Genetic interactions effects for cancer disease identification using computational models: a review.

Szczegóły
Abstrakt

Tytuł:: Genetic interactions effects for cancer disease identification using computational models: a review.
Autorzy:: Manavalan R; Department of Computer Science, Arignar Anna Government Arts College, Villupuram, Tamil Nadu, 605602, India. manavalan_.
Priya S; Computer Science, Arignar Anna Government Arts College, Villupuram, Tamil Nadu, India.
Źródło:: Medical & biological engineering & computing [Med Biol Eng Comput] 2021 Apr; Vol. 59 (4), pp. 733-758. Date of Electronic Publication: 2021 Apr 11.
Typ publikacji:: Journal Article; Review
Język:: English
Imprint Name(s):: Publication: New York, NY : Springer
Original Publication: Stevenage, Eng., Peregrinus.
MeSH Terms:: Epistasis, Genetic*
Neoplasms*/genetics
Computational Biology ; Genome-Wide Association Study ; Humans ; Models, Genetic ; Multifactor Dimensionality Reduction ; Polymorphism, Single Nucleotide
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Contributed Indexing:: Keywords: Cancer; Computational model; Epistasis; Gene; Genetic interactions; SNPs
Entry Date(s):: Date Created: 20210411 Date Completed: 20210929 Latest Revision: 20210929
Update Code:: 20240105
DOI:: 10.1007/s11517-021-02343-9
PMID:: 33839998
: Czasopismo naukowe

Pełny tekst

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.

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