2020
COSIFER: a Python package for the consensus inference of molecular interaction networks
Manica M, Bunne C, Mathis R, Cadow J, Ahsen M, Stolovitzky G, Martínez M. COSIFER: a Python package for the consensus inference of molecular interaction networks. Bioinformatics 2020, 37: 2070-2072. PMID: 33241320, PMCID: PMC8337002, DOI: 10.1093/bioinformatics/btaa942.Peer-Reviewed Original ResearchConceptsAdvent of high-throughput technologiesNetwork inferenceMolecular interaction networksHigh-throughput dataHigh-throughput technologiesState-of-the-artSupplementary dataExpression dataInteraction networkPython source codeInference servicesState-of-the-art methodologiesWeb servicesSource codeMolecular networksWeb-based platformRegulatory apparatusBioinformaticsPython packageConsensus strategyNetworkRobust networkInference methodsInferenceIndividual methods
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