气候变化领域集成服务门户(CCPortal): Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function

CCPortal

DOI	10.1073/pnas.2103070118
	Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function
	Gardiner L.-J.; Rusholme-Pilcher R.; Colmer J.; Rees H.; Crescente J.M.; Carrieri A.P.; Duncan S.; Pyzer-Knapp E.O.; Krishna R.; Hall A.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:32
英文摘要	The circadian clock is an important adaptation to life on Earth. Here, we use machine learning to predict complex, temporal, and circadian gene expression patterns in Arabidopsis. Most significantly, we classify circadian genes using DNA sequence features generated de novo from public, genomic resources, facilitating downstream application of our methodswith no experimental work or prior knowledge needed. We use local model explanation that is transcript specific to rank DNA sequence features, providing a detailed profile of the potential circadian regulatory mechanisms for each transcript. Furthermore, we can discriminate the temporal phase of transcript expression using the local, explanation-derived, and ranked DNA sequence features, revealing hidden subclasses within the circadian class. Model interpretation/explanation provides the backbone of our methodological advances, giving insight into biological processes and experimental design. Next, we use model interpretation to optimize sampling strategies when we predict circadian transcripts using reduced numbers of transcriptomic timepoints. Finally, we predict the circadian time from a single, transcriptomic timepoint, deriving marker transcripts that are most impactful for accurate prediction; this could facilitate the identification of altered clock function from existing datasets. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Circadian; Explainable AI; Function; Regulation; Transcriptome
语种	英语
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/238834
作者单位	IBM Research Europe, The Hartree Centre, Warrington, WA4 4AD, United Kingdom; Earlham Institute, Norwich, NR4 7UZ, United Kingdom; Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, C1425FQB, Argentina; School of Biological Sciences, University of East Anglia, Norwich, NR4 7TJ, United Kingdom
推荐引用方式 GB/T 7714	Gardiner L.-J.,Rusholme-Pilcher R.,Colmer J.,et al. Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function[J],2021,118(32).
APA	Gardiner L.-J..,Rusholme-Pilcher R..,Colmer J..,Rees H..,Crescente J.M..,...&Hall A..(2021).Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function.Proceedings of the National Academy of Sciences of the United States of America,118(32).
MLA	Gardiner L.-J.,et al."Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function".Proceedings of the National Academy of Sciences of the United States of America 118.32(2021).