Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design
© 2020 The Author(s) A machine learning algorithm that also reports its certainty about a prediction can help a researcher design new experiments. Algorithms called Gaussian processes trained with modern data can make accurate predictions with informative uncertainty. We leverage this approach to fi...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV
2021
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| Online Access: | https://hdl.handle.net/1721.1/136149 |
| _version_ | 1826217807709208576 |
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| author | Hie, Brian Bryson, Bryan D Berger, Bonnie |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Hie, Brian Bryson, Bryan D Berger, Bonnie |
| author_sort | Hie, Brian |
| collection | MIT |
| description | © 2020 The Author(s) A machine learning algorithm that also reports its certainty about a prediction can help a researcher design new experiments. Algorithms called Gaussian processes trained with modern data can make accurate predictions with informative uncertainty. We leverage this approach to find nanomolar kinase binders, Mycobacterium tuberculosis inhibitors, mutations that enhance protein fluorescence, and genes important for cell development. |
| first_indexed | 2024-09-23T17:09:28Z |
| format | Article |
| id | mit-1721.1/136149 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T17:09:28Z |
| publishDate | 2021 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1361492023-01-10T19:48:17Z Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design Hie, Brian Bryson, Bryan D Berger, Bonnie Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Biological Engineering Ragon Institute of MGH, MIT and Harvard Massachusetts Institute of Technology. Department of Mathematics © 2020 The Author(s) A machine learning algorithm that also reports its certainty about a prediction can help a researcher design new experiments. Algorithms called Gaussian processes trained with modern data can make accurate predictions with informative uncertainty. We leverage this approach to find nanomolar kinase binders, Mycobacterium tuberculosis inhibitors, mutations that enhance protein fluorescence, and genes important for cell development. 2021-10-27T20:31:05Z 2021-10-27T20:31:05Z 2020 2021-08-25T17:12:01Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136149 en 10.1016/J.CELS.2020.09.007 Cell Systems Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Elsevier |
| spellingShingle | Hie, Brian Bryson, Bryan D Berger, Bonnie Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design |
| title | Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design |
| title_full | Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design |
| title_fullStr | Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design |
| title_full_unstemmed | Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design |
| title_short | Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design |
| title_sort | leveraging uncertainty in machine learning accelerates biological discovery and design |
| url | https://hdl.handle.net/1721.1/136149 |
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