International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

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 Volume 6, Issue 10 (October 2019), Pages: 62-72

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 Original Research Paper

 Title: A supervised classifier based chemoinformatics model to predict inhibitors essential for sexual reproduction and transmission of the P. falciparum parasite into mosquitoes

 Author(s): Asif Hassan Syed *, Tabrej Khan

 Affiliation(s):

 Department of Computer Science, Faculty of Computing and Information Technology Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi Arabia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-7288-3098

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2019.10.011

 Abstract:

The falciparum malaria is a significant life-threatening disease caused by Plasmodium falciparum a protozoan parasite transmitted by the female Anopheles mosquito. The resistance of P. falciparum parasite to a limited class of antimalarial medicine has accelerated the process of screening a novel drug for falciparum malaria. In recent years the implementation of Machine Learning (ML) approaches to build a predictive model to facilitate the target-specific drug discovery process for both infectious and non-infectious pathogen has gained significance. The availability of High-throughput Screening (HTS) anti-malarial bioassay dataset has provided an opportunity to build ML-based chemoinformatics, predictive models, using features extracted from different Feature Selection (FS) algorithms. In the present study, a combination of feature selection algorithms namely Greedy Stepwise algorithm in association with CfsSubsetEval and Principal Components Analysis (PCA) in conjunction with Ranker method was used on the HTS dataset. The dataset comprising of P. Falciparum Calcium-Dependent Protein Kinase4 (PfCDPK4) inhibitors and non-inhibitors were used to train and build four state-of-art classifiers based model for predicting inhibitors of PfCDPK4 protein from an independent test dataset accurately. The classification models were evaluated based on specific statistical measures of the Weka software tool. The J48 classifier based predictive model was found to accurately predict active anti-PfCDPK4 molecule based on better Accuracy, Recall, Precision, and Area under the Curve (AUC) values. Thus, the authors conclude that the J48-based classification model will be efficient and cost-effective in screening future active anti-CDPK4 molecule against P. falciparum malaria parasite. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Falciparum malaria, High-throughput screening dataset, Supervised learning based model, Precision, Recall

 Article History: Received 20 April 2019, Received in revised form 4 August 2019, Accepted 5 August 2019

 Acknowledgement:

The Authors are grateful to the Dean of Faculty of Computing and Information Technology, King Abdulaziz University to provide an excellent platform for conducting various machine learning experiments.

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

 Syed AH and Khan T (2019). A supervised classifier based chemoinformatics model to predict inhibitors essential for sexual reproduction and transmission of the P. falciparum parasite into mosquitoes. International Journal of Advanced and Applied Sciences, 6(10): 62-72

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 

 Tables

 Table 1 Table 2 Table 3

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