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Data science, human intelligence, and therapeutics discovery: An interview with Sean Escola, Saul Kato, and Pavan Ramkumar
Sean Escola, Saul Kato, and Pavan Ramkumar explain the importance of data science in their research. They have developed a simple non-parametric statistical method called the Rank-to-Group (RTG) score that identifies hierarchical confounder effects in raw data and machine learning-derived data embed...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023890/ https://www.ncbi.nlm.nih.gov/pubmed/35465229 http://dx.doi.org/10.1016/j.patter.2022.100490 |
_version_ | 1784690439985561600 |
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author | Ramkumar, Pavan Kato, Saul Escola, G. Sean |
author_facet | Ramkumar, Pavan Kato, Saul Escola, G. Sean |
author_sort | Ramkumar, Pavan |
collection | PubMed |
description | Sean Escola, Saul Kato, and Pavan Ramkumar explain the importance of data science in their research. They have developed a simple non-parametric statistical method called the Rank-to-Group (RTG) score that identifies hierarchical confounder effects in raw data and machine learning-derived data embeddings. This approach should be generally useful in experiment-analysis cycles and to ensure confounder robustness in machine learning models. |
format | Online Article Text |
id | pubmed-9023890 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-90238902022-04-23 Data science, human intelligence, and therapeutics discovery: An interview with Sean Escola, Saul Kato, and Pavan Ramkumar Ramkumar, Pavan Kato, Saul Escola, G. Sean Patterns (N Y) People of Data Sean Escola, Saul Kato, and Pavan Ramkumar explain the importance of data science in their research. They have developed a simple non-parametric statistical method called the Rank-to-Group (RTG) score that identifies hierarchical confounder effects in raw data and machine learning-derived data embeddings. This approach should be generally useful in experiment-analysis cycles and to ensure confounder robustness in machine learning models. Elsevier 2022-04-08 /pmc/articles/PMC9023890/ /pubmed/35465229 http://dx.doi.org/10.1016/j.patter.2022.100490 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | People of Data Ramkumar, Pavan Kato, Saul Escola, G. Sean Data science, human intelligence, and therapeutics discovery: An interview with Sean Escola, Saul Kato, and Pavan Ramkumar |
title | Data science, human intelligence, and therapeutics discovery: An interview with Sean Escola, Saul Kato, and Pavan Ramkumar |
title_full | Data science, human intelligence, and therapeutics discovery: An interview with Sean Escola, Saul Kato, and Pavan Ramkumar |
title_fullStr | Data science, human intelligence, and therapeutics discovery: An interview with Sean Escola, Saul Kato, and Pavan Ramkumar |
title_full_unstemmed | Data science, human intelligence, and therapeutics discovery: An interview with Sean Escola, Saul Kato, and Pavan Ramkumar |
title_short | Data science, human intelligence, and therapeutics discovery: An interview with Sean Escola, Saul Kato, and Pavan Ramkumar |
title_sort | data science, human intelligence, and therapeutics discovery: an interview with sean escola, saul kato, and pavan ramkumar |
topic | People of Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023890/ https://www.ncbi.nlm.nih.gov/pubmed/35465229 http://dx.doi.org/10.1016/j.patter.2022.100490 |
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