AIMC Journal:
IEEE transactions on computational biology and bioinformatics
Showing 11 to 20 of 56 articles
KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction.
IEEE transactions on computational biology and bioinformatics
Jul 21, 2026
Accurate prediction of protein-ligand binding affinity is critical for drug discovery. While recent deep learning approaches have demonstrated promising results, they often rely solely on structural features of proteins and ligands, overlooking their...
Predicting miRNA-disease associations based on adaptive neighborhood propagation and feature spatial recombination.
IEEE transactions on computational biology and bioinformatics
Jul 21, 2026
MicroRNAs (miRNAs) are critical regulators in biological processes such as cell proliferation, differentiation, and apoptosis, with their aberrant expression strongly linked to a range of complex diseases. Because traditional experimental methods for...
Identifying Cancer Driver Genes Based on Vision Transformer and Hierarchical Feature Fusion Module.
IEEE transactions on computational biology and bioinformatics
Jul 17, 2026
Cancer is a global public health problem that poses a huge threat to human life and health. The identification of cancer driver genes helps to discover the intrinsic mechanisms of cancer occurrence and development. Existing deep learning-based method...
GraphGDel: Constructing and Learning Graph Representations of Genome-Scale Metabolic Models for Growth-Coupled Gene Deletion Prediction.
IEEE transactions on computational biology and bioinformatics
Jul 17, 2026
In genome-scale constraint-based metabolic models, gene deletion strategies are essential for achieving growth-coupled production, where cell growth and target metabolite synthesis occur simultaneously. Despite the inherently networked nature of geno...
scVAEAT: An Integrative Attention-Augmented Variational Autoencoder for Predicting Single-Cell Perturbation Responses.
IEEE transactions on computational biology and bioinformatics
Jul 7, 2026
Understanding how individual cells respond to genetic or environmental perturbations is crucial for deciphering disease mechanisms and advancing precision medicine. However, predicting single-cell perturbation responses from standard single-cell RNA ...
Exploring Complex Genetic Mechanisms in Brain Imaging Genetics via a New Multi-task Learning Method.
IEEE transactions on computational biology and bioinformatics
Jul 1, 2026
Brain imaging genetics generally combines genotype data with brain structure and functional measures to investigate the genetic basis of neurological disorders. Multimodal brain imaging data carry different but complementary information, which can cl...
Decoding Gene-Disease Associations with Computational Methods: A Survey.
IEEE transactions on computational biology and bioinformatics
Jun 25, 2026
Identifying gene-disease associations (GDAs) remains a fundamental challenge in biomedical research due to the enormous combinatorial space of candidate gene-disease pairs and the limited scalability of experimental validation. As wet lab studies can...
Parameter Efficient Deep Learning Models for Multi-Target Binding Affinity and hERG Cardiotoxicity Prediction.
IEEE transactions on computational biology and bioinformatics
Jun 22, 2026
Accurately predicting binding affinity and toxicity is critical to drug discovery, offering the potential to reduce development costs and enhance safety profiles. Traditional computational methods, such as molecular docking and quantitative structure...
GENIE: A Two-Stage Interpretable Deep Learning Framework for Revealing the High-Order Genetic Interaction Network of Alzheimer's Disease.
IEEE transactions on computational biology and bioinformatics
Jun 18, 2026
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive impairment and memory loss. The underlying mechanisms of AD onset and progression remain unclear. To date, no effective method has been able to uncover th...
A Biophysical Prior-Based Hierarchical Graph Pooling Strategy for Protein Function Prediction.
IEEE transactions on computational biology and bioinformatics
Jun 10, 2026
Accurate prediction of protein function is fundamental to understanding biological systems. Graph Neural Networks (GNNs) have demonstrated promise in this field; however, effectively aggregating residue-level features into discriminative global repre...
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