AIMC Topic: Computational Biology
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Interpretable Dynamic Directed Graph Convolutional Network for Multi-Relational Prediction of Missense Mutation and Drug Response.
IEEE journal of biomedical and health informatics
Feb 10, 2025
Tumor heterogeneity presents a significant challenge in predicting drug responses, especially as missense mutations within the same gene can lead to varied outcomes such as drug resistance, enhanced sensitivity, or therapeutic ineffectiveness. These ...
Predicting Clinical Anticancer Drug Response of Patients by Using Domain Alignment and Prototypical Learning.
IEEE journal of biomedical and health informatics
Feb 10, 2025
Anticancer drug response prediction is crucial in developing personalized treatment plans for cancer patients. However, High-quality patient anticancer drug response data are scarce and cell line data and patient data have different distributions, mo...
Analysis and validation of programmed cell death genes associated with spinal cord injury progression based on bioinformatics and machine learning.
International immunopharmacology
Feb 9, 2025
BACKGROUND: Spinal cord injury (SCI) is a severe condition affecting the central nervous system. It is marked by a high disability rate and potential for death. Research has demonstrated that programmed cell death (PCD) plays a significant role in th...
Conditional similarity triplets enable covariate-informed representations of single-cell data.
BMC bioinformatics
Feb 9, 2025
BACKGROUND: Single-cell technologies enable comprehensive profiling of diverse immune cell-types through the measurement of multiple genes or proteins per individual cell. In order to translate immune signatures assayed from blood or tissue into powe...
HybProm: An attention-assisted hybrid CNN-BiLSTM model for the interpretable prediction of DNA promoter.
Methods (San Diego, Calif.)
Feb 8, 2025
Promoter prediction is essential for analyzing gene structures, understanding regulatory networks, transcription mechanisms, and precisely controlling gene expression. Recently, computational and deep learning methods for promoter prediction have gai...
Severe deviation in protein fold prediction by advanced AI: a case study.
Scientific reports
Feb 8, 2025
Artificial intelligence (AI) and deep learning are making groundbreaking strides in protein structure prediction. AlphaFold is remarkable in this arena for its outstanding accuracy in modelling proteins fold based solely on their amino acid sequences...
Utilizing bioinformatics and machine learning to identify CXCR4 gene-related therapeutic targets in diabetic foot ulcers.
Frontiers in endocrinology
Feb 7, 2025
BACKGROUND: Diabetic foot ulcers (DFUs) are a serious complication of diabetes mellitus that manifests as chronic, non-healing wounds that have a significant impact on patients quality of life. Identifying key molecular targets associated with DFUs c...
Prediction of circRNA-Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural Predictor.
Biomolecules
Feb 6, 2025
Circular RNAs (circRNAs) have attracted increasing attention for their roles in human diseases, making the prediction of circRNA-disease associations (CDAs) a critical research area for advancing disease diagnosis and treatment. However, traditional ...
Protein ligand structure prediction: From empirical to deep learning approaches.
Current opinion in structural biology
Feb 5, 2025
Protein-ligand structure prediction methods, aiming to predict the three-dimensional complex structure and binding energy of a compound and target protein, are essential in many structure-based drug discovery pipelines, including virtual screening an...
Ontology-based expansion of virtual gene panels to improve diagnostic efficiency for rare genetic diseases.
BMC medical informatics and decision making
Feb 5, 2025
BACKGROUND: Virtual Gene Panels (VGP) comprising disease-associated causal genes are utilized in the diagnosis of rare genetic diseases to evaluate candidate genes identified by whole-genome and whole-exome sequencing. VGPs generated by the PanelApp ...
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