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
Tran TV, Kinney GL, Comellas A, Hoth KF, Baldomero AK, Mamary AJ, Curtis JL, Hanania N, Casaburi R, Young KA, Kim V, Make B, Wan ...
Young EW, Zhao J, Pisoni RL, Piraino BM, Shen JI, Boudville N, Schreiber MJ, Teitelbaum I, Perl J, McCullough K. Peritoneal Dialysis-Associated Peritonitis Trends Using ...
Park HB, Lee J, Hong Y, Byungchang S, Kim W, Lee BK, Lin FY, Hadamitzky M, Kim YJ, Conte E, Andreini D, Pontone G, Budoff ...