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Publication Type : Book Chapter
Publisher : Springer Netherlands
Source : Lecture Notes in Computational Vision and Biomechanics, Springer Netherlands, Volume 31, p.261-269 (2019)
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2019
Abstract : Understanding the meaning of protein sequences is tedious with human efforts alone. Through this work, we experiment an NLP technique to extract features and give appropriate representation for the protein sequences. In this paper, we have used GloVe representation for the same. A dataset named Swiss-Prot has been incorporated into this work. We were able to create a representation that has comparable ability to understand the semantics of protein sequences compared to the existing ones. We have analyzed the performance of representation by the classification of different protein families in the Swiss-Prot dataset using machine learning technique. The analysis done by us proved the significance of this representation. © Springer Nature Switzerland AG 2019.
Cite this Research Publication : A. George, Ganesh, H. B. Barathi, M. Kumar, A., and Dr. Soman K. P., “Significance of global vectors representation in protein sequences analysis”, in Lecture Notes in Computational Vision and Biomechanics, vol. 31, Springer Netherlands, 2019, pp. 261-269.