The Potential Uses of Data Science and Deep Learning Techniques in Mining Biological Data: A Comprehensive Analysis

An in-depth study of data science and Deep Learning (DL) concepts is provided, with a focus on which DL component is required for a more refined prognosis. Additionally, this research provides a thorough connection of several DL strategies used in the Biomedical Sector (BS). A bright prospect in dev...

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Bibliographic Details
Published in2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) pp. 755 - 759
Main Authors Praveena, Murala, Sivaraman, K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.12.2023
Subjects
Benchmark testing
Biological Science
Data science
Deep learning
Deoxyribonucleic acid Analysis
Industries
Medical imaging
Molecular Biology
neuro-science
Prognostics and health management
Soft sensors
Systematics
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Summary:An in-depth study of data science and Deep Learning (DL) concepts is provided, with a focus on which DL component is required for a more refined prognosis. Additionally, this research provides a thorough connection of several DL strategies used in the Biomedical Sector (BS). A bright prospect in developing remedies to the complex problems in the BS, which stayed previously independent of the use of systematic strategies or mathematical modeling, is observed due to the emergence of prevailing computing devices, sophisticated DL techniques, and wide compilation of data from divergent tools of industry. Each specific inside the log data as well as each piece of data connected to the information being targeted might be included by DL methods. Despite their limitations, they continue to be unrestricted due to AS's restricting assumptions or NS's specialized data and/or energy computation demands. This thorough study can serve as a benchmark for DL solutions in this industry. Based on the analysis conducted, it has been found that DL techniques have a significant impact on addressing problems in the three main BS disciplines of forecasting, categorization, and segmentation.
DOI:10.1109/ICIMIA60377.2023.10426401