Machine learning empowers phosphoproteome prediction in cancers.

ABSTRACT:

Motivation

Reversible protein phosphorylation is an essential post-translational modification regulating protein functions and signaling pathways in many cellular processes. Aberrant activation of signaling pathways often contributes to cancer development and progression. The mass spectrometry-based phosphoproteomics technique is a powerful tool to investigate the site-level phosphorylation of the proteome in a global fashion, paving the way for understanding the regulatory mechanisms underlying cancers. However, this approach is time-consuming and requires expensive instruments, specialized expertise and a large amount of starting material. An alternative in silico approach is predicting the phosphoproteomic profiles of cancer patients from the available proteomic, transcriptomic

SUBMITTER: Li H 

PROVIDER: S-EPMC7868059 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature