AI Medical Compendium Topic

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Disease

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A literature-driven method to calculate similarities among diseases.

Computer methods and programs in biomedicine
BACKGROUND: "Our lives are connected by a thousand invisible threads and along these sympathetic fibers, our actions run as causes and return to us as results". It is Herman Melville's famous quote describing connections among human lives. To paraphr...

HPOSim: an R package for phenotypic similarity measure and enrichment analysis based on the human phenotype ontology.

PloS one
BACKGROUND: Phenotypic features associated with genes and diseases play an important role in disease-related studies and most of the available methods focus solely on the Online Mendelian Inheritance in Man (OMIM) database without considering the con...

HyDRA: gene prioritization via hybrid distance-score rank aggregation.

Bioinformatics (Oxford, England)
UNLABELLED: Gene prioritization refers to a family of computational techniques for inferring disease genes through a set of training genes and carefully chosen similarity criteria. Test genes are scored based on their average similarity to the traini...

Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data.

Nucleic acids research
The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens ...

DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis.

Bioinformatics (Oxford, England)
SUMMARY: Disease ontology (DO) annotates human genes in the context of disease. DO is important annotation in translating molecular findings from high-throughput data to clinical relevance. DOSE is an R package providing semantic similarity computati...

Evaluating the state of the art in disorder recognition and normalization of the clinical narrative.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The ShARe/CLEF eHealth 2013 Evaluation Lab Task 1 was organized to evaluate the state of the art on the clinical text in (i) disorder mention identification/recognition based on Unified Medical Language System (UMLS) definition (Task 1a) a...

SSL-VQ: vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases.

Bioinformatics (Oxford, England)
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)
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
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
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. ...