2020
PaccMann: a web service for interpretable anticancer compound sensitivity prediction
Cadow J, Born J, Manica M, Oskooei A, Martínez M. PaccMann: a web service for interpretable anticancer compound sensitivity prediction. Nucleic Acids Research 2020, 48: w502-w508. PMID: 32402082, PMCID: PMC7319576, DOI: 10.1093/nar/gkaa327.Peer-Reviewed Original ResearchMeSH KeywordsAntineoplastic AgentsComputer SimulationDrug RepositioningGene Expression ProfilingInternetNeural Networks, ComputerSirolimusSoftwareConceptsWeb servicesAttention-based neural networkState-of-the-art methodsState-of-the-artOutputs confidence scoresCompound structure informationInteractive editorModel decision makingChemical sub-structuresAttention heatmapsNeural networkWeb-based platformModel interpretationEfficient validationConfidence scoresDrug repositioningCompound structureDrug compoundsWebSource of informationStructural informationCompoundsBiomolecular samplesDecision makingPotential compounds
2019
Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer
Oskooei A, Manica M, Mathis R, Martínez M. Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer. Scientific Reports 2019, 9: 15918. PMID: 31685861, PMCID: PMC6828742, DOI: 10.1038/s41598-019-52093-w.Peer-Reviewed Original ResearchConceptsMembrane receptor pathwayDrug sensitivity predictionProtein-protein interaction networkDrug sensitivityGenomics of Drug SensitivityDrug targetsGene expression dataIGFR signaling pathwaysAssignment of high weightsBiomarker identificationExpression dataInteraction networkSensitivity predictionSignaling pathwaySignaling pathway inhibitorsReceptor pathwayTree ensemblesPathway inhibitorPathwayGenomeGenesGDSCNeighborhoods of influenceIdentificationSynthetic datasetsToward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders
Manica M, Oskooei A, Born J, Subramanian V, Sáez-Rodríguez J, Martínez M. Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders. Molecular Pharmaceutics 2019, 16: 4797-4806. PMID: 31618586, DOI: 10.1021/acs.molpharmaceut.9b00520.Peer-Reviewed Original ResearchConceptsConvolutional encoderReceptor tyrosine kinasesProtein-protein interaction networkAttention-based encoderStructural similarity indexSelection of encodingDrug designDrug sensitivity predictionGene expression profilesIn silico predictionSensitivity predictionAttention weightsLeukemia cell linesSMILES sequencesInformative genesGene expression profiles of tumorsApoptotic processInteraction networkExpression profiles of tumorsBaseline modelIntracellular interactionsEncodingTyrosine kinaseDevelopment of personalized therapiesGenes
2015
Elucidating Compound Mechanism of Action by Network Perturbation Analysis
Woo J, Shimoni Y, Yang W, Subramaniam P, Iyer A, Nicoletti P, Martínez M, López G, Mattioli M, Realubit R, Karan C, Stockwell B, Bansal M, Califano A. Elucidating Compound Mechanism of Action by Network Perturbation Analysis. Cell 2015, 162: 441-451. PMID: 26186195, PMCID: PMC4506491, DOI: 10.1016/j.cell.2015.05.056.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAntineoplastic AgentsEpistasis, GeneticGenome-Wide Association StudyMolecular Targeted TherapyNeoplasmsSmall Molecule LibrariesConceptsGenome-wide identificationRegulatory network analysisBind target proteinsCompound's mechanism of actionNovel proteinsTarget proteinsCompound perturbationGlobal dysregulationSmall molecule compoundsCompound similarityProteinNetwork-based approachRepair activityMolecular interactionsTested compoundsCompoundsMechanism of actionNetwork analysisCompound analysisActivity modulationAnticancer drugsCompound efficacyPerturbation profilesSimilarityEffector