AIMC Topic: Disease

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The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions.

BMC medical informatics and decision making Mar 4, 2025
Explainable Artificial Intelligence (XAI) enhances transparency and interpretability in AI models, which is crucial for trust and accountability in healthcare. A potential application of XAI is disease prediction using various data modalities. This s...
Artificial Intelligence Disease Diagnosis Humans
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Annotated corpus for traditional formula-disease relationships in biomedical articles.

Scientific data Jan 7, 2025
The Traditional Formula (TF), a combination of herbs prepared in accordance with traditional medicine principles, is increasingly garnering global attention as an alternative to modern medicine. Specifically, there is growing interest in exploring TF...
Disease Medicine, Traditional Humans Natural Language Processing PubMed
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Self-Supervised Contrastive Learning on Attribute and Topology Graphs for Predicting Relationships Among lncRNAs, miRNAs and Diseases.

IEEE journal of biomedical and health informatics Jan 7, 2025
Exploring associations between long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is crucial for disease prevention, diagnosis and treatment. While determining these relationships experimentally is resource-intensive and time-consuming, ...
RNA, Long Noncoding Supervised Machine Learning Computational Biology MicroRNAs Humans Algorithms Disease
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Integration of biological data via NMF for identification of human disease-associated gene modules through multi-label classification.

PloS one Dec 12, 2024
Proteins associated with multiple diseases often interact, forming disease modules that are critical for understanding disease mechanisms. This study integrates protein-protein interactions (PPIs) and Gene Ontology data using non-negative matrix fact...
Protein Interaction Maps Protein Interaction Mapping Humans Genetic Predisposition to Disease Disease Gene Regulatory Networks Algorithms Computational Biology Gene Ontology
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MGCNRF: Prediction of Disease-Related miRNAs Based on Multiple Graph Convolutional Networks and Random Forest.

IEEE transactions on neural networks and learning systems Oct 29, 2024
Increasing microRNAs (miRNAs) have been confirmed to be inextricably linked to various diseases, and the discovery of their associations has become a routine way of treating diseases. To overcome the time-consuming and laborious shortcoming of tradit...
Humans Computational Biology MicroRNAs Random Forest Neural Networks, Computer Algorithms Disease
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SSGU-CD: A combined semantic and structural information graph U-shaped network for document-level Chemical-Disease interaction extraction.

Journal of biomedical informatics Aug 29, 2024
Document-level interaction extraction for Chemical-Disease is aimed at inferring the interaction relations between chemical entities and disease entities across multiple sentences. Compared with sentence-level relation extraction, document-level rela...
Disease Humans Algorithms Data Mining Natural Language Processing Semantics
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Identifying diseases symptoms and general rules using supervised and unsupervised machine learning.

Scientific reports Aug 2, 2024
The symptoms of diseases can vary among individuals and may remain undetected in the early stages. Detecting these symptoms is crucial in the initial stage to effectively manage and treat cases of varying severity. Machine learning has made major adv...
Disease Unsupervised Machine Learning Supervised Machine Learning Adult Male Middle Aged Support Vector Machine Algorithms Female Humans
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Automated annotation of disease subtypes.

Journal of biomedical informatics May 1, 2024
BACKGROUND: Distinguishing diseases into distinct subtypes is crucial for study and effective treatment strategies. The Open Targets Platform (OT) integrates biomedical, genetic, and biochemical datasets to empower disease ontologies, classifications...
Humans Computational Biology Machine Learning Disease Deep Learning Algorithms ROC Curve
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FedOSS: Federated Open Set Recognition via Inter-Client Discrepancy and Collaboration.

IEEE transactions on medical imaging Jan 2, 2024
Open set recognition (OSR) aims to accurately classify known diseases and recognize unseen diseases as the unknown class in medical scenarios. However, in existing OSR approaches, gathering data from distributed sites to construct large-scale central...
Disease Machine Learning Software Humans
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Predicting pathogenic protein variants.

Science (New York, N.Y.) Sep 19, 2023
Machine-learning algorithm uses structure prediction to spot disease-causing mutations.
Protein Conformation Sequence Analysis, DNA Machine Learning Mutation, Missense Genetic Variation Disease Algorithms Mutant Proteins
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