Genetic Diagnosis, Classification, and Risk Prediction in Cancer Using Next-Generation Sequencing in Oncology

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dc.contributor.authorDauda, K. A.
dc.contributor.authorOlorede, K. O.
dc.contributor.authorBanjoko, A. W.
dc.contributor.authorYahya, W. B.
dc.contributor.authorAyipo, Y. O.
dc.date.accessioned2025-06-02T14:20:00Z
dc.date.available2025-06-02T14:20:00Z
dc.date.issued2024
dc.description.abstractIn recent years, researchers have been overwhelmed with large-scale genomic data due to an advancement toward data-driven science called next-generation sequencing. Hence, it has been established that if the sequence of a gene is mutated, there is the possibility of unscheduled production of the protein, leading to cancer. The prediction of genes and variants into true and false pathogenic groups, extraction of the most influential genes, and identification of a pattern from this genomic data are of interest to biologists and medical professionals. However, the identification and extraction of these biomarkers and molecular signatures of different cancers among thousands of genes seem complex and problematic. Therefore, this chapter is aimed to develop an algorithm that can aid and provide efficient solutions for gene extraction, identification, and prediction in the realm of next-generation sequencing data. The proposed algorithm consists of three folds: the filtering fold using minimum redundancy maximum relevance (MRMR), the wrapper fold using the Boruta algorithm, and the last fold consisting of the use of deep learning (DL). A comparison assessment was performed on the proposed algorithm and the existing methods using five RNA-seq datasets from a cancer patient. The results revealed that the proposed algorithm significantly outperforms the existing methods in selecting fewer highly relevant genes for the cancer type while maintaining a high classification prediction accuracy.
dc.identifier.citationDauda, K. A., Olorede, K. O., Banjoko, A. W., Yahya, W. B., & Ayipo, Y. O. (2024). Genetic Diagnosis, Classification, and Risk Prediction in Cancer Using Next-Generation Sequencing in Oncology. In Computational Approaches in Biomaterials and Biomedical Engineering Applications. Pranav Deepak Pathak, Roshani Raut, Sebastian Jaramillo-Isaza, Pradnya Borkar, and Rutvij H. Jhaveri (ed.) 107-122. Boca Raton, London New Yor. Published by CRC Press, Taylor & Francis Group. ISBN: 978-1-032-63530-9 (set), 978-1-032-63525-5(hbk), 978-1-032-63527-9(pbk), 978-1-032-69988-2(ebk). DOI: 10.1201/9781032699882-5. URL: https://tinyurl.com/2mvphhn2
dc.identifier.isbn9781032699882
dc.identifier.urihttps://tinyurl.com/2mvphhn2
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/5471
dc.language.isoen
dc.publisherCRC Press, Taylor & Francis Grou
dc.titleGenetic Diagnosis, Classification, and Risk Prediction in Cancer Using Next-Generation Sequencing in Oncology
dc.title.alternativeIn Computational Approaches in Biomaterials and Biomedical Engineering Applications
dc.typeBook chapter
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