DESIGN AND IMPLEMENTATION OF A LARGE SCALE DEEP LEARNING APPLICATION FOR PROTEIN STRUCTURE PREDICTION AND ANALYSIS

DESIGN AND IMPLEMENTATION OF A LARGE SCALE DEEP LEARNING APPLICATION FOR PROTEIN STRUCTURE PREDICTION AND ANALYSIS

Abstract

Proteins are such a vital piece of the scientific community’s understanding of molecular interactions in organisms, which is why the accurate analysis of proteins are such a pivotal piece of drug and toxin design. In this research we introduce the foundation of a software suite of interconnected novel tools that use machine learning and deep learning tools. These tools will provide not only insights and connected patterns about the proteins that they study, but also a new data set of information that can be used in future research. This tool would enable analysis from several different inputs, such as: the primary protein structure, FASTA, DSSP, or PDB files. Also, in this research we introduce a database of results that would use similar analysis techniques as social media analysis tools to attempt to analyze the interconnected nature of proteins in a meaningful way. This research is not intended to be considered a final product but a foundation and proof of concept. The favorable results found by the initial tools being used as inspiration along with a clear path of the future to build a more complete tool with the goals of one day being made publicly so the information can grow to its full potential.

DESIGN AND IMPLEMENTATION OF A LARGE SCALE DEEP LEARNING APPLICATION FOR PROTEIN STRUCTURE PREDICTION AND ANALYSIS. GET MORE COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS

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