AIMC Topic: Disease
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SSL-VQ: vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases.
Bioinformatics (Oxford, England)
Feb 4, 2025
MOTIVATION: Identifying effective therapeutic targets poses a challenge in drug discovery, especially for uncharacterized diseases without known therapeutic targets (e.g. rare diseases, intractable diseases).
A comprehensive graph neural network method for predicting triplet motifs in disease-drug-gene interactions.
Bioinformatics (Oxford, England)
Feb 4, 2025
MOTIVATION: The drug-disease, gene-disease, and drug-gene relationships, as high-frequency edge types, describe complex biological processes within the biomedical knowledge graph. The structural patterns formed by these three edges are the graph moti...
EnrichDO: a global weighted model for Disease Ontology enrichment analysis.
GigaScience
Jan 6, 2025
BACKGROUND: Disease Ontology (DO) has been widely studied in biomedical research and clinical practice to describe the roles of genes. DO enrichment analysis is an effective means to discover associations between genes and diseases. Compared to hundr...
Comparing natural language processing representations of coded disease sequences for prediction in electronic health records.
Journal of the American Medical Informatics Association : JAMIA
Jun 20, 2024
OBJECTIVE: Natural language processing (NLP) algorithms are increasingly being applied to obtain unsupervised representations of electronic health record (EHR) data, but their comparative performance at predicting clinical endpoints remains unclear. ...
MACC: a visual interactive knowledgebase of metabolite-associated cell communications.
Nucleic acids research
Jan 5, 2024
Metabolite-associated cell communications play critical roles in maintaining the normal biological function of human through coordinating cells, organs and physiological systems. Though substantial information of MACCs has been continuously reported,...
Enhancing Semantic and Structure Modeling of Diseases for Diagnosis Prediction.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
Electronic Health Records (EHRs) are valuable healthcare data, aiding researchers and doctors in improving diagnosis accuracy. Researchers have developed several predictive models by learning disease representations to forecast the potential diagnosi...
Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion.
Briefings in bioinformatics
Jan 17, 2022
More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Rapid and accurate prediction of pathogenic nsSNPs can improve our understanding of the principle and design of new drugs, which...
Predicting miRNA-disease associations using an ensemble learning framework with resampling method.
Briefings in bioinformatics
Jan 17, 2022
MOTIVATION: Accumulating evidences have indicated that microRNA (miRNA) plays a crucial role in the pathogenesis and progression of various complex diseases. Inferring disease-associated miRNAs is significant to explore the etiology, diagnosis and tr...
HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks.
Briefings in bioinformatics
Jan 17, 2022
Identifying new indications for drugs plays an essential role at many phases of drug research and development. Computational methods are regarded as an effective way to associate drugs with new indications. However, most of them complete their tasks ...
Decoding the effects of synonymous variants.
Nucleic acids research
Dec 16, 2021
Synonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological impact and may lead to disease. Existing computational methods for evaluating the effect of sSNVs su...
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