Open AccessProtein Structure Classification Based on Distance Feature
Author(s) -
Sheshang Degadwala,
Dhairya Vyas,
Harsh Dave
Publication year - 2020
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit206464
Subject(s) - artificial intelligence , random forest , computer science , naive bayes classifier , decision tree , artificial neural network , classifier (uml) , protein structure prediction , machine learning , support vector machine , feature vector , tree structure , feature (linguistics) , protein structure , pattern recognition (psychology) , data structure , biology , biochemistry , programming language , linguistics , philosophy
In Bioinformatics field Protein Structure Classification is the hugest undertaking. The realized proteins have been requested subject to their level, feature, work, amino destructive and family and superfamily. Protein structure segregated into four sorts: all ? protein structure, all ? protein structure, ?+? protein structure, and ?/? protein structure. The use of a standard way to deal with perform plan is a very inconvenient and dreary task. The quantity of cutting edge Machine Intelligence enrolling strategies such Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree and Naïve Bayes Classifier had been proposed in the composition. Our objective right currently is to develop a system that performs better than anything past markers for protein structure gathering by thinking about the separation among the distinctive Amino Acid buildups. To take a gander at the display of proposed work particular datasets are used.