2025
Batch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns
Millard N, Chen J, Palshikar M, Pelka K, Spurrell M, Price C, He J, Hacohen N, Raychaudhuri S, Korsunsky I. Batch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns. Genome Biology 2025, 26: 36. PMID: 40001084, PMCID: PMC11863647, DOI: 10.1186/s13059-025-03479-9.Peer-Reviewed Original ResearchConceptsBatch effectsVisualization of gene expression patternsSpatial gene patternsGene expression analysis of cellsGene expression patternsGene expression analysisGene expression levelsGene colocalizationAnalysis of cellsGene patternsTranscriptome analysisLigand-receptor interactionsExpression patternsSpatial transcriptomicsSpatial transcriptomic analysisExpression levelsGenesMultiple samplesSpatial patternsTranscriptomeColocalizationAnatomical contextPatternsCount dataThe development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas
Jester N, Singh M, Lorr S, Tommasini S, Wiznia D, Buono F. The development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas. Scientific Reports 2025, 15: 5918. PMID: 39966622, PMCID: PMC11836447, DOI: 10.1038/s41598-025-88589-x.Peer-Reviewed Original ResearchConceptsGround-truth datasetDice scoreVestibular schwannomaImage processing accuracyVolumetric analysisML-based algorithmsMeasuring tumor sizeMean dice scoreAuto-segmentation toolAccurate AIAI modelsTumor sizeTumor modelVS tumorsTumor growthTesting stageAI-LTumorImage processing softwareClinical practicePatient recruitmentProcessing softwareSchwannomaDatasetManual segmentationA Selective Review of Network Analysis Methods for Gene Expression Data
Li R, Yi H, Ma S. A Selective Review of Network Analysis Methods for Gene Expression Data. Methods In Molecular Biology 2025, 2880: 293-307. PMID: 39900765, DOI: 10.1007/978-1-0716-4276-4_14.Peer-Reviewed Original ResearchConceptsGene Expression DataGene expression networksExpression DataDownstream analysisExpression networksGene expressionBiological processesGenesMolecular mechanismsBiological implicationsHigh-throughput profiling techniquesBiological findingsGlobal viewComplex interactionsProfiling techniquesRegulationA new method for detecting mixed Mycobacterium tuberculosis infection and reconstructing constituent strains provides insights into transmission
Sobkowiak B, Cudahy P, Chitwood M, Clark T, Colijn C, Grandjean L, Walter K, Crudu V, Cohen T. A new method for detecting mixed Mycobacterium tuberculosis infection and reconstructing constituent strains provides insights into transmission. Genome Medicine 2025, 17: 8. PMID: 39871355, PMCID: PMC11771024, DOI: 10.1186/s13073-025-01430-y.Peer-Reviewed Original ResearchConceptsShort-read WGS dataWhole-genome sequencingStrain sequencesWGS dataMultiple strainsStrain proportionsMycobacterium tuberculosis populationMixed infectionGenome sequenceBioinformatics pipelineClustering allele frequenciesDownstream analysisAllele frequenciesEvidence of mixed infectionSequenceTuberculosis populationStrainIsolatesIn vitroTransmission clustersMixed samplesAllelesInfectionMycobacterium tuberculosis infectionPathogensGuidelines to Analyze ChIP-Seq Data: Journey Through QC and Analysis Considerations
De Kumar B, Krishnan J. Guidelines to Analyze ChIP-Seq Data: Journey Through QC and Analysis Considerations. Methods In Molecular Biology 2025, 2889: 193-206. PMID: 39745614, DOI: 10.1007/978-1-0716-4322-8_14.Peer-Reviewed Original ResearchConceptsChIP-seqChIP-seq analysisQC metricsProperties of transcription factorsNext-generation sequencing approachChIP-seq experimentsStudy DNA-protein interactionsGene regulatory propertiesDNA-protein interactionsENCODE consortiumChromatin stateSequencing approachTranscription factorsChromatinGenesNext-generationImmunoprecipitationSequenceA Comprehensive Bioinformatics Approach to Analysis of Variants: Variant Calling, Annotation, and Prioritization
Koroglu M, Bilguvar K. A Comprehensive Bioinformatics Approach to Analysis of Variants: Variant Calling, Annotation, and Prioritization. Methods In Molecular Biology 2025, 2889: 207-233. PMID: 39745615, DOI: 10.1007/978-1-0716-4322-8_15.Peer-Reviewed Original ResearchConceptsGenomic dataHigh-throughput sequencing technologyGenomic data analysisField of genomicsNext-generation sequencingVariant callingNGS technologiesSequencing technologiesBioinformatics approachComprehensive computational approachSequenceComputational approachCancer researchGenomeTranscriptomeBioinformaticsNGSProteomicsNext-generationDNARNAEfficient sequenceAnnotationVariantsFragments
2024
nipalsMCIA: flexible multi-block dimensionality reduction in R via nonlinear iterative partial least squares
Mattessich M, Reyna J, Aron E, Ay F, Kilmer M, Kleinstein S, Konstorum A. nipalsMCIA: flexible multi-block dimensionality reduction in R via nonlinear iterative partial least squares. Bioinformatics 2024, 41: btaf015. PMID: 39799512, PMCID: PMC11783316, DOI: 10.1093/bioinformatics/btaf015.Peer-Reviewed Original ResearchConceptsIterative partial least squaresNonlinear iterative partial least squaresDimensionality reductionMultiple co-inertia analysisJoint dimensionality reductionSignificant speed-upUnsupervised learningSingle-cell datasetsMulti-omics dataCo-inertia analysisFeature dimensionsSpeed-upBioconductor packageSingle-cell analysisPartial least squaresLeast squaresRobust approachImplementationHTMLDatasetBioconductorCosGeneGate selects multi-functional and credible biomarkers for single-cell analysis
Liu T, Long W, Cao Z, Wang Y, He C, Zhang L, Strittmatter S, Zhao H. CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis. Briefings In Bioinformatics 2024, 26: bbae626. PMID: 39592241, PMCID: PMC11596696, DOI: 10.1093/bib/bbae626.Peer-Reviewed Original ResearchREliable PIcking by Consensus (REPIC): a consensus methodology for harnessing multiple cryo-EM particle pickers
Cameron C, Seager S, Sigworth F, Tagare H, Gerstein M. REliable PIcking by Consensus (REPIC): a consensus methodology for harnessing multiple cryo-EM particle pickers. Communications Biology 2024, 7: 1421. PMID: 39482410, PMCID: PMC11528043, DOI: 10.1038/s42003-024-07045-0.Peer-Reviewed Original ResearchConceptsCryo-EM usersParticle identificationParticle pickingLow signal-to-noise ratioAchievable resolutionSignal-to-noise ratioState-of-the-art computational algorithmsInteger linear programmingParticle setManual interventionHigh-quality particlesGraph problemsParticle locationMultiple pickersParticlesSDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data
Liu Y, Li N, Qi J, Xu G, Zhao J, Wang N, Huang X, Jiang W, Wei H, Justet A, Adams T, Homer R, Amei A, Rosas I, Kaminski N, Wang Z, Yan X. SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data. Genome Biology 2024, 25: 271. PMID: 39402626, PMCID: PMC11475911, DOI: 10.1186/s13059-024-03416-2.Peer-Reviewed Original ResearchAscle—A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study
Yang R, Zeng Q, You K, Qiao Y, Huang L, Hsieh C, Rosand B, Goldwasser J, Dave A, Keenan T, Ke Y, Hong C, Liu N, Chew E, Radev D, Lu Z, Xu H, Chen Q, Li I. Ascle—A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study. Journal Of Medical Internet Research 2024, 26: e60601. PMID: 39361955, PMCID: PMC11487205, DOI: 10.2196/60601.Peer-Reviewed Original ResearchConceptsNatural language processingNatural language processing toolkitQuestion-answering taskLanguage modelText generationText processingDomain-specific language modelsNatural language processing functionsMinimal programming expertiseText generation tasksMedical knowledge graphMachine translation tasksROUGE-L scoreDomain-specific challengesAll-in-one solutionROUGE-LText summarizationBLEU scoreKnowledge graphMachine translationUnstructured textQuestion-answeringHugging FaceProcessing toolkitLanguage processingPTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals
Wang S, Di Y, Yang Y, Salovska B, Li W, Hu L, Yin J, Shao W, Zhou D, Cheng J, Liu D, Yang H, Liu Y. PTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals. Cell Reports Methods 2024, 4: 100859. PMID: 39255793, PMCID: PMC11440062, DOI: 10.1016/j.crmeth.2024.100859.Peer-Reviewed Original ResearchConceptsP-siteSurrounding amino acid sequenceKinase-substrate networkQuantitative phosphoproteomic analysisFunctional enrichment analysisPhosphoproteomic resultsKinase motifsComparative phosphoproteomicsPTM sitesPhosphorylation eventsPhosphoproteomic analysisProteomic analysisEnrichment analysisMammalian speciesSpeciesEvolutionary anglePhosphoproteomeMotifEnvironmental factorsNon-human speciesPTMProteomicsKinaseMammalsProteinBioCoder: a benchmark for bioinformatics code generation with large language models
Tang X, Qian B, Gao R, Chen J, Chen X, Gerstein M. BioCoder: a benchmark for bioinformatics code generation with large language models. Bioinformatics 2024, 40: i266-i276. PMID: 38940140, PMCID: PMC11211839, DOI: 10.1093/bioinformatics/btae230.Peer-Reviewed Original ResearchConceptsCode generationLanguage modelAmount of domain knowledgeDomain-specific knowledgeJava methodsDomain knowledgeClass declarationsPerformance gainsData operationsPython functionsTraining datasetSuccess modelIntricate taskTest benchmarksDocker imageBenchmarksCodeSmall modelsDatasetGlobal variablesBioCodeFunctional dependenceEvaluate various modelsIncreasing needCodeGenOpenSAFELY: A platform for analysing electronic health records designed for reproducible research
Nab L, Schaffer A, Hulme W, DeVito N, Dillingham I, Wiedemann M, Andrews C, Curtis H, Fisher L, Green A, Massey J, Walters C, Higgins R, Cunningham C, Morley J, Mehrkar A, Hart L, Davy S, Evans D, Hickman G, Inglesby P, Morton C, Smith R, Ward T, O'Dwyer T, Maude S, Bridges L, Butler‐Cole B, Stables C, Stokes P, Bates C, Cockburn J, Hester F, Parry J, Bhaskaran K, Schultze A, Rentsch C, Mathur R, Tomlinson L, Williamson E, Smeeth L, Walker A, Bacon S, MacKenna B, Goldacre B. OpenSAFELY: A platform for analysing electronic health records designed for reproducible research. Pharmacoepidemiology And Drug Safety 2024, 33: e5815-e5815. PMID: 38783412, PMCID: PMC7616137, DOI: 10.1002/pds.5815.Peer-Reviewed Original ResearchMeSH KeywordsCOVID-19Electronic Health RecordsHumansReproducibility of ResultsResearch DesignSoftwareConceptsElectronic health recordsHealth recordsComputing environmentProgram codeSoftware platformAdministrative health dataAnalysis environmentAudit trailReproducibility of researchReproducible researchData preparationPublic sharingPublic health guidanceHealth dataHealth guidanceCodeOpenSAFELYTechnical solutionsPlatformPromote trustCOVID-19 pandemicIncrease transparencyCode-sharingWorkflowDataGlycoproteomics: Charting new territory in mass spectrometry and glycobiology
Malaker S. Glycoproteomics: Charting new territory in mass spectrometry and glycobiology. Journal Of Mass Spectrometry 2024, 59: e5034. PMID: 38726698, DOI: 10.1002/jms.5034.Peer-Reviewed Original ResearchPredictors of Disease Progression and Adverse Clinical Outcomes in Patients With Moderate Aortic Stenosis Using an Artificial Intelligence-Based Software Platform
Salem M, Gada H, Ramlawi B, Sotelo M, Nona P, Wagner L, Rogers C, Brigman L, Vora A. Predictors of Disease Progression and Adverse Clinical Outcomes in Patients With Moderate Aortic Stenosis Using an Artificial Intelligence-Based Software Platform. The American Journal Of Cardiology 2024, 223: 92-99. PMID: 38710350, DOI: 10.1016/j.amjcard.2024.04.051.Peer-Reviewed Original ResearchConceptsAdverse clinical outcomesRisk of adverse clinical outcomesModerate aortic stenosisClinical outcomesAortic stenosisDisease progressionSevere ASModerate ASAtrial fibrillationAmerican Heart Association/American College of Cardiology guidelinesLower left ventricular ejection fractionAssociated with adverse clinical outcomesPredictor of clinical outcomePredictors of disease progressionAmerican Heart Association/American CollegeFactors associated with outcomesVentricular ejection fractionEnd-stage renal diseaseHeart failure hospitalizationCox proportional hazards modelsProportional hazards modelSerial echoesAS severityEjection fractionCardiology guidelinesEstablishing robust governance of clinical artificial intelligence software – Why radiologists should lead
Cavallo J, Davis M. Establishing robust governance of clinical artificial intelligence software – Why radiologists should lead. Clinical Imaging 2024, 110: 110163. PMID: 38678765, DOI: 10.1016/j.clinimag.2024.110163.Peer-Reviewed Original ResearchIncorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction
Zhuang Y, Kim N, Fritsche L, Mukherjee B, Lee S. Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction. BMC Bioinformatics 2024, 25: 65. PMID: 38336614, PMCID: PMC11323637, DOI: 10.1186/s12859-024-05664-2.Peer-Reviewed Original ResearchConceptsPredictive performance of polygenic risk scoresFunctional annotationGenetic architecturePerformance of polygenic risk scoresPRS-CSAnnotation informationPolygenic risk predictionGenetic risk predictionPolygenic risk scoresFunctional annotation informationKyoto Encyclopedia of GenesRisk predictionProportion of variantsEncyclopedia of GenesGenomes (KEGGSource of annotationTrait heritabilityAnnotation groupsPathway informationQuantitative traitsKyoto EncyclopediaFunctional categoriesBackgroundGenetic variantsHeritable contributionReal world data sourcesChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation
Kyro G, Morgunov A, Brent R, Batista V. ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation. Journal Of Chemical Information And Modeling 2024, 64: 653-665. PMID: 38287889, DOI: 10.1021/acs.jcim.3c01456.Peer-Reviewed Original ResearchConceptsVastness of chemical spaceMolecular generationDomain of drug discoveryArtificial intelligence modelsChemical spaceIntelligence modelsLearning methodologyPython packageDrug discoverySmall molecule inhibitorsActive learning methodologiesFDA-approved small molecule inhibitorsMoleculesEfficient methodDomainSoftwareC-Abl kinaseScalable, accessible and reproducible reference genome assembly and evaluation in Galaxy
Larivière D, Abueg L, Brajuka N, Gallardo-Alba C, Grüning B, Ko B, Ostrovsky A, Palmada-Flores M, Pickett B, Rabbani K, Antunes A, Balacco J, Chaisson M, Cheng H, Collins J, Couture M, Denisova A, Fedrigo O, Gallo G, Giani A, Gooder G, Horan K, Jain N, Johnson C, Kim H, Lee C, Marques-Bonet T, O’Toole B, Rhie A, Secomandi S, Sozzoni M, Tilley T, Uliano-Silva M, van den Beek M, Williams R, Waterhouse R, Phillippy A, Jarvis E, Schatz M, Nekrutenko A, Formenti G. Scalable, accessible and reproducible reference genome assembly and evaluation in Galaxy. Nature Biotechnology 2024, 42: 367-370. PMID: 38278971, PMCID: PMC11462542, DOI: 10.1038/s41587-023-02100-3.Peer-Reviewed Original ResearchComputational BiologySoftware
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