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Published Paper Details:
Neural network prediction of genome wide transcriptome signatures in Adenocarcinoma
Authors
Sudheer Menon
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Keywords
Microarray, iGenSig, lncRNAs, Kernel Approach, p53 genes, LS-SVM, SVM, Tumor
Abstract
The microarray method is used to investigate the transcriptomic formation of a tumor and the transcriptome does not tell biology because of transcription and translational post-modifications, alternative splicing, and the effects of pathological conditions. Due to this reason fusing more than one source of genome-wide data like, transcriptome, proteome, epigenome, and genome become important. A diagnostic approach includes information about the transcriptional status of all genes across multiple tissue types in cancer diagnosis. It is identified that two p53-regulated long non-coding RNAs (lncRNAs) are required for the effective binding of p53 with some of its target genes. There are 18 high-confidence lncRNAs that are transcriptional targets and they are all identified with the help of p53 ChIP-sequence analysis. It is also demonstrated that expression of p53-lncRNAs is lower in colorectal cancer samples having a tumor suppressor with very high diagnostic power.
Multi-omics sequence is becoming the clinical routine and transformation of precision oncology and it is also low-cost. Transparent and interpretable computational methods have been proposed which are called integral genomic signature analysis. With the help of a genomic dataset of chemical disruption, a battery of integral genomic signature analysis (iGenSig) models for the prediction of cancer drug response was developed. iGenSig gives a computational framework for the facilitation to adapt cancer therapy based upon multi-omics data. Understanding cancer requires the discovery of cancer driver mutations that confer a proliferative advantage; however, due to the difficulty of modeling the somatic mutation rates seen across tumor genomes, searches have frequently been restricted to protein-coding sequences and particular noncoding elements (for example, promoters).
IJCRT's Publication Details
Unique Identification Number - IJCRT2412182
Paper ID - 273555
Author type - Indian Author
Page Number(s) - b716-b726
Pubished in - Volume 12 | Issue 12 | December 2024
DOI (Digital Object Identifier) -
No Of Downloads - 156
Author Country - India, 678593, palakkad, palakkad, 678593, Life Sciences All
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Sudheer Menon, "Neural network prediction of genome wide transcriptome signatures in Adenocarcinoma", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 12, pp.b716-b726, December 2024, Available at :http://www.ijcrt.org/papers/IJCRT2412182.pdf
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