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2023 Volume 14 Issue 1

Applications of Deep Learning and Machine Learning in Computational Medicine


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Abstract

Computational medicine has emerged due to the advances in medical technology in parallel with big data and artificial intelligence. A new way of treating complex diseases is evolving called ‘Precision Medicine’ fueled by big data extracting meaningful information from individual variability. At the forefront is biomedical research aiming to promote the area of precision medicine. Though traditional machine learning methods have built successful models for cancer diagnosis to sars-cov2 pulmonary infection, the advent of modern deep learning methods has had phenomenal growth in genomics, electronic health records, and drug development. The challenges in Deep learning applications in medicine include lack of data, privacy, heterogeneity of data, and interpretability. Analysis and discussion on these problems provide a reference to improve the application of deep learning in medical health.


How to cite this article
Vancouver
Adiga R, Biswas T, Shyam P. Applications of Deep Learning and Machine Learning in Computational Medicine. J Biochem Technol. 2023;14(1):1-6. https://doi.org/10.51847/iW1DfVoXVw
APA
Adiga, R., Biswas, T., & Shyam, P. (2023). Applications of Deep Learning and Machine Learning in Computational Medicine. Journal of Biochemical Technology, 14(1), 1-6. https://doi.org/10.51847/iW1DfVoXVw
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JOURNAL OF BIOCHEMICAL TECHNOLOGY
JOURNAL OF BIOCHEMICAL TECHNOLOGY
Journal of Biochemical Technology is a double-blind peer reviewed International Journal published by the Deniz Publication on behalf of the Biochemical Technology Society, a Registered Charity Organization from India

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Welcome to the Journal of Biochemical Technology, a prestigious international double-blind peer-reviewed journal dedicated to advancing excellence in biochemical, biotechnological, and bioinformatics research with direct applications in biology and medicine.

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This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. Keywords include, Biochemical Research: Endo/exocytosis, Trafficking, Membrane Biology, Cell Migration, Cell-Matrix Organelle Biogenesis, Cytoskeleton Proteolysis, Cell Death, Cell Cycle, Cancer, Cell Growth/Death, Differentiation, Drug Targets, Gene Therapy, Models of Disease, Proteomics, Stem Cells, Bioenergetics, Mitochondria, Free Radicals, Redox Signaling, Ion Transport/Channels, Oxidative