Literature DB >> 35264799

AI predicts the effectiveness and evolution of gene promoter sequences.

Andreas Wagner.   

Abstract

Entities:  

Keywords:  Computational biology and bioinformatics; Evolution; Genetics; Molecular biology

Mesh：

Year:  2022        PMID： 35264799     DOI： 10.1038/d41586-022-00384-0

Source DB:  PubMed          Journal:  Nature        ISSN： 0028-0836            Impact factor:   69.504


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  3 in total

1.  The evolution, evolvability and engineering of gene regulatory DNA.

Authors:  Eeshit Dhaval Vaishnav; Carl G de Boer; Jennifer Molinet; Moran Yassour; Lin Fan; Xian Adiconis; Dawn A Thompson; Joshua Z Levin; Francisco A Cubillos; Aviv Regev
Journal:  Nature       Date:  2022-03-09       Impact factor: 69.504

2.  Predicting effects of noncoding variants with deep learning-based sequence model.

Authors:  Jian Zhou; Olga G Troyanskaya
Journal:  Nat Methods       Date:  2015-08-24       Impact factor: 28.547

3.  Highly accurate protein structure prediction with AlphaFold.

Authors:  John Jumper; Richard Evans; Alexander Pritzel; Tim Green; Michael Figurnov; Olaf Ronneberger; Kathryn Tunyasuvunakool; Russ Bates; Augustin Žídek; Anna Potapenko; Alex Bridgland; Clemens Meyer; Simon A A Kohl; Andrew J Ballard; Andrew Cowie; Bernardino Romera-Paredes; Stanislav Nikolov; Rishub Jain; Demis Hassabis; Jonas Adler; Trevor Back; Stig Petersen; David Reiman; Ellen Clancy; Michal Zielinski; Martin Steinegger; Michalina Pacholska; Tamas Berghammer; Sebastian Bodenstein; David Silver; Oriol Vinyals; Andrew W Senior; Koray Kavukcuoglu; Pushmeet Kohli
Journal:  Nature       Date:  2021-07-15       Impact factor: 49.962
  3 in total

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