PANDA: Protein function prediction using domain architecture and affinity propagation

Protein Function Prediction using Phylogenomics, Domain Architecture Analysis, Data Integration, and Lexical Scoring

Protein Interaction Networks: Protein Domain Interaction and Protein Function Prediction

Most of a cell’s functional processes involve interactions among proteins, and a key challenge in proteomics is to better understand these complex interaction graphs at a systems level. Because of their importance in development and disease, protein-protein interactions (PPIs) have been the subject of intense research in recent years. In addition, a greater understanding of PPIs can be achieved...

CScore: a simple yet effective scoring function for protein-ligand binding affinity prediction using modified CMAC learning architecture.

Protein-ligand docking is a computational method to identify the binding mode of a ligand and a target protein, and predict the corresponding binding affinity using a scoring function. This method has great value in drug design. After decades of development, scoring functions nowadays typically can identify the true binding mode, but the prediction of binding affinity still remains a major prob...

The SBASE domain sequence resource, release 12: prediction of protein domain-architecture using support vector machines

SBASE (http://www.icgeb.trieste.it/sbase) is an online resource designed to facilitate the detection of domain homologies based on sequence database search. The present release of the SBASE A library of protein domain sequences contains 972,397 protein sequence segments annotated by structure, function, ligand-binding or cellular topology, clustered into 8547 domain groups. SBASE B contains 169...

Semi-supervised Protein Function Prediction via Sequential Linear Neighborhood Propagation

Predicting protein function is one of the most challenging problems of the post-genomic era. The development of experimental methods for genome scale analysis of molecular interaction networks has provided new approaches to inferring protein function. In this paper we introduce a new graph-based semi-supervised classification algorithm Sequential Linear Neighborhood Propagation (SLNP), which ad...