Laplacian Likelihood-Based Generalized Additive Model for RNA-Seq Analysis of Oral Squamous Cell Carcinoma

The study's objective is to identify the non-linear relationship of differentially expressed genes that vary in terms of the tumour and normal tissue and correct for any variations among the RNA-Seq experiment focused on Oral squamous cell carcinoma samples from patients. A Laplacian Likelihood...

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Bibliographic Details
Published in:International journal of cognitive informatics & natural intelligence 2022-06, Vol.15 (4), p.1-19
Main Authors: Biju, Vinai George, C. M, Prashanth
Format: Article
Language:English
Subjects:
Cancer
Comparative analysis
Fertility
Generalized linear models
Genes
Independent variables
Informatics
Machine learning
Normal distribution
Oral carcinoma
Oral squamous cell carcinoma
Ribonucleic acid
RNA
RNA sequencing
Sequence analysis
Squamous cell carcinoma
Standard error
Variables
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Summary:The study's objective is to identify the non-linear relationship of differentially expressed genes that vary in terms of the tumour and normal tissue and correct for any variations among the RNA-Seq experiment focused on Oral squamous cell carcinoma samples from patients. A Laplacian Likelihood version of the Generalized Additive Model is proposed and compared with the regular GAM models in terms of the non-linear fitting. The Non-Linear machine learning approach of Laplacian Likelihood-based GAM could complement RNA-Seq Analysis mainly to interpret, validate, and prioritize the patient samples data of differentially expressed genes. The analysis eases the standard parametric presumption and helps discover complexity in the association between the dependent and the independent variable and parameter smoothing that might otherwise be neglected. Concurvity, standard error, deviance, and other statistical verification have been carried out to confirm Laplacian Likelihood-based GAM efficiency.
ISSN:1557-3958
1557-3966
DOI:10.4018/IJCINI.20211001.oa18