The rise and fall of artificial neural networks is well documented in the scientific literature of
both computer science and computational chemistry. Yet almost two decades later, we are …

Background Predicting protein function and structure from sequence is one important
challenge for computational biology. For 26 years, most state-of-the-art approaches …

Machine learning methods are widely used in bioinformatics and computational and
systems biology. Here, we review the development of machine learning methods for protein …

PredictProtein is a meta-service for sequence analysis that has been predicting structural
and functional features of proteins since 1992. Queried with a protein sequence it returns …

Motivation: The accurate prediction of residue–residue contacts, critical for maintaining the
native fold of a protein, remains an open problem in the field of structural bioinformatics …

Many genetic variations are single nucleotide polymorphisms (SNPs). Non-synonymous
SNPs are 'neutral'if the resulting point-mutated protein is not functionally discernible from the …

Motivation: Residue–residue contact prediction is important for protein structure prediction
and other applications. However, the accuracy of current contact predictors often barely …

The existence and functioning of intrinsically disordered proteins (IDPs) challenge the
classical structure-function paradigm that equates function with a well-defined 3D structure …

The application of data mining and machine learning techniques to biological and
biomedicine data continues to be an ubiquitous research theme in current bioinformatics …

Background Predicting protein residue-residue contacts is an important 2D prediction task. It
is useful for ab initio structure prediction and understanding protein folding. In spite of steady …