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: | Doron Stupp, Elad Sharon, Idit Bloch, Marinka Zitnik, Or Zuk, Yuval Tabach |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2021-11-01
|
Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-021-26792-w |
Similar Items
-
Synthetic RNA-Based Immunomodulatory Gene Circuits for Cancer Immunotherapy
by: Stupp, Doron, et al.
Published: (2018) -
ACE2 Co-evolutionary Pattern Suggests Targets for Pharmaceutical Intervention in the COVID-19 Pandemic
by: Maya Braun, et al.
Published: (2020-08-01) -
Using multi-scale genomics to associate poorly annotated genes with rare diseases
by: Christina Canavati, et al.
Published: (2024-01-01) -
Expanding the MECP2 network using comparative genomics reveals potential therapeutic targets for Rett syndrome
by: Irene Unterman, et al.
Published: (2021-08-01) -
Developmental and temporal changes in petunia petal transcriptome reveal scent-repressing plant-specific RING–kinase–WD40 protein
by: Ekaterina Shor, et al.
Published: (2023-06-01)