VIPpred: a novel model for predicting variant impact on phosphorylation events driving carcinogenesis.

ABSTRACT: Disrupted protein phosphorylation due to genetic variation is a widespread phenomenon that triggers oncogenic transformation of healthy cells. However, few relevant phosphorylation disruption events have been verified due to limited biological experimental methods. Because of the lack of reliable benchmark datasets, current bioinformatics methods primarily use sequence-based traits to study variant impact on phosphorylation (VIP). Here, we increased the number of experimentally supported VIP events from less than 30 to 740 by manually curating and reanalyzing multi-omics data from 916 patients provided by the Clinical Proteomic Tumor Analysis Consortium. To predict VIP events in cancer cells, we developed VIPpred, a machine learning method characterized by multidimensional features that ex

SUBMITTER: Xu X 

PROVIDER: S-EPMC10782907 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature