Computational Approaches for Enteric Methane Mitigation Research: from Fermi Calculations to Artificial Intelligence Paradigms
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Aryee R, Mohammed N, Dey S, Arunraj B, Nadendla S, Sajeevan K bioRxiv. 2024; .
PMID: 39345548 PMC: 11429904. DOI: 10.1101/2024.09.16.613350.
References
1.
Kumar A, Wang L, Ng C, Maranas C
. Pathway design using de novo steps through uncharted biochemical spaces. Nat Commun. 2018; 9(1):184.
PMC: 5766603.
DOI: 10.1038/s41467-017-02362-x.
View
2.
Thompson L, Beck M, Buskirk D, Rowntree J, McKendree M
. Cow efficiency: modeling the biological and economic output of a Michigan beef herd. Transl Anim Sci. 2020; 4(3):txaa166.
PMC: 7751152.
DOI: 10.1093/tas/txaa166.
View
3.
Chowdhury R, Bouatta N, Biswas S, Floristean C, Kharkar A, Roy K
. Single-sequence protein structure prediction using a language model and deep learning. Nat Biotechnol. 2022; 40(11):1617-1623.
PMC: 10440047.
DOI: 10.1038/s41587-022-01432-w.
View
4.
Visan A, Negut I
. Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery. Life (Basel). 2024; 14(2).
PMC: 10890405.
DOI: 10.3390/life14020233.
View
5.
Arndt C, Hristov A, Price W, McClelland S, Pelaez A, Cueva S
. Full adoption of the most effective strategies to mitigate methane emissions by ruminants can help meet the 1.5 °C target by 2030 but not 2050. Proc Natl Acad Sci U S A. 2022; 119(20):e2111294119.
PMC: 9171756.
DOI: 10.1073/pnas.2111294119.
View