The complex language of eukaryotic gene expression remains incompletely understood.
Despite the importance suggested by many proteins variants statistically associated with …

Compared to the available protein sequences of different organisms, the number of revealed
protein–protein interactions (PPIs) is still very limited. So many computational methods have …

Sequence-derived structural and physicochemical features have frequently been used in the
development of statistical learning models for predicting proteins and peptides of different …

Protein sequence contains clues to its function. Functional prediction from sequence
presents a challenge particularly for proteins that have low or no sequence similarity to …

Protein–protein interactions regulate a variety of cellular processes. There is a great need
for computational methods as a complement to experimental methods with which to predict …

Protein interactions play an important role in the discovery of protein functions and pathways
in biological processes. This is especially true in case of the diseases caused by the loss of …

Sequence-derived structural and physicochemical features have been extensively used for
analyzing and predicting structural, functional, expression and interaction profiles of proteins …

Protein–protein interactions (PPIs), involved in many biological processes such as cellular
signaling, are ultimately encoded in the genome. Solving the problem of predicting protein …

In order to transform protein sequences into the feature vectors, several works have been
done, such as computing auto covariance (AC), conjoint triad (CT), local descriptor (LD) …

Advanced biological technologies are producing large-scale protein-protein interaction (PPI)
data at an ever increasing pace, which enable us to identify protein complexes from PPI …