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Machine Learning Perspective in Cancer Research

Machine Learning Perspective in Cancer Research

Aman Sharma, Rinkle Rani
Copyright: © 2024 |Pages: 22
ISBN13: 9798369330265|EISBN13: 9798369330272
DOI: 10.4018/979-8-3693-3026-5.ch047
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MLA

Sharma, Aman, and Rinkle Rani. "Machine Learning Perspective in Cancer Research." Research Anthology on Bioinformatics, Genomics, and Computational Biology, edited by Information Resources Management Association, IGI Global, 2024, pp. 1104-1125. https://doi.org/10.4018/979-8-3693-3026-5.ch047

APA

Sharma, A. & Rani, R. (2024). Machine Learning Perspective in Cancer Research. In I. Management Association (Ed.), Research Anthology on Bioinformatics, Genomics, and Computational Biology (pp. 1104-1125). IGI Global. https://doi.org/10.4018/979-8-3693-3026-5.ch047

Chicago

Sharma, Aman, and Rinkle Rani. "Machine Learning Perspective in Cancer Research." In Research Anthology on Bioinformatics, Genomics, and Computational Biology, edited by Information Resources Management Association, 1104-1125. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-3026-5.ch047

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Abstract

Advancement in genome sequencing technology has empowered researchers to think beyond their imagination. Researchers are trying their hard to fight against various genetic diseases like cancer. Artificial intelligence has empowered research in the healthcare sector. Moreover, the availability of opensource healthcare datasets has motivated the researchers to develop applications which can help in early diagnosis and prognosis of diseases. Further, next-generation sequencing (NGS) has helped to look into detailed intricacies of biological systems. It has provided an efficient and cost-effective approach with higher accuracy. The advent of microRNAs also known as small noncoding genes has begun the paradigm shift in oncological research. We are now able to profile expression profiles of RNAs using RNA-seq data. microRNA profiling has helped in uncovering their relationship in various genetic and biological processes. Here in this chapter, the authors present a review of the machine learning perspective in cancer research.

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