liverRegression code.
<p dir="ltr">Enhancers are transcriptional regulatory elements that help drive phenotypic diversity, yet they often undergo rapid sequence evolution despite functional conservation, posing a challenge for predicting their function across species. Previous machine learning models have...
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| _version_ | 1849927625229729792 |
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| author | Amy Stephen (22684448) |
| author_facet | Amy Stephen (22684448) |
| author_role | author |
| dc.creator.none.fl_str_mv | Amy Stephen (22684448) |
| dc.date.none.fl_str_mv | 2025-11-25T22:44:52Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.30715880.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/liverRegression_code_/30715880 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Bioinformatic methods development Genomics and transcriptomics Convolutional Neural Networks. comparative genomics approaches |
| dc.title.none.fl_str_mv | liverRegression code. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p dir="ltr">Enhancers are transcriptional regulatory elements that help drive phenotypic diversity, yet they often undergo rapid sequence evolution despite functional conservation, posing a challenge for predicting their function across species. Previous machine learning models have focused on the binary task of predicting the presence or absence of open chromatin, but yet to demonstrate an ability to predict continuous differences in signal. Here, we trained convolutional neural networks (CNNs) on a regression task to predict chromatin accessibility, which is a proxy for enhancer activity, in the liver across five mammals, and we developed a novel framework to evaluate cross-species performance. We demonstrated that training on multiple species improves model generalization to both species used in training and held-out species. However, the models consistently achieved poor performance in predicting quantitative differences in accessibility between species at orthologous regions. Our study highlights the challenges in using regression models to predict chromatin accessibility changes between species. </p> |
| eu_rights_str_mv | openAccess |
| id | Manara_9cbf7aee1719bf233e6184efcac740d1 |
| identifier_str_mv | 10.6084/m9.figshare.30715880.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30715880 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | liverRegression code.Amy Stephen (22684448)Bioinformatic methods developmentGenomics and transcriptomicsConvolutional Neural Networks.comparative genomics approaches<p dir="ltr">Enhancers are transcriptional regulatory elements that help drive phenotypic diversity, yet they often undergo rapid sequence evolution despite functional conservation, posing a challenge for predicting their function across species. Previous machine learning models have focused on the binary task of predicting the presence or absence of open chromatin, but yet to demonstrate an ability to predict continuous differences in signal. Here, we trained convolutional neural networks (CNNs) on a regression task to predict chromatin accessibility, which is a proxy for enhancer activity, in the liver across five mammals, and we developed a novel framework to evaluate cross-species performance. We demonstrated that training on multiple species improves model generalization to both species used in training and held-out species. However, the models consistently achieved poor performance in predicting quantitative differences in accessibility between species at orthologous regions. Our study highlights the challenges in using regression models to predict chromatin accessibility changes between species. </p>2025-11-25T22:44:52ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.6084/m9.figshare.30715880.v1https://figshare.com/articles/dataset/liverRegression_code_/30715880CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/307158802025-11-25T22:44:52Z |
| spellingShingle | liverRegression code. Amy Stephen (22684448) Bioinformatic methods development Genomics and transcriptomics Convolutional Neural Networks. comparative genomics approaches |
| status_str | publishedVersion |
| title | liverRegression code. |
| title_full | liverRegression code. |
| title_fullStr | liverRegression code. |
| title_full_unstemmed | liverRegression code. |
| title_short | liverRegression code. |
| title_sort | liverRegression code. |
| topic | Bioinformatic methods development Genomics and transcriptomics Convolutional Neural Networks. comparative genomics approaches |