Chapter title |
Machine Learning Approaches Toward Building Predictive Models for Small Molecule Modulators of miRNA and Its Utility in Virtual Screening of Molecular Databases.
|
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Chapter number | 11 |
Book title |
Drug Target miRNA
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6563-2_11 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6561-8, 978-1-4939-6563-2
|
Authors |
Vinita Periwal, Vinod Scaria |
Editors |
Marco F. Schmidt |
Abstract |
The ubiquitous role of microRNAs (miRNAs) in a number of pathological processes has suggested that they could act as potential drug targets. RNA-binding small molecules offer an attractive means for modulating miRNA function. The availability of bioassay data sets for a variety of biological assays and molecules in public domain provides a new opportunity toward utilizing them to create models and further utilize them for in silico virtual screening approaches to prioritize or assign potential functions for small molecules. Here, we describe a computational strategy based on machine learning for creation of predictive models from high-throughput biological screens for virtual screening of small molecules with the potential to inhibit microRNAs. Such models could be potentially used for computational prioritization of small molecules before performing high-throughput biological assay. |
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Demographic breakdown
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Researcher | 3 | 18% |
Student > Postgraduate | 2 | 12% |
Student > Bachelor | 2 | 12% |
Lecturer > Senior Lecturer | 1 | 6% |
Other | 1 | 6% |
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Unknown | 6 | 35% |
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Other | 0 | 0% |
Unknown | 10 | 59% |