Reviewing the Methods of Predicting Protein Secondary Structure
Shivani Agarwal, Rishabh Kaushik and Atul Kumar. Article: Reviewing the Methods of Predicting Protein Secondary Structure. IJCA Proceedings on National Seminar on Future Trends and Innovations in Computer Engineering NSFTICE 2015(1):25-28, August 2016. Full text available. BibTeX
@article{key:article,
author = {Shivani Agarwal and Rishabh Kaushik and Atul Kumar},
title = {Article: Reviewing the Methods of Predicting Protein Secondary Structure},
journal = {IJCA Proceedings on National Seminar on Future Trends and Innovations in Computer Engineering},
year = {2016},
volume = {NSFTICE 2015},
number = {1},
pages = {25-28},
month = {August},
note = {Full text available}
}
Abstract
Not just will a good understanding of the protein structure assist in deciphering the biochemical mechanisms of proteins which would in turn result in diagnosing deficiencies, diseases, but also in treating them by giving humans the ability to create very explicit drugs targeted to perform a specific function. It is one of the key problems of molecular biology in this century. Predicting the protein secondary structure is an important step in this direction, as the structure of a protein is amalgamated with its function and characteristics. Although, this problem of predicting the protein structure with very high accuracy still lies unsolved even after decades of tedious research. But with the advancements in machine learning in the recent years have provided us with new tools that offer a ray of hope to tackle this problem. This review paper brings to the light the advancements in machine learning to predict the protein secondary structure.
References
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