Predicting gene essentiality in Caenorhabditis elegans by feature engineering and machine-learning-Reference-Cited by-同舟云学术

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Predicting gene essentiality in Caenorhabditis elegans by feature engineering and machine-learning

Published:2020 Issue: Volume:18 Page:1093-1102
ISSN:2001-0370
Container-title:Computational and Structural Biotechnology Journal
language:en
Short-container-title:Computational and Structural Biotechnology Journal

Author:

Campos Tulio L.,Korhonen Pasi K.,Sternberg Paul W.,Gasser Robin B.,Young Neil D.

Funder

National Health and Medical Research Council

National Institutes of Health

Australian Research Council

Publisher

Elsevier BV

Subject

Computer Science Applications,Genetics,Biochemistry,Structural Biology,Biophysics,Biotechnology

Reference68 articles.

1. Towards a compendium of essential genes - From model organisms to synthetic lethality in cancer cells;Zhan;Crit Rev in Biochem Mol Biol,2016

2. Model organism data evolving in support of translational medicine;Howe;Lab Anim (NY),2018

3. Editorial: Model organisms: a precious resource for the understanding of molecular mechanisms underlying human physiology and disease;Giansanti;Front Genet,2019

4. Caenorhabditis elegans Sequencing Consortium. Genome sequence of the nematode C. 666 elegans: a platform for investigating biology. Science 1998;282:2012–8.

5. The unc-22(IV) region of Caenorhabditis elegans: genetic analysis of lethal mutations;Clark;Genetics,1988

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4. Inference of Essential Genes of the Parasite Haemonchus contortus via Machine Learning;International Journal of Molecular Sciences;2024-06-27

5. Essential genes identification model based on sequence feature map and graph convolutional neural network;BMC Genomics;2024-01-10

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