1. < Harbor UCLA

Listopad S, Magnan C, Asghar A, Stolz A, Tayek JA, Liu ZX, Morgan TR, Norden-Krichmar TM. Differentiating between liver diseases by applying multiclass machine learning approaches to transcriptomics of liver tissue or blood-based samples. JHEP Rep. 2022 Aug 18;4(10):100560. doi: 10.1016/j.jhepr.2022.100560. PMID: 36119721; PMCID: PMC9472076.

AC, alcohol-associated cirrhosis, AH, alcohol-associated hepatitis, AKR1B10, aldo-keto reductase family 1 member B10, BTM, blood transcription module, Classification, DE, differential expression, FPKM, fragments per kilobase of exon model per million reads mapped, GSEA, gene set-enrichment analysis, IG, information gain, IPA, Ingenuity Pathway Analysis, LR, logistic regression, LTCDS, liver tissue cell distribution system, LV, liver tissue, ML, machine learning, MMP, matrix metalloproteases, NAFLD, non-alcohol-associated fatty liver disease, PBMCs, peripheral blood mononuclear cells, RNA sequencing, RNA-seq, RNA sequencing, SCAHC, Southern California Alcoholic Hepatitis Consortium, SVM, support vector machine, TNF, tumor necrosis factor, alcohol-associated liver disease, biomarker discovery, kNN, k-nearest neighbors,