Artificial Intelligence In Medicine
ISSN: 0933-3657
Datasets associated with articles published in Artificial Intelligence In Medicine
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- Data for: Fuzzy inference model based on triaxial signals for pronation and supination assessment in Parkinson´s disease patientsQuantification of biomechanical features during the pronation and supination hand movements of patients with Parkisnons disease.
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- Data for: A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical-datasetsbasic dataset of stroke prediction
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- Data for: On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditionsThe attached files contain the information of the Fuzzy Inference Systems that constitute the risk evaluation model proposed in the paper
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- Data for: The Virtual Doctor: An Interactive Artificial Intelligence based on Deep Learning for Non-Invasive Prediction of Diabetessurvey data
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- Data for: Sparse Support Vector Machines with L0 Approximation for Ultra-high Dimensional Omics DataMATLAB toolbox for L0SVM
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- Data for: Sparse Support Vector Machines with L0 Approximation for Ultra-high Dimensional Omics DataSoftware for L0SVM
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- Data for: Detection of protein complexes from multiple protein interaction networks using graph embeddingThere are three folders. The networks folder contains the protein-protein interaction networks of different species. The golden complexes folder contains golden complexes of different species. And the ID map folder contains the id mappings of different species.
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- Data for: Combining quantitative and qualitative approaches for visual case-based reasoning on breast cancerResult data of the user study (CSV file) and statistical analysis (in R).
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- Data for: An Ontology-Driven Clinical Decision Support System (IDDAP) for Infectious Disease Diagnosis and Antibiotics PrescriptionS1. antibiotic_change_IRI.owl. A domain ontology containing 1,267,004 classes, 7,608,725 axioms, and 1,266,993 members of ‘SubClassOf” that pertain to infectious diseases, bacteria, syndromes, anti-bacterial drugs and other relevant components was constructed. The ontology is stored in an OWL format file and can be opened as a text file.
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- Uncovering Hidden Therapeutic Indications through Drug Repurposing with Graph Neural Networks and Heterogeneous DataDrug repurposing has gained the attention of many in the recent years. The practice of repurposing existing drugs for new therapeutic uses helps to simplify the drug discovery process, which in turn reduces the costs and risks that are associated with de novo development. Representing biomedical data in the form of a graph is a simple and effective method to depict the underlying structure of the information. Using deep neural networks in combination with this data represents a promising approach to address drug repurposing. This paper presents BEHOR a more comprehensive version of the REDIRECTION model, which was previously presented. Both versions utilize the DISNET biomedical graph as the primary source of information, providing the model with extensive and intricate data to tackle the drug repurposing challenge. This new version’s results for the reported metrics in the RepoDB test are 0.9604 for AUROC and 0.9518 for AUPRC. Additionally, a discussion is provided regarding some of the novel predictions to demonstrate the reliability of the model. The authors believe that BEHOR holds promise for generating drug repurposing hypotheses and could greatly benefit the field
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