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Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function.

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Bibliographic Details
Title: Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function.
Authors: Gardiner LJ; IBM Research Europe, The Hartree Centre, Warrington WA4 4AD, United Kingdom; laura-jayne.gardiner@ibm.com., Rusholme-Pilcher R; Earlham Institute, Norwich NR4 7UZ, United Kingdom., Colmer J; Earlham Institute, Norwich NR4 7UZ, United Kingdom., Rees H; Earlham Institute, Norwich NR4 7UZ, United Kingdom., Crescente JM; IBM Research Europe, The Hartree Centre, Warrington WA4 4AD, United Kingdom.; Consejo Nacional de Investigaciones Científicas y Técnicas, C1425FQB Buenos Aires, Argentina., Carrieri AP; IBM Research Europe, The Hartree Centre, Warrington WA4 4AD, United Kingdom., Duncan S; Earlham Institute, Norwich NR4 7UZ, United Kingdom., Pyzer-Knapp EO; IBM Research Europe, The Hartree Centre, Warrington WA4 4AD, United Kingdom., Krishna R; IBM Research Europe, The Hartree Centre, Warrington WA4 4AD, United Kingdom., Hall A; Earlham Institute, Norwich NR4 7UZ, United Kingdom.; School of Biological Sciences, University of East Anglia, Norwich NR4 7TJ, United Kingdom.
Source: Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2021 Aug 10; Vol. 118 (32).
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: National Academy of Sciences Country of Publication: United States NLM ID: 7505876 Publication Model: Print Cited Medium: Internet ISSN: 1091-6490 (Electronic) Linking ISSN: 00278424 NLM ISO Abbreviation: Proc Natl Acad Sci U S A Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1091-6490
DOI:10.1073/pnas.2103070118