Discovery of drug mode of action and drug repositioning from transcriptional responses

By F. Iorio, R. Bosotti, E. Scacheri, Vincenzo Belcastro, Pratibha Mithbaokar, R. Ferriero, L. Murino, Roberto Tagliaferri, N. Brunetti‐Pierri, A. Isacchi and 1 more. Proceedings of the National Academy of Sciences of the United States of America, 2010

755 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/37f2e1c2b39f2ada14a902fd7c103bbee8a6f687

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Citations in the corpus

Computational Drug Repositioning: From Data to Therapeutics (2013, background): “23 The authors identified groups of tightly connected drugs with shared modes of action as defined by the manual curation of data including Anatomical Therapeutic Chemical codes.”
A survey of current trends in computational drug repositioning (2016, methodology): “Other methods include rescreening the existing pharmacopeia against new targets to uncover the novel drug indications [14], looking for similarities of molecular activities [15], or exploring the relationships between drugs and diseases.”
Data Fusion by Matrix Factorization (2013, background): “Identification of the mechanisms of action of chemical compounds is a crucial task in drug discovery [63], [64].”
Prediction of Drug Combinations by Integrating Molecular and Pharmacological Data (2011, background): “to drug-target predictions [7,8,9,10], a number of computational”
A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information (2017, background): “In addition to chemical and genomic data, previous works have incorporated pharmacological or phenotypic information, such as side-effect [7, 41], transcriptional response data [24], drug-disease associations [70], public gene expression data [18, 53] and functional data [77] for DTI prediction.”
DrugComboRanker: drug combination discovery based on target network analysis (2014, background): “First, dysfunctional signaling networks of diseases are complex and stable, and effective drug combinations can inhibit major modules of the networks simultaneously.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “binedwithdisease-inducedgeneexpressionchanges, disease–drug associations, interactome s, or signaling networks), was used to suggest unexpected, novel uses of existing drugs ( Hu & Agarwal, 2009; Iorio et al., 2010 ,MANTRAserver,http://mantra.tigem. it; Kotelnikova et al., 2010; Suthram et al., ”
Predicting drug-target interactions using restricted Boltzmann machines (2013, methodology): “In this article, we mainly use this term to represent the following three specific types of interactions: binding, activation and inhibition.”
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference (2012, background): “Iorio et al. predicted and validated new drug modes of action and drug repositioning from transcriptional responses [14].”
Network-based machine learning and graph theory algorithms for precision oncology (2017, methodology): “The paper 65 developed a method to predict similarities in terms of drug effect by comparing gene expression pro ﬁ les following drug treatment across multiple cell lines and dosages.”