Co-evolution based machine-learning for predicting functional interactions between human genes
With the rise in number of eukaryotic species being fully sequenced, large scale phylogenetic profiling can give insights on gene function, Here, the authors describe a machine-learning approach that integrates co-evolution across eukaryotic clades to predict gene function and functional interaction...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-021-26792-w |
| _version_ | 1798033164381192192 |
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| author | Doron Stupp Elad Sharon Idit Bloch Marinka Zitnik Or Zuk Yuval Tabach |
| author_facet | Doron Stupp Elad Sharon Idit Bloch Marinka Zitnik Or Zuk Yuval Tabach |
| author_sort | Doron Stupp |
| collection | DOAJ |
| description | With the rise in number of eukaryotic species being fully sequenced, large scale phylogenetic profiling can give insights on gene function, Here, the authors describe a machine-learning approach that integrates co-evolution across eukaryotic clades to predict gene function and functional interactions among human genes. |
| first_indexed | 2024-04-11T20:25:57Z |
| format | Article |
| id | doaj.art-59ff6a7933cf40ff80afbe2c5b1265df |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-11T20:25:57Z |
| publishDate | 2021-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-59ff6a7933cf40ff80afbe2c5b1265df2022-12-22T04:04:40ZengNature PortfolioNature Communications2041-17232021-11-0112111410.1038/s41467-021-26792-wCo-evolution based machine-learning for predicting functional interactions between human genesDoron Stupp0Elad Sharon1Idit Bloch2Marinka Zitnik3Or Zuk4Yuval Tabach5Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University of JerusalemDepartment of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University of JerusalemDepartment of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University of JerusalemDepartment of Biomedical Informatics, Harvard UniversityDepartment of Statistics and Data Science, The Hebrew University of JerusalemDepartment of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University of JerusalemWith the rise in number of eukaryotic species being fully sequenced, large scale phylogenetic profiling can give insights on gene function, Here, the authors describe a machine-learning approach that integrates co-evolution across eukaryotic clades to predict gene function and functional interactions among human genes.https://doi.org/10.1038/s41467-021-26792-w |
| spellingShingle | Doron Stupp Elad Sharon Idit Bloch Marinka Zitnik Or Zuk Yuval Tabach Co-evolution based machine-learning for predicting functional interactions between human genes Nature Communications |
| title | Co-evolution based machine-learning for predicting functional interactions between human genes |
| title_full | Co-evolution based machine-learning for predicting functional interactions between human genes |
| title_fullStr | Co-evolution based machine-learning for predicting functional interactions between human genes |
| title_full_unstemmed | Co-evolution based machine-learning for predicting functional interactions between human genes |
| title_short | Co-evolution based machine-learning for predicting functional interactions between human genes |
| title_sort | co evolution based machine learning for predicting functional interactions between human genes |
| url | https://doi.org/10.1038/s41467-021-26792-w |
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