DRABAL: novel method to mine large high-throughput screening assays using Bayesian active learning
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ArticleAuthors
Soufan, Othman
Ba Alawi, Wail

Afeef, Moataz A.
Essack, Magbubah

Kalnis, Panos

Bajic, Vladimir B.

KAUST Department
Computational Bioscience Research Center (CBRC)Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Bioscience Program
Applied Mathematics and Computational Science Program
KAUST Grant Number
URF/1/1976-02Date
2016-11-10Online Publication Date
2016-11-10Print Publication Date
2016-12Permanent link to this record
http://hdl.handle.net/10754/621869
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Show full item recordAbstract
Background Mining high-throughput screening (HTS) assays is key for enhancing decisions in the area of drug repositioning and drug discovery. However, many challenges are encountered in the process of developing suitable and accurate methods for extracting useful information from these assays. Virtual screening and a wide variety of databases, methods and solutions proposed to-date, did not completely overcome these challenges. This study is based on a multi-label classification (MLC) technique for modeling correlations between several HTS assays, meaning that a single prediction represents a subset of assigned correlated labels instead of one label. Thus, the devised method provides an increased probability for more accurate predictions of compounds that were not tested in particular assays. Results Here we present DRABAL, a novel MLC solution that incorporates structure learning of a Bayesian network as a step to model dependency between the HTS assays. In this study, DRABAL was used to process more than 1.4 million interactions of over 400,000 compounds and analyze the existing relationships between five large HTS assays from the PubChem BioAssay Database. Compared to different MLC methods, DRABAL significantly improves the F1Score by about 22%, on average. We further illustrated usefulness and utility of DRABAL through screening FDA approved drugs and reported ones that have a high probability to interact with several targets, thus enabling drug-multi-target repositioning. Specifically DRABAL suggests the Thiabendazole drug as a common activator of the NCP1 and Rab-9A proteins, both of which are designed to identify treatment modalities for the Niemann–Pick type C disease. Conclusion We developed a novel MLC solution based on a Bayesian active learning framework to overcome the challenge of lacking fully labeled training data and exploit actual dependencies between the HTS assays. The solution is motivated by the need to model dependencies between existing experimental confirmatory HTS assays and improve prediction performance. We have pursued extensive experiments over several HTS assays and have shown the advantages of DRABAL. The datasets and programs can be downloaded from https://figshare.com/articles/DRABAL/3309562.Citation
Soufan O, Ba-Alawi W, Afeef M, Essack M, Kalnis P, et al. (2016) DRABAL: novel method to mine large high-throughput screening assays using Bayesian active learning. Journal of Cheminformatics 8. Available: http://dx.doi.org/10.1186/s13321-016-0177-8.Sponsors
Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) and KAUST Office of Sponsored Research (OSR) under Award No. URF/1/1976-02. The computational analysis for this study was performed on the Dragon and Snapdragon compute clusters of the Computational Bioscience Research Center at KAUST.Publisher
Springer NatureJournal
Journal of CheminformaticsAdditional Links
http://jcheminf.springeropen.com/articles/10.1186/s13321-016-0177-8Relations
Is Supplemented By:- [Dataset]
Soufan, O., Ba-Alawi, W., Moataz Afeef, Magbubah Essack, Kalnis, P., & Bajic, V. (2016). DRABAL: novel method to mine large high-throughput screening assays using Bayesian active learning. Figshare. https://doi.org/10.6084/m9.figshare.c.3696499. DOI: 10.6084/m9.figshare.c.3696499 HANDLE: 10754/624144
ae974a485f413a2113503eed53cd6c53
10.1186/s13321-016-0177-8
Scopus Count
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