Data integration and predictive modeling methods for multi-omics datasets-Reference-Cited by-同舟云学术

Data integration and predictive modeling methods for multi-omics datasets

Published:2018 Issue:1 Volume:14 Page:8-25
ISSN:2515-4184
Container-title:Molecular Omics
language:en
Short-container-title:Mol. Omics

Author:

Kim Minseung¹²³⁴⁵^ORCID,Tagkopoulos Ilias¹²³⁴⁵^ORCID

Affiliation:

1. Department of Computer Science

2. University of California

3. Davis

4. USA

5. Genome Center

Abstract

We provide an overview of opportunities and challenges in multi-omics predictive analytics with particular emphasis on data integration and machine learning methods.

Funder

National Science Foundation

Army Research Office

Publisher

Royal Society of Chemistry (RSC)

Subject

Genetics,Molecular Biology,Biochemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2018/MO/C7MO00051K

Reference223 articles.

1. P. Simon , Too Big to Ignore: The Business Case for Big Data , John Wiley & Sons , 2013 , vol. 72

2. The model organism as a system: integrating 'omics' data sets

3. Methods for the integration of multi-omics data: mathematical aspects

4. Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli

5. Integrating Heterogeneous omics Data via Statistical Inference and Learning Techniques

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