MOTIVATION: Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierar...
Predicting the subcellular locations of proteins can provide useful hints that reveal their functions, increase our understanding of the mechanisms of some diseases, and finally aid in the development of novel drugs. As the number of newly discovered...
Conformationally heterogenous or "fuzzy" proteins have often been described as lacking specificity in binding and in function. The activation domains, for example, of transcriptional activators were labeled as negative noodles, with little structure ...
MOTIVATION: Protein fold classification is a critical step in protein structure prediction. There are two possible ways to classify protein folds. One is through template-based fold assignment and the other is ab-initio prediction using machine learn...
MOTIVATION: Protein subcellular localization prediction has been an important research topic in computational biology over the last decade. Various automatic methods have been proposed to predict locations for large scale protein datasets, where stat...
MOTIVATION: Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accurac...
MOTIVATION: Protein model quality assessment (QA) plays a very important role in protein structure prediction. It can be divided into two groups of methods: single model and consensus QA method. The consensus QA methods may fail when there is a large...
Predicting functions of proteins is a key issue in the post-genomic era. Some experimental methods have been designed to predict protein functions. However, these methods cannot accommodate the vast amount of sequence data due to their inherent diffi...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
The Protein Ontology (PRO) is the reference ontology for proteins in the Open Biomedical Ontologies (OBO) foundry and consists of three sub-ontologies representing protein classes of homologous genes, proteoforms (e.g., splice isoforms, sequence vari...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2017
The overarching goal of the Gene Ontology (GO) Consortium is to provide researchers in biology and biomedicine with all current functional information concerning genes and the cellular context under which these occur. When the GO was started in the 1...
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