Differentiating between liver diseases by applying multiclass machine learning approaches to transcriptomics of liver tissue or blood-based samples
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,
Related Posts
Chao E, Marshalek JP, Yashar D, Tomassetti S. Doxorubicin, bleomycin, vinblastine, and dacarbazine for Hodgkin lymphoma: Real-world experience from a Los Angeles County hospital. SAGE[...]
Kalantar-Zadeh K, Zisman-Ilani Y. Understanding the Determinants of Decision Readiness in Kidney Replacement Therapy: Shared Decision Making, Health Literacy, and Population-Health Strategies. Clin J Am[...]
Adeyemo S, Crews DC, Lentine KL, Zisman-Ilani Y, Lincoln K, Flores GM, Bunnapradist S, Ferrey AJ, Reddy UG, Muzaale AD, Rule AD, Saunders M, Garg[...]