Featured Publications
SDePER: 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 Research
2024
Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies
Ji H, Xi N, Mohan C, Yan X, Jain K, Zang Q, Gahtan V, Zhao R. Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies. Frontiers In Immunology 2024, 14: 1342429. PMID: 38250062, PMCID: PMC10797773, DOI: 10.3389/fimmu.2023.1342429.Peer-Reviewed Original ResearchConceptsMolecular endotypesChronic granulomatous disorderSarcoidosis biomarkersTNF blockadeDifferentiate sarcoidosisGranulomatous disorderSarcoidosisAdvanced omics approachesHealthy controlsDisease heterogeneityEndotypesDocumented biomarkerBiomarker profilesPersonalized medicineBiomarkersOmics approachesOmics studiesDiseaseDiagnosisTargeted studiesOmicsBlockadePrognosisTherapyEtiology