October 2024
Intermediate to advanced
560 pages
14h 37m
English
book
Genomics at the Nexus of AI, Computer Vision, and Machine Learning
by Shilpa Choudhary, Sandeep Kumar, Swathi Gowroju, Monali Gulhane, R. Sri Lakshmi
Content preview from Genomics at the Nexus of AI, Computer Vision, and Machine Learning
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8Optimizing TCGA Data Analysis: Unveiling Crucial Cancer-Related Gene Alterations Through a Fusion Approach QL Gradient
Sushma Chowdary Polavarapu1, Sri Hari Nallamala2*, Sudheer Mangalampalli3, Brahma Naidu Nalluri2, Lalitha Rajeswari Burra2 and Swarna Lalitha Chukka2
1Department of EIE, V.R. Siddhartha Engineering College (Autonomous), Vijayawada, Krishna Dist., Andhra Pradesh, India
2Department of CSE, Vasireddy Venkatadri Institute of Technology (Autonomous), Nambur, Guntur, Andhra Pradesh, India
3School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
Abstract
Analyzing The Cancer Genome Atlas (TCGA) data helps researchers identify genes frequently mutated or altered in cancer. These alterations may play a crucial role in the development and progression of cancer. The Cancer Genome Atlas (TCGA) data allows classifying cancers into molecular subtypes based on their genomic profiles. By identifying specific genetic alterations in cancer cells, researchers can explore targeted therapies that aim to inhibit the activity of these altered genes. Early systems used popular tools such as edgeR and DESeq2 for genetic analysis, but these tools are sensitive to the experimental design, and incorrect model specification can lead to biased results. In the latest research, people have applied clustering algorithms to group samples based on their gene expression patterns. Different clustering algorithms have various parameters that need to be ...
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