Genetic interactions effects for cancer disease identification using computational models: a review.
Genome-wide association studies (GWAS) provide clear insight into understanding genetic variations and environmental influences responsible for various human diseases. Cancer identification through genetic interactions (epistasis) is one of the significant ongoing researches in GWAS. The growth of t...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 4; pp. 733 - 759 |
|---|---|
| Autores principales: | , |
| Formato: | review Journal Article |
| Publicado: |
Springer Nature
Apr2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149884716&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149884716 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2021 vid: 59 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 149884716 149727395 149884716 NLM33839998 149884716 10.1007/s11517-021-02343-9 NLM33839998 149884716 ppf: 733 ppct: 26 formats: fmt: @attributes: type: P tig: atl: Genetic interactions effects for cancer disease identification using computational models: a review. aug: au: Manavalan, R. Priya, S. affil: Department of Computer Science, Arignar Anna Government Arts College, 605602, Villupuram, Tamil Nadu, India sug: subj: Genes Neoplasms Polymorphism, Genetic Data Mining Models, Biological Bioinformatics Sequence Analysis ab: Genome-wide association studies (GWAS) provide clear insight into understanding genetic variations and environmental influences responsible for various human diseases. Cancer identification through genetic interactions (epistasis) is one of the significant ongoing researches in GWAS. The growth of the cancer cell emerges from multi-locus as well as complex genetic interaction. It is impractical for the physician to detect cancer via manual examination of SNPs interaction. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2001 and 2020. In this contemporary review, various computational methods are as follows: multifactor dimensionality reduction-based approaches, statistical strategies, machine learning, and optimization-based techniques are carefully reviewed and presented with their evaluation results. Moreover, these computational approaches' strengths and limitations are described. The issues behind the computational methods for identifying the cancer disease through genetic interactions and the various evaluation parameters used by researchers have been analyzed. This review is highly beneficial for researchers and medical professionals to learn techniques adapted to discover the epistasis and aids to design novel automatic epistasis detection systems with strong robustness and maximum efficiency to address the different research problems in finding practical solutions effectively. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|