Evidence map›Paper›PMID 32883271›Full record

ArticleBMC medical informatics and decision making2020

Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization.

Charles C N Wang, Jennifer Jin, Jan-Gowth Chang, Masahiro Hayakawa, Atsushi Kitazawa, Jeffrey J P Tsai, Phillip C-Y Sheu

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Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Charles C N WangDepartment of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
Jennifer JinDepartment of EECS and BME, University of California, Irvine, USA.
Jan-Gowth ChangDepartment of Laboratory Medicine, China Medical University Hospital, Taichung, Taiwan.
Masahiro HayakawaNEC Solution Innovators, Koto-ku, Tokyo, Japan.
Atsushi KitazawaNEC Solution Innovators, Koto-ku, Tokyo, Japan.
Jeffrey J P TsaiDepartment of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
Phillip C-Y SheuDepartment of EECS and BME, University of California, Irvine, USA. psheu@uci.edu.ORCID 0000-0003-2036-850X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide.

methodsThis paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of co-occurring genes that are the most influential based on an influence maximization algorithm.

resultsThe sets of co-occurring genes that are the most influential that we discover include RARA - CRBP1, CASP3 - BCL2, BCL2 - CASP3 - CRBP1, RARA - CASP3 - CRBP1, FOXJ1 - RASSF3 - ESR1, FOXJ1 - RASSF1A - ESR1, FOXJ1 - RASSF1A - TNFAIP8 - ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer.

conclusionsOur pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications.

Indexed as

Data MiningGastrointestinal NeoplasmsGenes, NeoplasmAlgorithmsApoptosis Regulatory ProteinsHumansApoptosis Regulatory ProteinsBi-LSTM-CNN-CRFCo-occurrence networkGastrointestinal cancerInfluence maximizationText mining

Identifiers

PMID32883271
PMCPMC7469322

What Socratic holds

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.