Literature DB >> 30927505

Biotechnology, Big Data and Artificial Intelligence.

Arlindo L Oliveira1.   

Abstract

Developments in biotechnology are increasingly dependent on the extensive use of big data, generated by modern high-throughput instrumentation technologies, and stored in thousands of databases, public and private. Future developments in this area depend, critically, on the ability of biotechnology researchers to master the skills required to effectively integrate their own contributions with the large amounts of information available in these databases. This article offers a perspective of the relations that exist between the fields of big data and biotechnology, including the related technologies of artificial intelligence and machine learning and describes how data integration, data exploitation, and process optimization correspond to three essential steps in any future biotechnology project. The article also lists a number of application areas where the ability to use big data will become a key factor, including drug discovery, drug recycling, drug safety, functional and structural genomics, proteomics, pharmacogenetics, and pharmacogenomics, among others.
© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  artificial intelligence; big data; bioengineering; machine learning

Mesh:

Year:  2019        PMID: 30927505     DOI: 10.1002/biot.201800613

Source DB:  PubMed          Journal:  Biotechnol J        ISSN: 1860-6768            Impact factor:   4.677


  7 in total

1.  Assessing the Risks Posed by the Convergence of Artificial Intelligence and Biotechnology.

Authors:  John T O'Brien; Cassidy Nelson
Journal:  Health Secur       Date:  2020 May/Jun

2.  Artificial Intelligence in Biomedicine: A Legal Insight.

Authors:  Takis Vidalis
Journal:  BioTech (Basel)       Date:  2021-07-14

Review 3.  Rethinking drug design in the artificial intelligence era.

Authors:  Petra Schneider; W Patrick Walters; Alleyn T Plowright; Norman Sieroka; Jennifer Listgarten; Robert A Goodnow; Jasmin Fisher; Johanna M Jansen; José S Duca; Thomas S Rush; Matthias Zentgraf; John Edward Hill; Elizabeth Krutoholow; Matthias Kohler; Jeff Blaney; Kimito Funatsu; Chris Luebkemann; Gisbert Schneider
Journal:  Nat Rev Drug Discov       Date:  2019-12-04       Impact factor: 84.694

4.  Sharing data, sharing methods, sharing science.

Authors:  Sergio Pantano
Journal:  MethodsX       Date:  2021-12-14

5.  Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis.

Authors:  Yilin Li; Fengjiao Xie; Qin Xiong; Honglin Lei; Peimin Feng
Journal:  Front Oncol       Date:  2022-08-18       Impact factor: 5.738

Review 6.  White paper on high-throughput process development for integrated continuous biomanufacturing.

Authors:  Mariana N São Pedro; Tiago C Silva; Rohan Patil; Marcel Ottens
Journal:  Biotechnol Bioeng       Date:  2021-04-02       Impact factor: 4.530

7.  Application of Machine Learning Algorithms to Predict Lymph Node Metastasis in Early Gastric Cancer.

Authors:  HuaKai Tian; ZhiKun Ning; Zhen Zong; Jiang Liu; CeGui Hu; HouQun Ying; Hui Li
Journal:  Front Med (Lausanne)       Date:  2022-01-18
  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.