Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction

Effective drug combinations in breast, colon and pancreatic cancer cells

(2022) Patricia Jaaks et al.   NATURE

TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations

(2021) Qiao Liu et al.   PLoS Computational Biology

SSI–DDI: substructure–substructure interactions for drug–drug interaction prediction

(2021) Arnold K Nyamabo et al.   BRIEFINGS IN BIOINFORMATICS

MVGCN: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite networks

(2021) Haitao Fu et al.   BIOINFORMATICS

Modeling drug combination effects via latent tensor reconstruction

(2021) Tianduanyi Wang et al.   BIOINFORMATICS

Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction

(2021) Omid Bazgir et al.   BIOINFORMATICS

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

(2021) Jinxian Wang et al.   BRIEFINGS IN BIOINFORMATICS

GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction

(2021) Xuan Liu et al.   BRIEFINGS IN BIOINFORMATICS

MatchMaker: A Deep Learning Framework for Drug Synergy Prediction

(2021) Halil Ibrahim Kuru et al.   IEEE-ACM Transactions on Computational Biology and Bioinformatics

DTF: Deep Tensor Factorization for Predicting Anticancer Drug Synergy

(2020) Zexuan Sun et al.   BIOINFORMATICS

SynergyFinder 2.0: visual analytics of multi-drug combination synergies

(2020) Aleksandr Ianevski et al.   NUCLEIC ACIDS RESEARCH

Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design

(2020) Xiang Liu et al.   BRIEFINGS IN BIOINFORMATICS

Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects

(2020) Heli Julkunen et al.   Nature Communications

A Comprehensive Survey on Graph Neural Networks

(2020) Zonghan Wu et al.   IEEE Transactions on Neural Networks and Learning Systems

Hypergraph convolution and hypergraph attention

(2020) Song Bai et al.   PATTERN RECOGNITION

Publisher Correction: Network-based prediction of drug combinations

(2019) Feixiong Cheng et al.   Nature Communications

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

(2019) Michael P. Menden et al.   Nature Communications

In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data

(2019) Remzi Celebi et al.   Scientific Reports

Network Propagation Predicts Drug Synergy in Cancers

(2018) Hongyang Li et al.   CANCER RESEARCH

PubChem 2019 update: improved access to chemical data

(2018) Sunghwan Kim et al.   NUCLEIC ACIDS RESEARCH

An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies

(2016) Jennifer O'Neil et al.   MOLECULAR CANCER THERAPEUTICS

Synergistic activity of vorinostat combined with gefitinib but not with sorafenib in mutant KRAS human non-small cell lung cancers and hepatocarcinoma

(2016) Victor Jeannot et al.   OncoTargets and Therapy

COSMIC: exploring the world's knowledge of somatic mutations in human cancer

(2014) Simon A. Forbes et al.   NUCLEIC ACIDS RESEARCH

Everolimus synergizes with gefitinib in non-small-cell lung cancer cell lines resistant to epidermal growth factor receptor tyrosine kinase inhibitors

(2012) Song Dong et al.   CANCER CHEMOTHERAPY AND PHARMACOLOGY

Phase II study of gefitinib in combination with paclitaxel (P) and carboplatin (C) as second-line therapy for ovarian, tubal or peritoneal adenocarcinoma (1839IL/0074)

(2010) Patricia Pautier et al.   GYNECOLOGIC ONCOLOGY

Mechanisms of drug combinations: interaction and network perspectives

(2009) Jia Jia et al.   NATURE REVIEWS DRUG DISCOVERY