Zhongyang Liu

TL;DR: BATMAN-TCM will contribute to the understanding of the “multi-component, multi-target and multi-pathway” combinational therapeutic mechanism of TCM, and provide valuable clues for subsequent experimental validation, accelerating the elucidation of TCm’s molecular mechanism.

TL;DR: This study maps the interactions of an unbiased selection of 5026 human liver expression proteins by yeast two‐hybrid technology and establishes a human liver protein interaction network (HLPN) composed of 3484 interactions among 2582 proteins.

TL;DR: The authors take an in silico naïve Bayesian classifier approach to integrate multiple lines of evidence for E3-substrate prediction, enabling prediction of the proteome-wide human E3 ligase interaction network.

TL;DR: This method combines multiple heterogeneous biological evidences, including model organism protein-protein interaction, interaction domain, functional annotation, gene expression, genome context, and network topology structure, to assign reliability to the human protein- protein interactions identified by high throughput experiments.

TL;DR: This work helps to understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high throughput finding of self- Interacting proteins and provide clues for elucidating their functions.