A more appropriate Protein Classification using Data Mining

TL;DR

This paper introduces an improved data mining method for protein classification that overcomes limitations of existing techniques like PSIMAP, effectively classifying proteins with scale-free properties using a hierarchical tree structure.

Contribution

The paper presents a novel hierarchical data mining approach that accurately classifies all proteins, including scale-free property proteins, addressing shortcomings of previous methods.

Findings

Successfully classifies proteins with scale-free properties

Uses six key attributes for hierarchical grouping

Outperforms PSIMAP in comprehensive protein classification

Abstract

Research in bioinformatics is a complex phenomenon as it overlaps two knowledge domains, namely, biological and computer sciences. This paper has tried to introduce an efficient data mining approach for classifying proteins into some useful groups by representing them in hierarchy tree structure. There are several techniques used to classify proteins but most of them had few drawbacks on their grouping. Among them the most efficient grouping technique is used by PSIMAP. Even though PSIMAP (Protein Structural Interactome Map) technique was successful to incorporate most of the protein but it fails to classify the scale free property proteins. Our technique overcomes this drawback and successfully maps all the protein in different groups, including the scale free property proteins failed to group by PSIMAP. Our approach selects the six major attributes of protein: a) Structure comparison b) Sequence Comparison c) Connectivity d) Cluster Index e) Interactivity f) Taxonomic to group the protein from the databank by generating a hierarchal tree structure. The proposed approach calculates the degree (probability) of similarity of each protein newly entered in the system against of existing proteins in the system by using probability theorem on each six properties of proteins.