Modeling chromatin state from sequence across angiosperms using recurrent convolutional neural networks-Reference-Cited by-同舟云学术

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Modeling chromatin state from sequence across angiosperms using recurrent convolutional neural networks

Published:2022-08-03 Issue:3 Volume:15 Page:
ISSN:1940-3372
Container-title:The Plant Genome
language:en
Short-container-title:The Plant Genome

Author:

Wrightsman Travis¹^ORCID,Marand Alexandre P.²^ORCID,Crisp Peter A.³^ORCID,Springer Nathan M.⁴^ORCID,Buckler Edward S.¹⁵⁶^ORCID

Affiliation:

1. Section of Plant Breeding and Genetics Cornell Univ. Ithaca NY 14853 USA

2. Dep. of Genetics Univ. of Georgia Athens GA 30602 USA

3. School of Agriculture and Food Sciences Univ. of Queensland Brisbane QLD 4072 Australia

4. Dep. of Plant and Microbial Biology Univ. of Minnesota Saint Paul MN 55108 USA

5. Institute for Genomic Diversity Cornell Univ. Ithaca NY 14853 USA

6. USDA‐ARS Ithaca NY 14853 USA

Publisher

Wiley

Subject

Plant Science,Agronomy and Crop Science,Genetics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/tpg2.20249

Reference83 articles.

1. Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks

2. Evolutionary genetics of plant adaptation

3. Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq

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1. Cross-species modeling of plant genomes at single nucleotide resolution using a pre-trained DNA language model;2024-06-05

2. Current genomic deep learning architectures generalize across grass species but not alleles;2024-04-13

3. Frontiers and techniques in plant gene regulation;Current Opinion in Plant Biology;2023-10

4. Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize;2023-05-14

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