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The MeSH-Gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for Semantic Similarity.

Studies in health technology and informatics
Eliciting semantic similarity between concepts remains a challenging task. Recent approaches founded on embedding vectors have gained in popularity as they have risen to efficiently capture semantic relationships. The underlying idea is that two word...

Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The increasing amount of scientific literature in biological and biomedical science research has created a challenge in continuous and reliable curation of the latest knowledge discovered, and automatic biomedical text-mining has been one of the answ...

Gene multifunctionality scoring using gene ontology.

Journal of bioinformatics and computational biology
Multifunctional genes are important genes because of their essential roles in human cells. Studying and analyzing multifunctional genes can help understand disease mechanisms and drug discovery. We propose a computational method for scoring gene mult...

Exploiting and assessing multi-source data for supervised biomedical named entity recognition.

Bioinformatics (Oxford, England)
MOTIVATION: Recognition of biomedical entities from scientific text is a critical component of natural language processing and automated information extraction platforms. Modern named entity recognition approaches rely heavily on supervised machine l...

Automatic recognition of self-acknowledged limitations in clinical research literature.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To automatically recognize self-acknowledged limitations in clinical research publications to support efforts in improving research transparency.

Improving precision in concept normalization.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Most natural language processing applications exhibit a trade-off between precision and recall. In some use cases for natural language processing, there are reasons to prefer to tilt that trade-off toward high precision. Relying on the Zipfian distri...

nala: text mining natural language mutation mentions.

Bioinformatics (Oxford, England)
MOTIVATION: The extraction of sequence variants from the literature remains an important task. Existing methods primarily target standard (ST) mutation mentions (e.g. 'E6V'), leaving relevant mentions natural language (NL) largely untapped (e.g. 'glu...

PubMedReco: A Real-Time Recommender System for PubMed Citations.

Studies in health technology and informatics
We present a recommender system, PubMedReco, for real-time suggestions of medical articles from PubMed, a database of over 23 million medical citations. PubMedReco can recommend medical article citations while users are conversing in a synchronous co...

A Case Study on Sepsis Using PubMed and Deep Learning for Ontology Learning.

Studies in health technology and informatics
We investigate the application of distributional semantics models for facilitating unsupervised extraction of biomedical terms from unannotated corpora. Term extraction is used as the first step of an ontology learning process that aims to (semi-)aut...

Probability Statements Extraction with Constrained Conditional Random Fields.

Studies in health technology and informatics
This paper investigates how to extract probability statements from academic medical papers. In previous work we have explored traditional classification methods which led to numerous false negatives. This current work focuses on constraining classifi...