Literature DB >> 35713859

Finding Gene Associations by Text Mining and Annotating it with Gene Ontology.

Oviya Ramalakshmi Iyyappan1, Sharanya Manoharan2.   

Abstract

Digitalization of the research articles and their maintenance in a database was the first stage toward the development of biomedical research. With the large amounts of research being published daily, it has created a large gap in accessing all the articles for review to a given problem. To understand any biological process, an insight into the role of each element in the genome is essential. But with this gap in manual curation of literature, there are chances that important biological information may be lost. Hence, text mining plays an important role in bridging this gap and extracting important biological information from the text, finding associations among them and predicting annotations. An annotation may be gene, gene products, gene names, their physical and functional characteristics, and so on. The process of annotations may be classified as structural annotation, functional annotation, and relational annotation. In this chapter, a basic protocol utilizing text mining to extract biological information and predict their functional role based on Gene Ontology is provided.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Functional annotation; Gene Ontology (GO); Gene function prediction; MeSH terms; Semantic similarity; Text mining

Mesh:

Substances:

Year:  2022        PMID: 35713859     DOI: 10.1007/978-1-0716-2305-3_4

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  17 in total

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Review 2.  Automated protein function prediction--the genomic challenge.

Authors:  Iddo Friedberg
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3.  Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

Authors:  Yingwen Zhao; Guangyuan Fu; Jun Wang; Maozu Guo; Guoxian Yu
Journal:  Genomics       Date:  2018-02-23       Impact factor: 5.736

4.  Text-mining approach to evaluate terms for ontology development.

Authors:  Lam C Tsoi; Ravi Patel; Wenle Zhao; W Jim Zheng
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5.  The open biomedical annotator.

Authors:  Clement Jonquet; Nigam H Shah; Mark A Musen
Journal:  Summit Transl Bioinform       Date:  2009-03-01

6.  Text-mining and information-retrieval services for molecular biology.

Authors:  Martin Krallinger; Alfonso Valencia
Journal:  Genome Biol       Date:  2005-06-28       Impact factor: 13.583

7.  SNVHMM: predicting single nucleotide variants from next generation sequencing.

Authors:  Jiawen Bian; Chenglin Liu; Hongyan Wang; Jing Xing; Priyanka Kachroo; Xiaobo Zhou
Journal:  BMC Bioinformatics       Date:  2013-07-15       Impact factor: 3.169

8.  Annotating the Function of the Human Genome with Gene Ontology and Disease Ontology.

Authors:  Yang Hu; Wenyang Zhou; Jun Ren; Lixiang Dong; Yadong Wang; Shuilin Jin; Liang Cheng
Journal:  Biomed Res Int       Date:  2016-08-22       Impact factor: 3.411

9.  Gene function prediction based on the Gene Ontology hierarchical structure.

Authors:  Liangxi Cheng; Hongfei Lin; Yuncui Hu; Jian Wang; Zhihao Yang
Journal:  PLoS One       Date:  2014-09-05       Impact factor: 3.240

10.  A data and text mining pipeline to annotate human mitochondrial variants with functional and clinical information.

Authors:  Ornella Vitale; Roberto Preste; Donato Palmisano; Marcella Attimonelli
Journal:  Mol Genet Genomic Med       Date:  2019-12-10       Impact factor: 2.183

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