 citations recorded by the source corpus.

BackgroundPredicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data.ResultsUsing the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG.ConclusionsWe report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/026f4f3ca37ba15e8bed131ed1a89936255dc0db

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Citations in the corpus

Network-based machine learning and graph theory algorithms for precision oncology (2017, methodology): “The paper 72 proposed a manifold regularization semi-supervised learning method in which two classi ﬁ ers in drug space and target space are learned and then combined to give a ﬁ nal score for drug – target interaction prediction.”
Machine Learning in Drug Discovery: A Review (2021, background): “Finally, in this article, drug–protein interactions of various data sets such as ions, enzymes, and nuclear receptors provided well predictable results (Xia et al. 2010).”
Mining breast cancer genes with a network based noise-tolerant approach (2013, background): “However, different data categories usually contain inherent noise or systematic errors [25].”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “Indeed, the difficulty of the drug discoverytaskliesintherarityofexistingdrug–geneinteractions [6], and a major risk is in unexpected/unintended interaction of drugs with off-target proteins, i.e. side effects [7–9].”
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions (2018, background): “, a regularized least square framework) for predicting new DTIs [8, 9, 10].”
Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014, methodology): “NetLapRLS is a modification of LapRLS to consider drug–target interactions more directly [29].”
Deep learning in drug discovery: an integrative review and future challenges (2022, methodology): “(2018), outperformed moderate ML approaches such as KronRLS (Nascimento et al.”
SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines (2017, background): “Regularized Least Squares Models (RLS) have previously been shown to be able to predict binary drug target interaction with high accuracy [31].”
Collaborative matrix factorization with multiple similarities for predicting drug-target interactions (2013, methodology): “These datasets were collected from four general databases and frequently used in predicting drug-target interactions [2, 28, 26, 9].”
A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information (2017, methodology): “[76] proposed a semi-supervised learning method for DTI prediction, called NetLapRLS, which applies Laplacian regularized least square and incorporates both similarity and interaction kernels into the prediction framework.”
Drug-target interaction prediction by learning from local information and neighbors (2013, methodology): “The BLM method has been further studied and improved in Xia et al. (2010) and Laarhoven et al. (2011).”
Predicting drug-target interactions using restricted Boltzmann machines (2013, methodology): “Unlike previous network-based approaches (Bleakley and Yamanishi, 2009; Chen et al ., 2012; Cheng et al ., 2012; Mei et al ., 2012; van Laarhoven et al ., 2011; Xia et al ., 2010; Yamanishi et al ., 2008), which only predict binary DTIs, our approach not only identifies new DTIs but also infers thei”