Drug Target Identification Using Side-Effect Similarity

By Monica Campillos, Michael Kuhn, A. Gavin, Lars Juhl Jensen, Peer Bork. 2008

1,189 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Pharmacology

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Citations in the corpus

The SIDER database of drugs and side effects (2015, background): “For example, it was shown that ADRs can be used to predict drug targets (1) and that causal relations between targets and ADRs can be elucidated (2,3).”
Prediction of Drug Combinations by Integrating Molecular and Pharmacological Data (2011, background): “In total, 117 drug pairs approved as effective combinations until November 2010 have specific targets, when metabolizing enzymes and unspecific protein binders are excluded [16].”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “Shortest path and random walk analysis, as well as the identification of tight clusters, bridges and bottlenecks of these networks (Campillos et al., 2008; Yamanishi et al., 2010; Oprea et al., 2011; Takarabe et al., 2012) combined with the selective optimization of side activities (Wermuth, 2006)…”
Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory (2016, methodology): “…repositioned drugs have been based on various computational methods, such as mapping gene expression profiles using drug response profiles [10 – 14], the use of side-effect-based similarities [15 – 17], heterogeneous network clustering [18], and the graph-based inference method [19 – 22].”
Systematic integration of biomedical knowledge prioritizes drugs for repurposing (2017, background): “…Iorio et al., 2013 ), protein interactions ( Guney et al., 2016 ), genetic association ( Nelson et al., 2015 ; Sanseau et al., 2012 ), drug side effects ( Campillos et al., 2008 ; Nugent et al., 2016 ), disease symptoms ( Zhou et al., 2014 ), and molecular pathways ( Pratanwanich and Lio´, 2014 ).”
Discovery of drug mode of action and drug repositioning from transcriptional responses (2010, neutral): “Prediction of drug MoA has been attempted by using gene expression profiles following drug treatment (7 – 13), by comparing side-effect similarities (14), by text-mining literature (15), or by applying chemoinformatic tools to search for small molecules similarities (16, 17).”
Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces (2010, neutral): “Our future work, will incorporate more sophisticated and biologically relevant information into the kernel similarity, such as side effect [15], to improve the prediction accuracy.”
Network-based machine learning and graph theory algorithms for precision oncology (2017, background): “The paper 64 derived drug – drug similarities based on mining the side-effect description from medical symptoms in the Uni ﬁ ed Medical Language System ontology.”
Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects (2015, background): “The advantages of leveraging clinical phenotypic information lies on two aspects: (1) clinical phenotypic information could serve as biomarkers for both therapeutic effects 16,17 and toxic effect 18 , thus has potential to be used in DDI predictions.”
A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information (2017, neutral): “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.”
Target identification using drug affinity responsive target stability (DARTS) (2009, methodology): “Developing new methods for drug target identification is an area of intense interest, and both experimental and computational approaches have been developed (28, 29).”
Network-based in silico drug efficacy screening (2016, background): “The increasing interest in reusing existing drugs for novel therapies has recently given rise to various approaches that aim to identify candidate drugs with similar characteristics to known drugs used in a disease 7,8,19–20 .”
Exploiting drug-disease relationships for computational drug repositioning (2011, background): “[42] performed a systematic analysis to identify novel drug–target relationships for 746 approved drugs using a side effect similarity approach.”