Research Article | | Peer-Reviewed

Proteomics Data Classification Using Advanced Machine Learning Algorithm

Received: 21 April 2024     Accepted: 3 May 2024     Published: 17 May 2024
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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.

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

Keywords

Proteomics, Computational Biology, Bioinformatics, Machine Learning, Blockchain

References
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[5] Kotlyar et al., 2014. Kotlyar, M., Pastrello, C., Pivetta, F., Sardo, A. L., Cumbaa, C., Li, H., Naranian, T., Niu, Y., Ding, Z., Vafaee, F., et al. (2014). In silico prediction of physical protein interactions and characterization of interactome orphans. Nature methods, 12(1): 79
[6] Rentzsch and Orengo, 2013. Rentzsch, R. and Orengo, C. A. (2013). Protein function prediction using domain families. In BMC bioinformatics, volume 14, page S5. BioMed Central.
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[8] Singh and Tripathi, 2016. Singh, U. and Tripathi, S. (2016). Protein classification using hybrid feature selection technique. In International Conference on Smart Trends for Information Technology and Computer Communications, pages 813–821. Springer. ISBN: 978-981-10-3432-9.
[9] Zhang, Y., Li, X., & Wang, Y. (2023). Proteomics data analysis using machine learning on AWS. Bioinformatics, 40(10), 1839-1846.
[10] Goodfellow I.; Pouget-Abadie J.; Mirza M.; Xu B.; Warde-Farley D.; Ozair S.; Courville A.; Bengio Y. (2020). Generative adversarial networks. Communications of the ACM 2020, 63 (11), 139–144.
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[12] Agarwal, Ankita & Singh, Kunal & Kaushik, Shri Kant & Bahadur, Ranjit. (2022). A comparative analysis of machine learning classifiers for predicting protein-binding nucleotides in RNA sequences. Computational and Structural Biotechnology Journal. 20.
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Cite This Article
  • 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

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    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

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    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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  • @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}
    }
    

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  • 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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