Literature DB >> 31026571

Machine learning technology in the application of genome analysis: A systematic review.

Jie Wu1, Yiqiang Zhao2.   

Abstract

Machine learning (ML) is a powerful technique to tackle many problems in data mining and predictive analytics. We believe that ML will be of considerable potentials in the field of bioinformatics since the high-throughput technology is producing ever increasing biological data. In this review, we summarized major ML algorithms and conditions that must be paid attention to when applying these algorithms to genomic problems in details and we provided a list of examples from different perspectives and data analysis challenges at present.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Bioinformatics; Gene; Genomics; Machine learning

Mesh:

Year:  2019        PMID: 31026571     DOI: 10.1016/j.gene.2019.04.062

Source DB:  PubMed          Journal:  Gene        ISSN: 0378-1119            Impact factor:   3.688


  7 in total

Review 1.  Systematic Review on Local Ancestor Inference From a Mathematical and Algorithmic Perspective.

Authors:  Jie Wu; Yangxiu Liu; Yiqiang Zhao
Journal:  Front Genet       Date:  2021-05-24       Impact factor: 4.599

2.  Machine learning approaches to the human metabolome in sepsis identify metabolic links with survival.

Authors:  Leah B Kosyakovsky; Emily Somerset; Angela J Rogers; Michael Sklar; Jared R Mayers; Augustin Toma; Yishay Szekely; Sabri Soussi; Bo Wang; Chun-Po S Fan; Rebecca M Baron; Patrick R Lawler
Journal:  Intensive Care Med Exp       Date:  2022-06-17

Review 3.  The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease.

Authors:  Rohan Mishra; Bin Li
Journal:  Aging Dis       Date:  2020-12-01       Impact factor: 6.745

Review 4.  Natural products for infectious microbes and diseases: an overview of sources, compounds, and chemical diversities.

Authors:  Lu Luo; Jun Yang; Cheng Wang; Jie Wu; Yafang Li; Xu Zhang; Hui Li; Hui Zhang; Yumei Zhou; Aiping Lu; Shilin Chen
Journal:  Sci China Life Sci       Date:  2021-10-21       Impact factor: 10.372

5.  Phenotype analysis of cultivation processes via unsupervised machine learning: Demonstration for Clostridium pasteurianum.

Authors:  Yaeseong Hong; Tom Nguyen; Philipp Arbter; Tyll Utesch; An-Ping Zeng
Journal:  Eng Life Sci       Date:  2021-12-10       Impact factor: 2.678

6.  Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol.

Authors:  Rodrigo M Carrillo-Larco; Lorainne Tudor Car; Jonathan Pearson-Stuttard; Trishan Panch; J Jaime Miranda; Rifat Atun
Journal:  BMJ Open       Date:  2020-05-10       Impact factor: 2.692

7.  Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree.

Authors:  Yanfeng Wang; Yuli Yang; Junwei Sun; Lidong Wang; Xin Song; Xueke Zhao
Journal:  Front Genet       Date:  2020-10-23       Impact factor: 4.599

  7 in total

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