DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer

The Role of TP53 Mutations in EGFR-Mutated Non-Small-Cell Lung Cancer: Clinical Significance and Implications for Therapy

(2022) Matteo Canale et al.   Cancers

Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions

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NASMDR: a framework for miRNA-drug resistance prediction using efficient neural architecture search and graph isomorphism networks

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Graph representation learning in biomedicine and healthcare

(2022) Michelle M. Li et al.   Nature Biomedical Engineering

Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

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SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures

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TGSA: protein–protein association-based twin graph neural networks for drug response prediction with similarity augmentation

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An overview of machine learning methods for monotherapy drug response prediction

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Graph Convolutional Networks for Drug Response Prediction

(2021) Tuan Nguyen et al.   IEEE-ACM Transactions on Computational Biology and Bioinformatics

Cancer subtype classification and modeling by pathway attention and propagation

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Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells

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Pathway-Guided Deep Neural Network toward Interpretable and Predictive Modeling of Drug Sensitivity

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DeepCDR: a hybrid graph convolutional network for predicting cancer drug response

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MOLI: multi-omics late integration with deep neural networks for drug response prediction

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Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network

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Transcriptome profiling revealed multiple genes and ECM-receptor interaction pathways that may be associated with breast cancer

(2019) Yulong Bao et al.   CELLULAR & MOLECULAR BIOLOGY LETTERS

Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization

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Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches

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Deep learning for drug response prediction in cancer

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A Deep Learning Framework for Predicting Response to Therapy in Cancer

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Personal Omics for Precision Health

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STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

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Regulation of Ubiquitin Enzymes in the TGF-β Pathway

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TGF-β Family Signaling in Tumor Suppression and Cancer Progression

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Context-dependent roles for lymphotoxin-β receptor signaling in cancer development

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A Landscape of Pharmacogenomic Interactions in Cancer

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Critical evaluation of challenges and future use of animals in experimentation for biomedical research

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Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection

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Dasatinib blocks transcriptional and promigratory responses to transforming growth factor-beta in pancreatic adenocarcinoma cells through inhibition of Smad signalling: implications for in vivo mode of action

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Correlating chemical sensitivity and basal gene expression reveals mechanism of action

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Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset

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Bortezomib in multiple myeloma: systematic review and clinical considerations

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An Interactive Resource to Identify Cancer Genetic and Lineage Dependencies Targeted by Small Molecules

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Wnt Signaling Pathway in Non-Small Cell Lung Cancer

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Induction of Melanogenesis by Rapamycin in Human MNT-1 Melanoma Cells

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Rationally Designed Treatment for Solid Tumors with MAPK Pathway Activation: A Phase I Study of Paclitaxel and Bortezomib Using an Adaptive Dose-Finding Approach

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Predicting in vitro drug sensitivity using Random Forests

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PubChem: a public information system for analyzing bioactivities of small molecules

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