Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms
Virtual screening is the most critical process in drug discovery, and it relies on machine learning to facilitate the screening process. It enables the discovery of molecules that bind to a specific protein to form a drug. Despite its benefits, virtual screening generates enormous data and suffers f...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6675279 |
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| _version_ | 1832560093818781696 |
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| author | Sahar K. Hussin Salah M. Abdelmageid Adel Alkhalil Yasser M. Omar Mahmoud I. Marie Rabie A. Ramadan |
| author_facet | Sahar K. Hussin Salah M. Abdelmageid Adel Alkhalil Yasser M. Omar Mahmoud I. Marie Rabie A. Ramadan |
| author_sort | Sahar K. Hussin |
| collection | DOAJ |
| description | Virtual screening is the most critical process in drug discovery, and it relies on machine learning to facilitate the screening process. It enables the discovery of molecules that bind to a specific protein to form a drug. Despite its benefits, virtual screening generates enormous data and suffers from drawbacks such as high dimensions and imbalance. This paper tackles data imbalance and aims to improve virtual screening accuracy, especially for a minority dataset. For a dataset identified without considering the data’s imbalanced nature, most classification methods tend to have high predictive accuracy for the majority category. However, the accuracy was significantly poor for the minority category. The paper proposes a K-mean algorithm coupled with Synthetic Minority Oversampling Technique (SMOTE) to overcome the problem of imbalanced datasets. The proposed algorithm is named as KSMOTE. Using KSMOTE, minority data can be identified at high accuracy and can be detected at high precision. A large set of experiments were implemented on Apache Spark using numeric PaDEL and fingerprint descriptors. The proposed solution was compared to both no-sampling method and SMOTE on the same datasets. Experimental results showed that the proposed solution outperformed other methods. |
| format | Article |
| id | doaj-art-28849350eb54460288cac53db9348760 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-28849350eb54460288cac53db93487602025-02-03T01:28:23ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66752796675279Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning AlgorithmsSahar K. Hussin0Salah M. Abdelmageid1Adel Alkhalil2Yasser M. Omar3Mahmoud I. Marie4Rabie A. Ramadan5Communication and Computers Engineering Department Alshrouck Academy, Cairo, EgyptComputer Engineering Department, Collage of Comp. Science and Engineering, Taibah University, Medina, Saudi ArabiaCollege of Computer Science and Engineering, University of Hai’l, Hai’l, Saudi ArabiaArab Academy for Science Technology and Maritime Transport, Cairo, EgyptComputer and System Engineering Department, Al-Azhar University, Cairo, EgyptCollege of Computer Science and Engineering, University of Hai’l, Hai’l, Saudi ArabiaVirtual screening is the most critical process in drug discovery, and it relies on machine learning to facilitate the screening process. It enables the discovery of molecules that bind to a specific protein to form a drug. Despite its benefits, virtual screening generates enormous data and suffers from drawbacks such as high dimensions and imbalance. This paper tackles data imbalance and aims to improve virtual screening accuracy, especially for a minority dataset. For a dataset identified without considering the data’s imbalanced nature, most classification methods tend to have high predictive accuracy for the majority category. However, the accuracy was significantly poor for the minority category. The paper proposes a K-mean algorithm coupled with Synthetic Minority Oversampling Technique (SMOTE) to overcome the problem of imbalanced datasets. The proposed algorithm is named as KSMOTE. Using KSMOTE, minority data can be identified at high accuracy and can be detected at high precision. A large set of experiments were implemented on Apache Spark using numeric PaDEL and fingerprint descriptors. The proposed solution was compared to both no-sampling method and SMOTE on the same datasets. Experimental results showed that the proposed solution outperformed other methods.http://dx.doi.org/10.1155/2021/6675279 |
| spellingShingle | Sahar K. Hussin Salah M. Abdelmageid Adel Alkhalil Yasser M. Omar Mahmoud I. Marie Rabie A. Ramadan Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms Complexity |
| title | Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms |
| title_full | Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms |
| title_fullStr | Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms |
| title_full_unstemmed | Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms |
| title_short | Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms |
| title_sort | handling imbalance classification virtual screening big data using machine learning algorithms |
| url | http://dx.doi.org/10.1155/2021/6675279 |
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