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...
Enregistré dans:
Auteurs principaux: | Doron Stupp, Elad Sharon, Idit Bloch, Marinka Zitnik, Or Zuk, Yuval Tabach |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/59ff6a7933cf40ff80afbe2c5b1265df |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Robust seed germination prediction using deep learning and RGB image data
par: Yuval Nehoshtan, et autres
Publié: (2021) -
A machine learning framework for predicting drug–drug interactions
par: Suyu Mei, et autres
Publié: (2021) -
Prioritizing network communities
par: Marinka Zitnik, et autres
Publié: (2018) -
Identification of disease treatment mechanisms through the multiscale interactome
par: Camilo Ruiz, et autres
Publié: (2021) -
Publisher Correction: A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes
par: Frédéric Cadet, et autres
Publié: (2021)