Abstract Machine learning methods for extracting patterns from high-dimensional data are
very important in the biological sciences. However, in certain cases, real-world applications …

LncRNAs have attracted lots of attentions from researchers worldwide in recent decades.
With the rapid advances in both experimental technology and computational prediction …

Abstract Protein-Protein Interactions (PPIs) are fundamental means of functions and
signalings in biological systems. The massive growth in demand and cost associated with …

Drug–drug interactions (DDIs) are known as the main cause of life-threatening adverse
events, and their identification is a key task in drug development. Existing computational …

Identification of drug–target interactions is an important process in drug discovery. Although
high-throughput screening and other biological assays are becoming available …

Computational prediction of drug–target interactions (DTIs) has become an essential task in
the drug discovery process. It narrows down the search space for interactions by suggesting …

Motivation: More and more evidences have indicated that long–non-coding RNAs (lncRNAs)
play critical roles in many important biological processes. Therefore, mutations and …

A number of supervised machine learning models have recently been introduced for the
prediction of drug–target interactions based on chemical structure and genomic sequence …

Drug–target interactions (DTIs) are central to current drug discovery processes and public
health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse …

Proteomics has been revolutionized by large protein language models (PLMs), which learn
unsupervised representations from large corpora of sequences. These models are typically …