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Mimvec: a deep learning approach for analyzing the human phenome.

Mingxin Gan¹, Wenran Li², Wanwen Zeng²

¹Department of Management Science and Engineering, Dongling School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China.

BMC Systems Biology

|September 28, 2017

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This summary is machine-generated.

This study introduces mimvec, a deep learning method to analyze the human phenome, improving disease gene discovery by capturing semantic relationships in biomedical text and overcoming limitations of traditional methods.

Area of Science:

Genomics
Bioinformatics
Natural Language Processing

Background:

Traditional methods for disease gene inference often use TF-IDF, which ignores semantic relationships and creates high-dimensional vectors.
Existing frameworks struggle to capture the intrinsic semantic characteristics of biomedical documents.

Purpose of the Study:

To propose mimvec, a novel framework utilizing deep learning for human phenome analysis.
To overcome the limitations of traditional TF-IDF methods in capturing semantic nuances in biomedical data.

Main Methods:

Developed mimvec, a deep learning approach for analyzing the human phenome.
Converted 24,061 Online Mendelian Inheritance in Man (OMIM) records into low-dimensional vectors.
Derived pairwise phenotype similarities for 7988 human inherited diseases.

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Main Results:

Vector representations enabled effective classification of phenotype and gene records.
Successfully discriminated diseases based on inheritance styles and mechanisms.
Demonstrated that phenotype overlap implies genotype overlap when analyzing phenome data with genomic data.
Prioritized candidate genes using derived phenotype similarities, showing advantages over existing methods.

Conclusions:

The mimvec method captures semantic relationships and mitigates the dimensionality issues of TF-IDF.
This approach is expected to have wide applications in analyzing the growing volume of electronic health records for precision medicine.