Literature DB >> 29887378

Next-Generation Machine Learning for Biological Networks.

Diogo M Camacho1, Katherine M Collins2, Rani K Powers3, James C Costello4, James J Collins5.   

Abstract

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.
Copyright © 2018 Elsevier Inc. All rights reserved.

Keywords:  Machine leaning; deep learning; network biology; neural networks; synthetic biology; systems biology

Mesh:

Year:  2018        PMID: 29887378     DOI: 10.1016/j.cell.2018.05.015

Source DB:  PubMed          Journal:  Cell        ISSN: 0092-8674            Impact factor:   41.582


  152 in total

Review 1.  Predictive biology: modelling, understanding and harnessing microbial complexity.

Authors:  Allison J Lopatkin; James J Collins
Journal:  Nat Rev Microbiol       Date:  2020-05-29       Impact factor: 60.633

2.  Deep convolutional neural network: a novel approach for the detection of Aspergillus fungi via stereomicroscopy.

Authors:  Haozhong Ma; Jinshan Yang; Xiaolu Chen; Xinyu Jiang; Yimin Su; Shanlei Qiao; Guowei Zhong
Journal:  J Microbiol       Date:  2021-03-29       Impact factor: 3.422

Review 3.  Dissecting the Genetics of Osteoporosis using Systems Approaches.

Authors:  Basel M Al-Barghouthi; Charles R Farber
Journal:  Trends Genet       Date:  2018-11-20       Impact factor: 11.639

Review 4.  Clinician Guide to Microbiome Testing.

Authors:  Christopher Staley; Thomas Kaiser; Alexander Khoruts
Journal:  Dig Dis Sci       Date:  2018-09-28       Impact factor: 3.199

5.  MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction.

Authors:  Nathan LaPierre; Chelsea J-T Ju; Guangyu Zhou; Wei Wang
Journal:  Methods       Date:  2019-03-16       Impact factor: 3.608

Review 6.  Systems Biology of Cancer Metastasis.

Authors:  Yasir Suhail; Margo P Cain; Kiran Vanaja; Paul A Kurywchak; Andre Levchenko; Raghu Kalluri
Journal:  Cell Syst       Date:  2019-08-28       Impact factor: 10.304

7.  TCR Repertoires of Thymic Conventional and Regulatory T Cells: Identification and Characterization of Both Unique and Shared TCR Sequences.

Authors:  Annette Ko; Masashi Watanabe; Thomas Nguyen; Alvin Shi; Achouak Achour; Baojun Zhang; Xiaoping Sun; Qun Wang; Yuan Zhuang; Nan-Ping Weng; Richard J Hodes
Journal:  J Immunol       Date:  2020-01-10       Impact factor: 5.422

8.  Application of deep learning in genomics.

Authors:  Jianxiao Liu; Jiying Li; Hai Wang; Jianbing Yan
Journal:  Sci China Life Sci       Date:  2020-10-10       Impact factor: 6.038

9.  Development of Stock Networks Using Part Mutual Information and Australian Stock Market Data.

Authors:  Yan Yan; Boyao Wu; Tianhai Tian; Hu Zhang
Journal:  Entropy (Basel)       Date:  2020-07-15       Impact factor: 2.524

Review 10.  How Machine Learning Will Transform Biomedicine.

Authors:  Jeremy Goecks; Vahid Jalili; Laura M Heiser; Joe W Gray
Journal:  Cell       Date:  2020-04-02       Impact factor: 41.582

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