The advent of full‐length transcriptome sequencing technologies has accelerated the
discovery of novel splicing isoforms. However, existing alternative splicing (AS) tools are …

Investigating the interactions among various drugs is an indispensable issue in the field of
computational biology. Scientific literature represents a rich source for the retrieval of …

Abstract Machine Learning (ML) and Artificial Intelligence (AI) have become an integral part
of the drug discovery and development value chain. Many teams in the pharmaceutical …

Artificial Intelligence (AI) and its data-centric branch of machine learning (ML) have greatly
evolved over the last few decades. However, as AI is used increasingly in real world use …

Positive and unlabeled (PU) learning is to learn a binary classifier with good generalization
ability from PU data. A variety of PU learning algorithms with promising performance have …

Identifying drug-target interactions through computational methods is raised an important
and key step in the process of drug discovery and drug-oriented research during the last …

Positive and unlabeled (PU) learning is an important research topic in machine learning
area, whose aim is to learn a good classifier from PU data. Due to its wide applications, a …

For many classification tasks, particularly in the bioinformatics field, only experimentally
validated positive samples are available, and experimentally validated negative samples …

Drug-Drug Interactions (DDIs) can alter a drug's efficacy and lead to adverse effects.
Predicting potential DDIs during clinical trials is challenging; thus, computational methods …

The simultaneous use of multiple drugs, known as polypharmacy, heightens the risks of
harmful side effects due to drug‐drug interactions. Predicting these interactions is crucial in …