Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …

The emergence of nanotechnology as a key enabling technology over the past years has
opened avenues for new and innovative applications in nanomedicine. From the business …

Data-centric approaches have been used to develop predictive methods for elucidating
uncharacterized properties of proteins; however, studies indicate that these methods should …

The identification of drug/compound–target interactions (DTIs) constitutes the basis of drug
discovery, for which computational predictive approaches have been developed. As a …

Motivation Antibiotic resistance constitutes a major public health crisis, and finding new
sources of antimicrobial drugs is crucial to solving it. Bacteriocins, which are bacterially …

The adaptive branch of the immune system learns pathogenic patterns and remembers them
for future encounters. It does so through dynamic and diverse repertoires of T-and B-cell …

MicroRNAs (miRNAs) silence genes by binding to messenger RNAs, whereas long non-
coding RNAs (lncRNAs) act as competitive endogenous RNAs (ceRNAs) that can relieve …

AMPylation is a biologically significant yet understudied post-translational modification
where an adenosine monophosphate (AMP) group is added to Tyrosine and Threonine …

Neural models have been able to obtain state-of-the-art performances on several genome
sequence-based prediction tasks. Such models take only nucleotide sequences as input …

Recently several deep learning models have been used for DNA sequence based
classification tasks. Often such tasks require long and variable length DNA sequences in the …