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Computational Biology

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Deep Learning for Antimicrobial Peptides: Computational Models and Databases.

Journal of chemical information and modeling
Antimicrobial peptides are a promising strategy to combat antimicrobial resistance. However, the experimental discovery of antimicrobial peptides is both time-consuming and laborious. In recent years, the development of computational technologies (es...

SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction.

BMC bioinformatics
BACKGROUND: A massive amount of protein sequences have been obtained, but their functions remain challenging to discern. In recent research on protein function prediction, Protein-Protein Interaction (PPI) Networks have played a crucial role. Uncover...

DSANIB: Drug-Target Interaction Predictions With Dual-View Synergistic Attention Network and Information Bottleneck Strategy.

IEEE journal of biomedical and health informatics
Prediction of drug-target interactions (DTIs) is one of the crucial steps for drug repositioning. Identifying DTIs through bio-experimental manners is always expensive and time-consuming. Recently, deep learning-based approaches have shown promising ...

Mapping the learning curves of deep learning networks.

PLoS computational biology
There is an important challenge in systematically interpreting the internal representations of deep neural networks (DNNs). Existing techniques are often less effective for non-tabular tasks, or they primarily focus on qualitative, ad-hoc interpretat...

CellCircLoc: Deep Neural Network for Predicting and Explaining Cell Line-Specific CircRNA Subcellular Localization.

IEEE journal of biomedical and health informatics
The subcellular localization of circular RNAs (circRNAs) is crucial for understanding their functional relevance and regulatory mechanisms. CircRNA subcellular localization exhibits variations across different cell lines, demonstrating the diversity ...

Interpretable Dynamic Directed Graph Convolutional Network for Multi-Relational Prediction of Missense Mutation and Drug Response.

IEEE journal of biomedical and health informatics
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
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
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
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.)
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...