Literature DB >> 33051704

Application of deep learning in genomics.

Jianxiao Liu1,2, Jiying Li3, Hai Wang4, Jianbing Yan5.   

Abstract

In recent years, deep learning has been widely used in diverse fields of research, such as speech recognition, image classification, autonomous driving and natural language processing. Deep learning has showcased dramatically improved performance in complex classification and regression problems, where the intricate structure in the high-dimensional data is difficult to discover using conventional machine learning algorithms. In biology, applications of deep learning are gaining increasing popularity in predicting the structure and function of genomic elements, such as promoters, enhancers, or gene expression levels. In this review paper, we described the basic concepts in machine learning and artificial neural network, followed by elaboration on the workflow of using convolutional neural network in genomics. Then we provided a concise introduction of deep learning applications in genomics and synthetic biology at the levels of DNA, RNA and protein. Finally, we discussed the current challenges and future perspectives of deep learning in genomics.

Keywords:  convolutional neural network; deep learning; genomics

Year:  2020        PMID: 33051704     DOI: 10.1007/s11427-020-1804-5

Source DB:  PubMed          Journal:  Sci China Life Sci        ISSN: 1674-7305            Impact factor:   6.038


  63 in total

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2.  Gene expression inference with deep learning.

Authors:  Yifei Chen; Yi Li; Rajiv Narayan; Aravind Subramanian; Xiaohui Xie
Journal:  Bioinformatics       Date:  2016-02-11       Impact factor: 6.937

3.  Biological sequence modeling with convolutional kernel networks.

Authors:  Dexiong Chen; Laurent Jacob; Julien Mairal
Journal:  Bioinformatics       Date:  2019-09-15       Impact factor: 6.937

Review 4.  Next-Generation Machine Learning for Biological Networks.

Authors:  Diogo M Camacho; Katherine M Collins; Rani K Powers; James C Costello; James J Collins
Journal:  Cell       Date:  2018-06-07       Impact factor: 41.582

5.  Central dogma of molecular biology.

Authors:  F Crick
Journal:  Nature       Date:  1970-08-08       Impact factor: 49.962

Review 6.  Promoter structure, promoter recognition, and transcription activation in prokaryotes.

Authors:  S Busby; R H Ebright
Journal:  Cell       Date:  1994-12-02       Impact factor: 41.582

7.  Adaptive evolution of non-coding DNA in Drosophila.

Authors:  Peter Andolfatto
Journal:  Nature       Date:  2005-10-20       Impact factor: 49.962

Review 8.  Deep learning for computational biology.

Authors:  Christof Angermueller; Tanel Pärnamaa; Leopold Parts; Oliver Stegle
Journal:  Mol Syst Biol       Date:  2016-07-29       Impact factor: 11.429

9.  DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning.

Authors:  Christof Angermueller; Heather J Lee; Wolf Reik; Oliver Stegle
Journal:  Genome Biol       Date:  2017-04-11       Impact factor: 13.583

Review 10.  Opportunities and obstacles for deep learning in biology and medicine.

Authors:  Travers Ching; Daniel S Himmelstein; Brett K Beaulieu-Jones; Alexandr A Kalinin; Brian T Do; Gregory P Way; Enrico Ferrero; Paul-Michael Agapow; Michael Zietz; Michael M Hoffman; Wei Xie; Gail L Rosen; Benjamin J Lengerich; Johnny Israeli; Jack Lanchantin; Stephen Woloszynek; Anne E Carpenter; Avanti Shrikumar; Jinbo Xu; Evan M Cofer; Christopher A Lavender; Srinivas C Turaga; Amr M Alexandari; Zhiyong Lu; David J Harris; Dave DeCaprio; Yanjun Qi; Anshul Kundaje; Yifan Peng; Laura K Wiley; Marwin H S Segler; Simina M Boca; S Joshua Swamidass; Austin Huang; Anthony Gitter; Casey S Greene
Journal:  J R Soc Interface       Date:  2018-04       Impact factor: 4.293

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  6 in total

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Journal:  Nat Commun       Date:  2022-07-05       Impact factor: 17.694

2.  Concat_CNN: A Model to Detect COVID-19 from Chest X-ray Images with Deep Learning.

Authors:  Priyanka Saha; Sarmistha Neogy
Journal:  SN Comput Sci       Date:  2022-05-23

Review 3.  Plant multiscale networks: charting plant connectivity by multi-level analysis and imaging techniques.

Authors:  Xi Zhang; Yi Man; Xiaohong Zhuang; Jinbo Shen; Yi Zhang; Yaning Cui; Meng Yu; Jingjing Xing; Guangchao Wang; Na Lian; Zijian Hu; Lingyu Ma; Weiwei Shen; Shunyao Yang; Huimin Xu; Jiahui Bian; Yanping Jing; Xiaojuan Li; Ruili Li; Tonglin Mao; Yuling Jiao; Haiyun Ren; Jinxing Lin
Journal:  Sci China Life Sci       Date:  2021-03-12       Impact factor: 6.038

4.  Using precision phenotyping to inform de novo domestication.

Authors:  Alisdair R Fernie; Saleh Alseekh; Jie Liu; Jianbing Yan
Journal:  Plant Physiol       Date:  2021-07-06       Impact factor: 8.340

Review 5.  Smart breeding approaches in post-genomics era for developing climate-resilient food crops.

Authors:  Rubab Zahra Naqvi; Hamid Anees Siddiqui; Muhammad Arslan Mahmood; Syed Najeebullah; Aiman Ehsan; Maryam Azhar; Muhammad Farooq; Imran Amin; Shaheen Asad; Zahid Mukhtar; Shahid Mansoor; Muhammad Asif
Journal:  Front Plant Sci       Date:  2022-09-16       Impact factor: 6.627

6.  Implicitly perturbed Hamiltonian as a class of versatile and general-purpose molecular representations for machine learning.

Authors:  Amin Alibakhshi; Bernd Hartke
Journal:  Nat Commun       Date:  2022-03-10       Impact factor: 17.694

  6 in total

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