2023
A guide to the BRAIN Initiative Cell Census Network data ecosystem
Hawrylycz M, Martone M, Ascoli G, Bjaalie J, Dong H, Ghosh S, Gillis J, Hertzano R, Haynor D, Hof P, Kim Y, Lein E, Liu Y, Miller J, Mitra P, Mukamel E, Ng L, Osumi-Sutherland D, Peng H, Ray P, Sanchez R, Regev A, Ropelewski A, Scheuermann R, Tan S, Thompson C, Tickle T, Tilgner H, Varghese M, Wester B, White O, Zeng H, Aevermann B, Allemang D, Ament S, Athey T, Baker C, Baker K, Baker P, Bandrowski A, Banerjee S, Bishwakarma P, Carr A, Chen M, Choudhury R, Cool J, Creasy H, D’Orazi F, Degatano K, Dichter B, Ding S, Dolbeare T, Ecker J, Fang R, Fillion-Robin J, Fliss T, Gee J, Gillespie T, Gouwens N, Zhang G, Halchenko Y, Harris N, Herb B, Hintiryan H, Hood G, Horvath S, Huo B, Jarecka D, Jiang S, Khajouei F, Kiernan E, Kir H, Kruse L, Lee C, Lelieveldt B, Li Y, Liu H, Liu L, Markuhar A, Mathews J, Mathews K, Mezias C, Miller M, Mollenkopf T, Mufti S, Mungall C, Orvis J, Puchades M, Qu L, Receveur J, Ren B, Sjoquist N, Staats B, Tward D, van Velthoven C, Wang Q, Xie F, Xu H, Yao Z, Yun Z, Zhang Y, Zheng W, Zingg B. A guide to the BRAIN Initiative Cell Census Network data ecosystem. PLOS Biology 2023, 21: e3002133. PMID: 37390046, PMCID: PMC10313015, DOI: 10.1371/journal.pbio.3002133.Peer-Reviewed Original Research
2021
Presence of complete murine viral genome sequences in patient-derived xenografts
Yuan Z, Fan X, Zhu J, Fu T, Wu J, Xu H, Zhang N, An Z, Zheng W. Presence of complete murine viral genome sequences in patient-derived xenografts. Nature Communications 2021, 12: 2031. PMID: 33795676, PMCID: PMC8017013, DOI: 10.1038/s41467-021-22200-5.Peer-Reviewed Original ResearchConceptsPatient-derived xenograftsViral infectionMurine viral infectionHigh virus loadDrug developmentDrug metabolism-related genesVirus loadXenograft experimentsMetabolism-related genesXenograftsUnbiased data-driven approachTumor cellsInfectionExpression levelsEntire viral genomeViral genome sequencesViral sequencesViral genomeCancerImmune
2015
Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action
Sun J, Zhao M, Jia P, Wang L, Wu Y, Iverson C, Zhou Y, Bowton E, Roden D, Denny J, Aldrich M, Xu H, Zhao Z. Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action. PLOS Computational Biology 2015, 11: e1004202. PMID: 26083494, PMCID: PMC4470683, DOI: 10.1371/journal.pcbi.1004202.Peer-Reviewed Original ResearchConceptsGWAS datasetsPathway networkDisease genesGenome-wide association study datasetDrug targetsSignal transduction networksSignal transduction cascadeMultiple signaling pathwaysDrug-induced gene expressionNovel drug targetsTransduction networksTransduction cascadeEnrichment analysisGene expressionCommon genesMolecular mechanismsSignaling pathwaysGenesNovel MycLiterature miningMolecular modePathwayMetformin actionDrug actionDisease pathogenesis
2006
A natural language processing (NLP) tool to assist in the curation of the laboratory Mouse Tumor Biology Database.
Xu H, Krupke D, Blake J, Friedman C. A natural language processing (NLP) tool to assist in the curation of the laboratory Mouse Tumor Biology Database. AMIA Annual Symposium Proceedings 2006, 2006: 1150. PMID: 17238769, PMCID: PMC1839428.Peer-Reviewed Original ResearchConceptsMouse Genome Informatics groupNatural language processing toolsModel organism databaseMouse Tumor Biology DatabaseLanguage processing toolsBiology databasesOrganism databasesGenomic informationBiological communitiesProcessing toolsSpecific organismsInformatics GroupOrganismsKey informationDatabaseInformationSubstantial effortCurationTool