International Journal of Computer
Trends and Technology

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Volume 34 | Number 1 | Year 2016 | Article Id. IJCTT-V34P125 | DOI : https://doi.org/10.14445/22312803/IJCTT-V34P125

Harnessing Power of Decision Tree Approach for HPF Prediction using SIPINA and See5

Sunny Sharma, Amritpal Singh, Dr. Rajinder Singh

Citation :

Sunny Sharma, Amritpal Singh, Dr. Rajinder Singh, "Harnessing Power of Decision Tree Approach for HPF Prediction using SIPINA and See5," International Journal of Computer Trends and Technology (IJCTT), vol. 34, no. 1, pp. 139-143, 2016. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V34P125

Abstract

Drug discovery process, Disease detection and Prediction of molecular class are the area of great significance for carrying out research. In past few decades some precise approaches were used to enhance the accuracy of Human protein Function (HPF) prediction. This research study is primarily concentrated on such approach of HPF prediction with sequence derived features (SDF) using decision trees and there variants implemented using C5 and C4.5 algorithms like See5 and SIPINA. More sequence derived features were identified and incorporated. The training data was improved with these incorporated features. The Sequence data was evolved from HPRD (Human protein reference database) in terms of number of sequences and the features used to extract the relation towards a specific class which enhancing power of training data. Multiple techniques were examined for accuracy in prediction and a widespread comparison was done amongst them incorporating with previous research results, and prescribed the overall accuracy of See5 with 64% and SIPINA with 88%.

Keywords

HPF, C5, C4.5, See5, Decision Tree, SDF, SIPINA.

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