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Biomedical Research

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Biosignature Discovery for Substance Use Disorders Using Statistical Learning.

Trends in molecular medicine
There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and 'omic' ...

Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets.

Journal of healthcare engineering
Many real-world medical datasets contain some proportion of missing (attribute) values. In general, missing value imputation can be performed to solve this problem, which is to provide estimations for the missing values by a reasoning process based o...

Harnessing electronic medical records to advance research on multiple sclerosis.

Multiple sclerosis (Houndmills, Basingstoke, England)
BACKGROUND: Electronic medical records (EMR) data are increasingly used in research, but no studies have yet evaluated similarity between EMR and research-quality data and between characteristics of an EMR multiple sclerosis (MS) population and known...

BioCreative VI Precision Medicine Track system performance is constrained by entity recognition and variations in corpus characteristics.

Database : the journal of biological databases and curation
Precision medicine aims to provide personalized treatments based on individual patient profiles. One critical step towards precision medicine is leveraging knowledge derived from biomedical publications-a tremendous literature resource presenting the...

Gene ontology concept recognition using named concept: understanding the various presentations of the gene functions in biomedical literature.

Database : the journal of biological databases and curation
OBJECTIVE: A major challenge in precision medicine is the development of patient-specific genetic biomarkers or drug targets. The firsthand information of the genes associated with the pathologic pathways of interest is buried in the ocean of biomedi...

Disease named entity recognition from biomedical literature using a novel convolutional neural network.

BMC medical genomics
BACKGROUND: Automatic disease named entity recognition (DNER) is of utmost importance for development of more sophisticated BioNLP tools. However, most conventional CRF based DNER systems rely on well-designed features whose selection is labor intens...

A multiple distributed representation method based on neural network for biomedical event extraction.

BMC medical informatics and decision making
BACKGROUND: Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification...

Vector representations of multi-word terms for semantic relatedness.

Journal of biomedical informatics
This paper presents a comparison between several multi-word term aggregation methods of distributional context vectors applied to the task of semantic similarity and relatedness in the biomedical domain. We compare the multi-word term aggregation met...

Supervised Learning and Knowledge-Based Approaches Applied to Biomedical Word Sense Disambiguation.

Journal of integrative bioinformatics
Word sense disambiguation (WSD) is an important step in biomedical text mining, which is responsible for assigning an unequivocal concept to an ambiguous term, improving the accuracy of biomedical information extraction systems. In this work we follo...

Identifying genotype-phenotype relationships in biomedical text.

Journal of biomedical semantics
BACKGROUND: One important type of information contained in biomedical research literature is the newly discovered relationships between phenotypes and genotypes. Because of the large quantity of literature, a reliable automatic system to identify thi...