Deep Learning and Protein Structures Abstract Deep ...

Deep Learning and Protein Structures Abstract Deep learning is an evolving field that helps in solving problems that deals with complex relationships. In machine learning it is a emerging area of research. It is able to solve problems that other classifiers are not capable enough to perform. Machine learning techniques are widely used and it is not new in bioinformatics and computational biology. Recently Deep Neural Networks is gaining importance and popularity in protein science in solving complex problems. Given its sequence determining the structure of a protein is a challenging problem. Deep learning takes advantage of both increasing data and power of computation. In data science both deep learning and big data are two high-focus. It is a valuable tool in big data analytics that helps in the analysis and learning of huge amounts of unsupervised data. With increasing protein data and improving deep learning architecture it will be able to solve problems related to protein structure prediction. In ab initio predictions machine learning approaches have proven themselves to be effective. After the success of Artificial Neural Networks, deep learning is gaining importance in solving the important and fundamental problem i.e. protein structure prediction. X-ray crystallography, NMR spectroscopy and electron microscopy and other experimental methods have been employed for protein structure determination. Both the cost and time involved in performing these methods have resulted in a gap leading to significantly smaller number of protein structures when compared with the number of known sequences. Hence there is a need for computational approaches that not only brings down the gap and understanding the protein sequence structure relationship but also can help in the advancements of protein-based biotechnology and drug discovery.

Sridhar Hariharaputran 18. May. 2017