Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer - Xu, Qingyu; Ma, Ling; Streuer, Alexander; Altrock, Eva; Schmitt, Nanni; Rapp, Felicitas; Klär, Alessa; Nowak, Verena; Obländer, Julia; Weimer, Nadine; Palme, Iris; Göl, Melda; et al. (Cell communication and signaling, 2025)
Keywords: Algorithms; Bioinformatics; Biomedical and Life Sciences; Breast cancer; Cancer; Care and treatment; Cell Biology; Computational Biology; Computer Simulation; Cytokines and Growth Factors; Data mining; Datasets; Development and progression; Enzymes; Gene expression; Gene Expression Regulation, Neoplastic; Gene regulation; Genomes; Genomics; Glioma; Glioma cells; Growth factors; Health aspects; Humans; Immune checkpoint inhibitors; Immunoregulation; Kidney cancer; Laminin; Learning algorithms; Life Sciences; Lysyl oxidases; Machine Learning; Macrophages; Methods; Neoplasms - diagnosis; Neoplasms - drug therapy; Neoplasms - enzymology; Neoplasms - genetics; Neoplasms - pathology; Nomograms; Oncology, Experimental; Oxidases; Pan cancer; Predictions; Prognosis; Prognostic model; Protein-Ligand Interactions; Protein-Lysine 6-Oxidase - genetics; Protein-Lysine 6-Oxidase - metabolism; Receptors; Response prediction; RNA sequencing; Statistical methods; Survival analysis; Therapeutic targets; Tumors; Vascular endothelial growth factor receptors; China; Prognostic model; Lysyl oxidases; Response prediction; Machine learning; Pan cancer