Drug-target interaction prediction by random walk on the heterogeneous network.

By Xing Chen, Mingxi Liu, Guiying Yan. Molecular Biosystems, 2012

483 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/10865c58b78bbaab4f6214ac4f076b0be70cd527

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In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

A review on machine learning principles for multi-view biological data integration (2016, methodology): “Random walk methods have been applied on either two-relational heterogeneous networks (such as gene–phenotype associations [88], drug–target interactions [89] and miRNA–disease associations [90, 91]) or multi-relational heterogeneous networks (for example, drug–disease associations [92]) to infer no”
Drug-target interaction prediction by learning from local information and neighbors (2013, background): “Other than *To whom correspondence should be addressed. inferring interactions from the drug similarity or target similarity, network-based inference was also studied in Cheng et al. (2012), which infers or predicts drug–target interactions based on the topology of the known interaction network.”
Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment (2019, methodology): “For example, the Random Walk (Chen et al., 2012) algorithm is a commonly used network clustering algorithm, which starts from a random node (drug, target, or disease) and calculates the similarity of this node and its adjacent node to construct a “drug-target-disease” network.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “Analysis of pathogen interactomes involving random walks or known drug resistance-related proteins plus gene expression changes revealed pathways often involved in resistance development helping cotarget determination (Raman & Chandra, 2008; Chen et al., 2012b).”
Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm (2016, methodology): “In contrast, only 427, 148 and 84 known associations are predicted at top one by HGBI, NBI and DrugNet, respectively.”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “Seal et al. [231] developed a standalone R and Shiny package called Netpredictor based on Random Walk with Restart (NRWRH) [196, 202] and NBI [195, 209] to predict any missing links between drugs, proteins and drug–proteins in any unipartite or bipartite.”
Predicting drug-target interactions using restricted Boltzmann machines (2013, background): “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”
Network-based machine learning and graph theory algorithms for precision oncology (2017, methodology): “The paper 73 applied several random-walk methods on a heterogeneous network of drug – drug similarities, target – target similarities and drug – target relations such that the global structure among all the networks can be used to improve the prediction of new drug – target pairs.”