Protein Function Prediction for newly sequenced organisms

Mateo Torres
, Haixuan Yang
, Alfonso E. Romero
, Alberto Paccanaro

Research output: Contribution to journal › Article › peer-review

316 Downloads (Pure)

Original language	English
Journal	Nature Machine Intelligence
DOIs	https://doi.org/10.1038/s42256-021-00419-7
Publication status	Published - 9 Dec 2021

Access to Document

10.1038/s42256-021-00419-7

Accepted ManuscriptAccepted author manuscript, 459 KB

New advances in insecticide resistance genomics: using Machine Learning to predict resistance phenotype from large-scale genomic data
Paccanaro, A. (PI)
Medical Research Council MRC
26/05/20 → 30/11/22
Project: Research
ABI innovation: A Graph based Approach for the Genome Wide Prediction of Conditionally Essential Genes
Paccanaro, A. (PI)
National Science Foundation (NSF), National Science Foundation
15/09/17 → 14/09/20
Project: Research
Bilateral NSF/BIO-BBSRC - Translational landscape to link cell growth with proliferation in the root meristem
Bogre, L. (PI) & Paccanaro, A. (PI)
Biotechnology and Biological Sciences Research Council BBSRC
1/07/15 → 30/06/18
Project: Research

Cite this

@article{a136b0e30cec44efb9cc9e6f03938023,

title = "Protein Function Prediction for newly sequenced organisms",

author = "Mateo Torres and Haixuan Yang and Romero, \{Alfonso E.\} and Alberto Paccanaro",

year = "2021",

month = dec,

day = "9",

doi = "10.1038/s42256-021-00419-7",

language = "English",

journal = "Nature Machine Intelligence",

issn = "2522-5839",

publisher = "Springer Nature Switzerland AG",

}