ProteinA: An Approach for Analyzing and Visualizing Protein Conformational Transitions Using Fuzzy and Hard Clustering Techniques

摘要

It is not easy finding arguments against the common belief that Proteomics and Genomics are the most challenging and important research fields, posing interesting problems for our current era. Gaining insight into the protein folding process has been the goal of many in the past few decades. Understanding completely how proteins come alive and behave will revolutionize modern medicine. With the main goal of understanding the importance of the protein folding problem and uncovering hidden patterns in protein data, we are analyzing protein conformational transitions with unsupervised learning tools, by applying different types of hard and fuzzy clustering algorithms and comparing the results. As an additional goal, the paper describes a software that can perform on demand analysis on protein data and display the results in a web interface. It is a proof of concept for potential useful features that make software algorithms available for researchers of all domains.