Bi-Qing Li

TL;DR: This study developed a computational method to identify colorectal cancer-related genes based on the gene expression profiles, and the shortest path analysis of functional protein association networks, which indicated that the method may become a useful tool, or at least plays a complementary role to the existing method.

TL;DR: It was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations.

TL;DR: A novel predictor based on Random Forest algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS) is developed that incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility.

TL;DR: A novel predictor based on nearest neighbor algorithm with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS) is developed and it is anticipated that the prediction method may become a useful tool for identifying the protein S-nitrosylation sites.

TL;DR: A novel predictor based on Random Forest algorithm (RF) using maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS) is developed that makes much more reliable predictions in terms of the overall prediction accuracy.