2024
PubMed Computed Authors in 2024: an open resource of disambiguated author names in biomedical literature
Tian S, Chen Q, Comeau D, Wilbur W, Lu Z. PubMed Computed Authors in 2024: an open resource of disambiguated author names in biomedical literature. Bioinformatics 2024, 40: btae672. PMID: 39520405, PMCID: PMC11588201, DOI: 10.1093/bioinformatics/btae672.Peer-Reviewed Original ResearchAuthor name disambiguationAuthor namesBiomedical literature searchWeb APIsEnhancement algorithmAuthority datasetsQueryBiomedical literatureDatasetAuthors' algorithmImproved accuracyIndividual researchersAlgorithmPubMed articlesSupplementary dataDisambiguationORCIDLiterature retrievalDownloadRetrievalWebAPIComprehensive datasetBioinformaticsRepresenting core gene expression activity relationships using the latent structure implicit in Bayesian networks
Gao J, Gerstein M. Representing core gene expression activity relationships using the latent structure implicit in Bayesian networks. Bioinformatics 2024, 40: btae463. PMID: 39051682, PMCID: PMC11316617, DOI: 10.1093/bioinformatics/btae463.Peer-Reviewed Original ResearchTranscriptional regulatory networksGene regulatory networksCo-expression networkGene expression activityChIP-seqGene conservationCluster genesSupplementary dataRegulatory networksBiological networksClearer clusteringCo-expressionExpression activityBioinformaticsGenesBiomedical studiesConservationExpressionClustersGenome-wide analysis and visualization of copy number with CNVpytor in igv.js
Panda A, Suvakov M, Thorvaldsdottir H, Mesirov J, Robinson J, Abyzov A. Genome-wide analysis and visualization of copy number with CNVpytor in igv.js. Bioinformatics 2024, 40: btae453. PMID: 39018173, PMCID: PMC11303504, DOI: 10.1093/bioinformatics/btae453.Peer-Reviewed Original ResearchA supervised Bayesian factor model for the identification of multi-omics signatures
Gygi J, Konstorum A, Pawar S, Aron E, Kleinstein S, Guan L. A supervised Bayesian factor model for the identification of multi-omics signatures. Bioinformatics 2024, 40: btae202. PMID: 38603606, PMCID: PMC11078774, DOI: 10.1093/bioinformatics/btae202.Peer-Reviewed Original ResearchConceptsMulti-omics signaturesBayesian factor modelMulti-omics dataMulti-omics integrationSupplementary dataOmics datasetsMulti-OmicsProfiling datasetsR packageDiverse assaysImproved biological understandingProfiling assaysSignature discoveryBioinformaticsProfiling studiesBiological understandingDimensionality reductionBiological responsesBiological signaturesCombination of dimensionality reduction
2023
Towards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning approach
Hu Y, Keloth V, Raja K, Chen Y, Xu H. Towards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning approach. Bioinformatics 2023, 39: btad542. PMID: 37669123, PMCID: PMC10500081, DOI: 10.1093/bioinformatics/btad542.Peer-Reviewed Original ResearchNatural language processingMicro-F1 scoreCOVID-19 datasetNLP pipelineF1 scoreEntity recognition modelAD datasetPICO elementsSentence classificationNER modelRecognition modelLanguage processingLearning approachLearning modelEnd evaluationSupplementary dataDatasetPipelineExtractionInformationRCT abstractsAnnotationSentencesBioinformaticsComplexityApplications of transformer-based language models in bioinformatics: a survey
Zhang S, Fan R, Liu Y, Chen S, Liu Q, Zeng W. Applications of transformer-based language models in bioinformatics: a survey. Bioinformatics Advances 2023, 3: vbad001. PMID: 36845200, PMCID: PMC9950855, DOI: 10.1093/bioadv/vbad001.Peer-Reviewed Original ResearchTransformer-based language modelsBioinformatics researchLanguage modelNatural language processingBioinformatics Advances</i>Field of natural language processingSupplementary dataBiological sequencesBioinformaticsBioinformatics applicationsNLP researchLanguage processingNatural languageGPT-3SequenceDrug discoveryStructure of transformerBioinformaticiansMonoNet: enhancing interpretability in neural networks via monotonic features
Nguyen A, Moreno D, Le-Bel N, Martínez M. MonoNet: enhancing interpretability in neural networks via monotonic features. Bioinformatics Advances 2023, 3: vbad016. PMID: 37143924, PMCID: PMC10152389, DOI: 10.1093/bioadv/vbad016.Peer-Reviewed Original ResearchNeural networkMonotonicity constraintsHigh-stakes scenariosInformation-theoretic analysisMachine learning modelsMedical informaticsNeural modelLearning capabilityLearning modelsBioinformatics Advances</i>Monotonous featuresComputational biologyEnhance interpretationModeling capabilitiesDatasetInterpretable modelsLearning processSample dataNetworkPower modelLearningSupplementary dataConstraintsPerformanceInformatics
2022
Insights from incorporating quantum computing into drug design workflows
Lau B, Emani P, Chapman J, Yao L, Lam T, Merrill P, Warrell J, Gerstein M, Lam H. Insights from incorporating quantum computing into drug design workflows. Bioinformatics 2022, 39: btac789. PMID: 36477833, PMCID: PMC9825754, DOI: 10.1093/bioinformatics/btac789.Peer-Reviewed Original ResearchConceptsQuantum machine learningComputer-aided drug designMachine-learning moduleQuantum computing methodsCommercial quantum computersMachine learningJupyter notebooksNeural networkComputing methodClassical baselinesDesign workflowQML modelsQuantum hardwarePython codeAcademic useSupplementary dataQuantum computerWorkflowJudicious partitioningModuleHardwareGitHubClassical counterpartCase studyComputerComprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES
Raredon M, Yang J, Kothapalli N, Lewis W, Kaminski N, Niklason L, Kluger Y. Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES. Bioinformatics 2022, 39: btac775. PMID: 36458905, PMCID: PMC9825783, DOI: 10.1093/bioinformatics/btac775.Peer-Reviewed Original ResearchConceptsCell-cell interactionsCell-cell signalingSingle-cell resolutionSingle-cell dataLocal cellular microenvironmentSingle-cell levelSpatial transcriptomics dataCell clustersExtracellular signalingTranscriptomic dataGene expression valuesSpatial transcriptomicsSignaling mechanismCellular microenvironmentNicheExpression valuesSupplementary dataSignalingTranscriptomicsComprehensive visualizationBioinformaticsInteractionIncorporating family disease history and controlling case–control imbalance for population-based genetic association studies
Zhuang Y, Wolford B, Nam K, Bi W, Zhou W, Willer C, Mukherjee B, Lee S. Incorporating family disease history and controlling case–control imbalance for population-based genetic association studies. Bioinformatics 2022, 38: 4337-4343. PMID: 35876838, PMCID: PMC9477535, DOI: 10.1093/bioinformatics/btac459.Peer-Reviewed Original ResearchConceptsEmpirical saddlepoint approximationFamily disease historyCase-control imbalanceSaddlepoint approximationGenome-wide association analysisPopulation-based genetic association studiesGenetic association testsVariant-phenotype associationsDisease historyGenetic association studiesLow detection powerType I error inflationCorrelation of phenotypesWhite British sampleSupplementary dataAssociation studiesPopulation-based biobanksIncreased phenotypic correlationsKorean GenomeSimulation studyPhenotype distributionPhenotypeAssociation TestBioinformaticsPhenotypic correlationsDECODE: a computational pipeline to discover T cell receptor binding rules
Papadopoulou I, Nguyen A, Weber A, Martínez M. DECODE: a computational pipeline to discover T cell receptor binding rules. Bioinformatics 2022, 38: i246-i254. PMID: 35758821, PMCID: PMC9235487, DOI: 10.1093/bioinformatics/btac257.Peer-Reviewed Original ResearchConceptsT cell receptor bindingT cell receptorComputational pipelineTCR-epitope bindingBlack-box natureSequence motifsSequencing technologiesSupplementary dataBlack-box modelsBiochemical rulesMachine learningVisualization toolsComputational rulesDecodingData abundanceSequenceBioinformaticsEasy-to-useAdaptive immune responsesBindingBinding propertiesT cell-based therapiesT-cell receptor sequencingTCR bindingTCR specificityMitoVisualize: a resource for analysis of variants in human mitochondrial RNAs and DNA
Lake NJ, Zhou L, Xu J, Lek M. MitoVisualize: a resource for analysis of variants in human mitochondrial RNAs and DNA. Bioinformatics 2022, 38: 2967-2969. PMID: 35561159, DOI: 10.1093/bioinformatics/btac216.Peer-Reviewed Original ResearchConceptsRibosomal RNA secondary structuresHuman mitochondrial RNAMitochondrial transfer RNAsPost-transcriptional modificationsHuman mitochondrial DNADisease-Associated VariantsRNA secondary structureEffects of variantsMtDNA mapMitochondrial RNAMtDNA variationMitochondrial DNATransfer RNAAnalysis of variantsRNA structureSecondary structureVariant annotationLarge deletionsSupplementary dataVariant interpretationRNADNAVariantsGenesNew toolscGraph: a graph neural network-based approach to automatically identify cell types
Yin Q, Liu Q, Fu Z, Zeng W, Zhang B, Zhang X, Jiang R, Lv H. scGraph: a graph neural network-based approach to automatically identify cell types. Bioinformatics 2022, 38: 2996-3003. PMID: 35394015, DOI: 10.1093/bioinformatics/btac199.Peer-Reviewed Original ResearchConceptsGene interaction relationshipsCell-type identificationAnalysis of regulatory mechanismsGene-gene interactionsSingle-cell RNA sequencingCell typesPathway enrichment analysisSingle-cell technologiesIdentification of cell typesSingle cell technologiesSupplementary dataScRNA-seqTranscriptional statesRNA sequencingEnrichment analysisGene expressionRegulatory mechanismsGenesCell differentiationBiological researchBiological dataNetwork-based approachBioinformaticsCellsNeural network-based approachMedTator: a serverless annotation tool for corpus development
He H, Fu S, Wang L, Liu S, Wen A, Liu H. MedTator: a serverless annotation tool for corpus development. Bioinformatics 2022, 38: 1776-1778. PMID: 34983060, PMCID: PMC10060696, DOI: 10.1093/bioinformatics/btab880.Peer-Reviewed Original ResearchConceptsAnnotation toolInteractive user interfaceApache 2.0 licenseDocument annotationUser interfaceAnnotation taskSource codeAdvanced featuresDifficulty of useAnnotation corpusCorpus developmentCorpus annotationCore stepsSupplementary dataVariety of needsAnnotationClinical research applicationsSummarizationResearch applicationsToolConsiderable timeTaskBioinformaticsTutorialCode
2021
Real-time mapping of nanopore raw signals
Zhang H, Li H, Jain C, Cheng H, Au K, Li H, Aluru S. Real-time mapping of nanopore raw signals. Bioinformatics 2021, 37: i477-i483. PMID: 34252938, PMCID: PMC8336444, DOI: 10.1093/bioinformatics/btab264.Peer-Reviewed Original ResearchConceptsRaw signalsBase-calling procedureLibrary preparation protocolState-of-the-artK-d treeSeed selection strategySupplementary dataGreen algaeTarget sequenceStreaming methodSequencing devicesGenomeSignal spaceAdapter sequencesSelection strategyChain algorithmSequenceBioinformaticsReal timeMapping accuracySignal characteristicsMapping methodRead signalSeedYeastpowerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis
Dong X, Li X, Chang T, Scherzer C, Weiss S, Qiu W. powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis. Bioinformatics 2021, 37: 4269-4271. PMID: 34009297, PMCID: PMC9492284, DOI: 10.1093/bioinformatics/btab385.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociGenome-wide association studiesEQTL analysisDetectable minor allele frequencyExpression quantitative trait loci analysisAllele frequenciesGene expressionPost-GWAS eraAssociated with gene expressionQuantitative trait lociR packageMinor allele frequencySingle-cell dataEstimate statistical powerSupplementary dataGenetic lociTrait lociAssociation studiesR Shiny applicationGenetic variationSample sizeGenetic variantsLociBioinformaticsShiny application
2020
DeepCDR: a hybrid graph convolutional network for predicting cancer drug response
Liu Q, Hu Z, Jiang R, Zhou M. DeepCDR: a hybrid graph convolutional network for predicting cancer drug response. Bioinformatics 2020, 36: i911-i918. PMID: 33381841, DOI: 10.1093/bioinformatics/btaa822.Peer-Reviewed Original ResearchConceptsCancer drug responsePredicting cancer drug responseDrug responseAnti-cancer drug designCancer-associated genesMulti-omics profilingSupplementary dataHybrid graph convolutional networkDrug designTranscriptome profilingOmics profilesCancer cellsCancer biologyChemical structures of drugsBioinformaticsProfile of individual patientsGraph convolutional networkCancer typesBonding of drugHeterogeneity of cancer patientsConvolutional networkIntrinsic chemical structuresStructure of drugsFeatures of drugsGenesIssues of Z-factor and an approach to avoid them for quality control in high-throughput screening studies
Zhang X, Wang D, Sun S, Zhang H. Issues of Z-factor and an approach to avoid them for quality control in high-throughput screening studies. Bioinformatics 2020, 36: 5299-5303. PMID: 33346821, DOI: 10.1093/bioinformatics/btaa1049.Peer-Reviewed Original ResearchCOSIFER: 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 methodsMicrobiomeExplorer: an R package for the analysis and visualization of microbial communities
Reeder J, Huang M, Kaminker J, Paulson J. MicrobiomeExplorer: an R package for the analysis and visualization of microbial communities. Bioinformatics 2020, 37: 1317-1318. PMID: 32960962, PMCID: PMC8193707, DOI: 10.1093/bioinformatics/btaa838.Peer-Reviewed Original Research
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