Michael Krauthammer, MD, PhD
Lecturer in Biomedical Informatics and Data ScienceDownloadHi-Res Photo
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Contact Info
About
Titles
Lecturer in Biomedical Informatics and Data Science
Appointments
Departments & Organizations
- Center for Biomedical Data Science
- Center for Medical Informatics
- Yale Cancer Center
- Yale Ventures
Education & Training
- PhD
- Columbia University (2003)
- MD
- Universitat Zurich (1995)
Research
Overview
Medical Subject Headings (MeSH)
Computational Biology; Medical Informatics; Models, Statistical; Pathology; Skin Neoplasms
ORCID
0000-0002-4808-1845- View Lab Website
Krauthammer Lab
Research at a Glance
Yale Co-Authors
Frequent collaborators of Michael Krauthammer's published research.
Publications Timeline
A big-picture view of Michael Krauthammer's research output by year.
Research Interests
Research topics Michael Krauthammer is interested in exploring.
Ruth Halaban, PhD
Harriet Kluger, MD
Mario Sznol, MD
David F. Stern, PhD
Joseph Schlessinger, PhD
Kei-Hoi Cheung, PhD
16Publications
1,508Citations
Skin Neoplasms
Publications
2024
Explainable deep learning for disease activity prediction in chronic inflammatory joint diseases
Trottet C, Allam A, Horvath A, Finckh A, Hügle T, Adler S, Kyburz D, Micheroli R, Krauthammer M, Ospelt C. Explainable deep learning for disease activity prediction in chronic inflammatory joint diseases. PLOS Digital Health 2024, 3: e0000422. PMID: 38935600, PMCID: PMC11210792, DOI: 10.1371/journal.pdig.0000422.Peer-Reviewed Original ResearchAltmetricConceptsNeural networkMulti-task learning modelShort-term memory networkMedical expert knowledgeFeature representationAttention layerLatent representationLatent embeddingsDeep learningK-nearestMemory networkTree-basedLearning modelsExpert knowledgeBaseline strategyActivity predictionScoring networkRegression algorithmAttribution methodsNetworkChronic inflammatory joint diseaseStatic baselinesRepresentationModular modelPatient representationCOL10A1 expression distinguishes a subset of cancer-associated fibroblasts present in the stroma of high-risk basal cell carcinoma
Esposito M, Yerly L, Shukla P, Hermes V, Sella F, Balazs Z, Lattmann E, Tastanova A, Turko P, Lang R, Kolm I, Staeger R, Kuonen F, Krauthammer M, Hafner J, Levesque M, Restivo G. COL10A1 expression distinguishes a subset of cancer-associated fibroblasts present in the stroma of high-risk basal cell carcinoma. British Journal Of Dermatology 2024, ljae258. PMID: 38916477, DOI: 10.1093/bjd/ljae258.Peer-Reviewed Original ResearchAltmetricConceptsBasal cell carcinoma subtypesBasal cell carcinomaCancer-associated fibroblastsHigh-risk basal cell carcinomasInvasive BCCCancer-associated fibroblast populationsLaser capture microdissectionCell carcinomaHigh-risk BCC subtypesSubtypes of basal cell carcinomaHigh risk of recurrenceBasal cell carcinoma developmentBasal cell carcinoma progressionHigh-risk subtypesBasal cell carcinoma samplesGene expression signaturesTailored treatment optionsBasosquamous subtypesHealthy skin samplesRNA sequencingStromal featuresTumor microenvironmentMorphological subtypesTreatment optionsPrognostic biomarkerMachine learning prediction of prime editing efficiency across diverse chromatin contexts
Mathis N, Allam A, Tálas A, Kissling L, Benvenuto E, Schmidheini L, Schep R, Damodharan T, Balázs Z, Janjuha S, Ioannidi E, Böck D, van Steensel B, Krauthammer M, Schwank G. Machine learning prediction of prime editing efficiency across diverse chromatin contexts. Nature Biotechnology 2024, 1-8. PMID: 38907037, DOI: 10.1038/s41587-024-02268-2.Peer-Reviewed Original ResearchCitationsAltmetricInnate acting memory Th1 cells modulate heterologous diseases
Rakebrandt N, Yassini N, Kolz A, Schorer M, Lambert K, Goljat E, Brull A, Rauld C, Balazs Z, Krauthammer M, Carballido J, Peters A, Joller N. Innate acting memory Th1 cells modulate heterologous diseases. Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2312837121. PMID: 38838013, PMCID: PMC11181110, DOI: 10.1073/pnas.2312837121.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and ConceptsConceptsIFN-gAutoimmune model of multiple sclerosisIFN-g productionInnate-like responsesMemory Th1 cellsModel of multiple sclerosisResponse to IL-12T helper 1Heterologous challengeTh1 cellsAutoimmune modelsIL-33IL-12Immune memoryDisease onsetIL-18Viral infectionUnrelated diseaseMultiple sclerosisDiseaseEnhanced responseInfectionHeterologous diseasesCellsRechallengeLongitudinal cell-free DNA characterization by low-coverage whole-genome sequencing in patients undergoing high-dose radiotherapy
Balázs Z, Balermpas P, Ivanković I, Willmann J, Gitchev T, Bryant A, Guckenberger M, Krauthammer M, Andratschke N. Longitudinal cell-free DNA characterization by low-coverage whole-genome sequencing in patients undergoing high-dose radiotherapy. Radiotherapy And Oncology 2024, 197: 110364. PMID: 38834154, DOI: 10.1016/j.radonc.2024.110364.Peer-Reviewed Original ResearchAltmetricConceptsCopy number alterationsCell-free DNACancer patientsTumor fractionHead and neck cancer patientsPlasma cell-free DNAAssociated with tumor aggressivenessSCCHN patient samplesSystemic tumor spreadTumor-agnostic approachHigh-dose radiotherapyPlasma cfDNA samplesWhole-genome sequencingHead and neckCell-free DNA sequencing dataDetect viral DNAViral DNAOligometastatic patientsSCCHN patientsRadiotherapy guidelinesTumor spreadTumor aggressivenessImaging findingsCfDNA samplesTreatment strategiesSimple Contrastive Representation Learning for Time Series Forecasting
Zheng X, Chen X, Schürch M, Mollaysa A, Allam A, Krauthammer M. Simple Contrastive Representation Learning for Time Series Forecasting. 2024, 00: 6005-6009. DOI: 10.1109/icassp48485.2024.10446875.Peer-Reviewed Original ResearchCitationsFormer smoking, but not active smoking, is associated with delirium in postoperative ICU patients: a matched case-control study
Komninou M, Egli S, Rossi A, Ernst J, Krauthammer M, Schuepbach R, Delgado M, Bartussek J. Former smoking, but not active smoking, is associated with delirium in postoperative ICU patients: a matched case-control study. Frontiers In Psychiatry 2024, 15: 1347071. PMID: 38559401, PMCID: PMC10979642, DOI: 10.3389/fpsyt.2024.1347071.Peer-Reviewed Original ResearchAltmetricConceptsIntensive care unitCase-control studyFormer smokingNon-delirious patientsAssociated with increased odds of deliriumActive smokersActive smokingNon-smokersSimplified Acute Physiology Score IIAcute Physiology Score IIRisk of postoperative deliriumSurgical intensive care unitIntensive care unit patientsIndependent risk factorGroup of patientsCritically ill patientsAssociated with deliriumAssociated with increased oddsOdds of deliriumIncidence of deliriumLogistic regression analysisOccurrence of deliriumPropensity score analysisMidazolam usageMorphine useFragmentstein—facilitating data reuse for cell-free DNA fragment analysis
Balázs Z, Gitchev T, Ivanković I, Krauthammer M. Fragmentstein—facilitating data reuse for cell-free DNA fragment analysis. Bioinformatics 2024, 40: btae017. PMID: 38224549, PMCID: PMC10805340, DOI: 10.1093/bioinformatics/btae017.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and ConceptsConceptsCopy number variantsNucleotide-level informationCell-free DNA sequencing dataDNA fragmentation analysisCell-free DNASensitive genomic dataFragment length analysisNucleosome occupancyBAM filesCommand-line toolSequence dataGenomic dataAnalysis of cell-free DNASequence informationGNU GPLv3Bioinformatics softwareFragment analysisFunctional analysisAlignment mapData sharingLength analysisFragmentsSimplified formatLimited data sharingGenome
2023
Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey
Shaitarova A, Zaghir J, Lavelli A, Krauthammer M, Rinaldi F. Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey. Yearbook Of Medical Informatics 2023, 32: 230-243. PMID: 38147865, PMCID: PMC10751112, DOI: 10.1055/s-0043-1768726.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsLanguage modelNLP tasksNatural language processing researchTransformer-based language modelsLanguage processing researchMedical natural language processingNatural language processingCommon NLP tasksEuropean languagesLow-resource onesNLP downstream tasksMultiple languagesMedical NLPNLP researchMedical textsBiomedical NLPLanguage processingLanguageInformation lacunaClinical NLPInformation extractionMedical domainEntity recognitionNegation detectionDownstream tasksBoosting Radiology Report Generation by Infusing Comparison Prior
Kim S, Nooralahzadeh F, Rohanian M, Fujimoto K, Nishio M, Sakamoto R, Rinaldi F, Krauthammer M. Boosting Radiology Report Generation by Infusing Comparison Prior. 2023, 50-61. DOI: 10.18653/v1/2023.bionlp-1.4.Peer-Reviewed Original Research
Links & Media
News
- October 12, 2017
Michael Krauthammer recognized for work in medical informatics
- December 21, 2016
Researchers Identify Heterogeneity of Tissue Resident Memory T Cells as Targets of Checkpoint Therapies
- July 27, 2015
Yale study identifies ‘major player’ in skin cancer genes
- December 31, 2014
Bioinformatics Boosts Cancer Research