AIMC Journal:
IEEE transactions on computational biology and bioinformatics

Super-enhancers (SEs), comprising clusters of transcriptional regulatory elements, play essential roles in gene expression regulation and cell fate determination. Current computational methods for identifying SEs from genomic sequences face challenge...

Protein toxicity prediction is crucial for drug discovery, safety assessment, and toxicological research. This study introduces $\mathrm{Deep\_{T}PPred}$, a novel hybrid deep learning (DL) model that integrates Convolutional Neural Networks (CNN) and...

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that remains a global challenge due to its complex pathology and the lack of definitive diagnostic tools. This paper introduces an innovative approach to predicting and analyzing Al...

Training machine learning models on large datasets, such as those derived from single-cell RNA sequencing (scRNA-seq), poses significant challenges due to high computational and memory demands. Additionally, integrating data from diverse sources intr...

Viruses are a significant threat to human life, as demonstrated by the global COVID-19 pandemic and the Ebola outbreak. Diseases such as smallpox, AIDS, hepatitis, liver cancer, and cervical cancer often caused by the Human Papillomavirus can lead to...

Accurately identifying drug-target interactions (DTIs) is a critical step in drug discovery. While structure-based drug design methods demonstrate impressive docking prediction accuracy, their heavy computational demands and resource intensive nature...