Adapting Random Forests to Predict Obesity-Associated Gene Expression-Reference-Cited by-同舟云学术

Adapting Random Forests to Predict Obesity-Associated Gene Expression

Published:2022-07-11 Issue: Volume: Page:
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Container-title:2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Watts Jeremy¹,Allen Elexis²,Mitoubsi Ahmad¹,Khojandi Anahita¹,Eales James³,Jalali-Najafabadi Farideh⁴,Papamarkou Theodore⁵

Affiliation:

1. University of Tennessee,Department of Industrial Engineering,Knoxville,TN,USA

2. University of Tennessee,Department of Biomedical Engineering,Knoxville,TN,USA

3. University of Manchester,Division of Cardiovascular Sciences,Manchester,UK

4. The University of Manchester,Centre for Genetics and Genomics Versus Arthritis, the Centre for Musculoskeletal Research, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre,Manchester,UK

5. University of Manchester,Department of Mathematics,Manchester,UK

Funder

National Institutes of Health

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9870821/9870822/09871234.pdf?arnumber=9871234

Reference21 articles.

1. The genetic contribution to non-syndromic human obesity

2. Genetics of Obesity: What have we Learned?

3. Genetics and epigenetics of obesity

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