Research Article, J Appl Bioinforma Comput Biol Vol: 6 Issue: 3
Automatic Categorization of PubMed microRNA Target Abstracts Based on Text Classification Techniques
Malik Yousef1*, Dawit Nigatu2 and Loai Abdalla3
1Community Information Systems, Zefat Academic College, Zefat, 13206, Israel
2Transmission Systems Group, Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany
3Management Information Systems, The Max Stern Yezreel Valley College, 19300, Israel
*Corresponding Author : Malik Yousef
Community Information Systems, Zefat Academic College, Zefat, 13206, Israel
Tel: +972 4-692-7777
E-mail: malik.yousef@gmail.com
Received: August 22, 2017 Accepted: October 10, 2017 Published: October 13, 2017
Citation: Yousef M, Nigatu D, Abdalla L (2017) Automatic Categorization of PubMed microRNA Target Abstracts Based on Text Classification Techniques. J Appl Bioinforma Comput Biol 6:3. doi: 10.4172/2329-9533.1000138
Abstract
This is a first study attempting to suggest an automatic categorization of microRNA articles from PubMed. In this study, text classification techniques using binary representations were applied on the abstract section of the articles. The PubMed articles related to microRNA targets were regarded as the positive class whereas documents retrieved using different criteria are used as a negative class. The results show that with a careful choice of the negative class, the PubMed articles about microRNA targets can be accurately distinguished. Moreover, we showed the robustness of the automatic text classification by building models not just from the top keywords but also from a combination of other keywords.
