AI Medical Compendium Topic

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Disease

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TaggerOne: joint named entity recognition and normalization with semi-Markov Models.

Bioinformatics (Oxford, England)
MOTIVATION: Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing machine le...

A knowledge-based approach for predicting gene-disease associations.

Bioinformatics (Oxford, England)
MOTIVATION: Recent advances of next-generation sequence technologies have made it possible to rapidly and inexpensively identify gene variations. Knowing the disease association of these gene variations is important for early intervention to treat de...

Learning disease relationships from clinical drug trials.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Our objective is to test the limits of the assumption that better learning from data in medicine requires more granular data. We hypothesize that clinical trial metadata contains latent scientific, clinical, and regulatory expert knowledge...

Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database.

PloS one
Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Co...

miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases.

Journal of biomedical semantics
BACKGROUND: MicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-diseas...

The Disease Portals, disease-gene annotation and the RGD disease ontology at the Rat Genome Database.

Database : the journal of biological databases and curation
The Rat Genome Database (RGD;http://rgd.mcw.edu/) provides critical datasets and software tools to a diverse community of rat and non-rat researchers worldwide. To meet the needs of the many users whose research is disease oriented, RGD has created a...

Causal Phenotype Discovery via Deep Networks.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The rapid growth of digital health databases has attracted many researchers interested in using modern computational methods to discover and model patterns of health and illness in a research program known as computational phenotyping. Much of the wo...

OVA: integrating molecular and physical phenotype data from multiple biomedical domain ontologies with variant filtering for enhanced variant prioritization.

Bioinformatics (Oxford, England)
MOTIVATION: Exome sequencing has become a de facto standard method for Mendelian disease gene discovery in recent years, yet identifying disease-causing mutations among thousands of candidate variants remains a non-trivial task.

SFM: A novel sequence-based fusion method for disease genes identification and prioritization.

Journal of theoretical biology
The identification of disease genes from human genome is of great importance to improve diagnosis and treatment of disease. Several machine learning methods have been introduced to identify disease genes. However, these methods mostly differ in the p...