Proteomics, the study of proteins and their functions within biological systems, has become increasingly data-intensive, presenting both opportunities and challenges. This project addresses the need for advanced data analytics and data integrity in proteomics research. Leveraging the power of machine learning (ML) and blockchain technology, this attempt aims to transform proteomics research. This work encompasses three key objectives. First, collect, clean, and integrate proteomics data from diverse sources, ensuring data quality and consistency. Second, employ ML algorithms to analyze this data, revealing crucial insights, identifying proteins, and predicting their functions. Third, implement blockchain technology to safeguard the authenticity and integrity of the proteomics data, providing an auditable and tamper-proof record. Implemented a user-friendly web interface, facilitating collaboration among researchers and scientists by granting access to shared data and results. This study included various classification methods for the investigation of protein classification, namely, random forests, logistic regression, neural networks, support vector machines, and decision trees. In conclusion, the proposed work is poised to revolutionize proteomics research by enhancing data analytics capabilities and securing data integrity, thereby enabling scientists to make more informed and confident discoveries in this critical field.
| Published in | American Journal of Artificial Intelligence (Volume 8, Issue 1) |
| DOI | 10.11648/j.ajai.20240801.13 |
| Page(s) | 13-21 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Proteomics, Computational Biology, Bioinformatics, Machine Learning, Blockchain
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APA Style
Ramanaiah, P. K. (2024). Proteomics Data Classification Using Advanced Machine Learning Algorithm. American Journal of Artificial Intelligence, 8(1), 13-21. https://doi.org/10.11648/j.ajai.20240801.13
ACS Style
Ramanaiah, P. K. Proteomics Data Classification Using Advanced Machine Learning Algorithm. Am. J. Artif. Intell. 2024, 8(1), 13-21. doi: 10.11648/j.ajai.20240801.13
AMA Style
Ramanaiah PK. Proteomics Data Classification Using Advanced Machine Learning Algorithm. Am J Artif Intell. 2024;8(1):13-21. doi: 10.11648/j.ajai.20240801.13
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Download@article{10.11648/j.ajai.20240801.13,
author = {Preethi Kolluru Ramanaiah},
title = {Proteomics Data Classification Using Advanced Machine Learning Algorithm
},
journal = {American Journal of Artificial Intelligence},
volume = {8},
number = {1},
pages = {13-21},
doi = {10.11648/j.ajai.20240801.13},
url = {https://doi.org/10.11648/j.ajai.20240801.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20240801.13},
abstract = {Proteomics, the study of proteins and their functions within biological systems, has become increasingly data-intensive, presenting both opportunities and challenges. This project addresses the need for advanced data analytics and data integrity in proteomics research. Leveraging the power of machine learning (ML) and blockchain technology, this attempt aims to transform proteomics research. This work encompasses three key objectives. First, collect, clean, and integrate proteomics data from diverse sources, ensuring data quality and consistency. Second, employ ML algorithms to analyze this data, revealing crucial insights, identifying proteins, and predicting their functions. Third, implement blockchain technology to safeguard the authenticity and integrity of the proteomics data, providing an auditable and tamper-proof record. Implemented a user-friendly web interface, facilitating collaboration among researchers and scientists by granting access to shared data and results. This study included various classification methods for the investigation of protein classification, namely, random forests, logistic regression, neural networks, support vector machines, and decision trees. In conclusion, the proposed work is poised to revolutionize proteomics research by enhancing data analytics capabilities and securing data integrity, thereby enabling scientists to make more informed and confident discoveries in this critical field.
},
year = {2024}
}
TY - JOUR T1 - Proteomics Data Classification Using Advanced Machine Learning Algorithm AU - Preethi Kolluru Ramanaiah Y1 - 2024/05/17 PY - 2024 N1 - https://doi.org/10.11648/j.ajai.20240801.13 DO - 10.11648/j.ajai.20240801.13 T2 - American Journal of Artificial Intelligence JF - American Journal of Artificial Intelligence JO - American Journal of Artificial Intelligence SP - 13 EP - 21 PB - Science Publishing Group SN - 2639-9733 UR - https://doi.org/10.11648/j.ajai.20240801.13 AB - Proteomics, the study of proteins and their functions within biological systems, has become increasingly data-intensive, presenting both opportunities and challenges. This project addresses the need for advanced data analytics and data integrity in proteomics research. Leveraging the power of machine learning (ML) and blockchain technology, this attempt aims to transform proteomics research. This work encompasses three key objectives. First, collect, clean, and integrate proteomics data from diverse sources, ensuring data quality and consistency. Second, employ ML algorithms to analyze this data, revealing crucial insights, identifying proteins, and predicting their functions. Third, implement blockchain technology to safeguard the authenticity and integrity of the proteomics data, providing an auditable and tamper-proof record. Implemented a user-friendly web interface, facilitating collaboration among researchers and scientists by granting access to shared data and results. This study included various classification methods for the investigation of protein classification, namely, random forests, logistic regression, neural networks, support vector machines, and decision trees. In conclusion, the proposed work is poised to revolutionize proteomics research by enhancing data analytics capabilities and securing data integrity, thereby enabling scientists to make more informed and confident discoveries in this critical field. VL - 8 IS - 1 ER -
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