Using machine learning to identify major shifts in human gut microbiome protein family abundance in disease

Mehrdad Yazdani; Bryn C. Taylor; Justine W. Debelius; Weizhong Li; Rob Knight; Larry Smarr

doi:10.1109/BigData.2016.7840731

Using machine learning to identify major shifts in human gut microbiome protein family abundance in disease

Mehrdad Yazdani, Bryn C. Taylor, Justine W. Debelius, Weizhong Li, Rob Knight, Larry Smarr

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Inflammatory Bowel Disease (IBD) is an autoimmune condition that is observed to be associated with major alterations in the gut microbiome taxonomic composition. Here we classify major changes in microbiome protein family abundances between healthy subjects and IBD patients. We use machine learning to analyze results obtained previously from computing relative abundance of ∼10,000 KEGG orthologous protein families in the gut microbiome of a set of healthy individuals and IBD patients. We develop a machine learning pipeline, involving the Kolomogorv-Smirnov test, to identify the 100 most statistically significant entries in the KEGG database. Then we use these 100 as a training set for a Random Forest classifier to determine ∼5% the KEGGs which are best at separating disease and healthy states. Lastly, we developed a Natural Language Processing classifier of the KEGG description files to predict KEGG relative over-or under-abundance. As we expand our analysis from 10,000 KEGG protein families to one million proteins identified in the gut microbiome, scalable methods for quickly identifying such anomalies between health and disease states will be increasingly valuable for biological interpretation of sequence data.

Original language	English (US)
Title of host publication	Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
Editors	Ronay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1272-1280
Number of pages	9
ISBN (Electronic)	9781467390040
DOIs	https://doi.org/10.1109/BigData.2016.7840731
State	Published - 2016
Externally published	Yes
Event	4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States Duration: Dec 5 2016 → Dec 8 2016

Publication series

Name	Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Conference

Conference	4th IEEE International Conference on Big Data, Big Data 2016
Country/Territory	United States
City	Washington
Period	12/5/16 → 12/8/16

Keywords

ibd
kegg
machine learning
microbiome
pca
random forest

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Hardware and Architecture

Access to Document

10.1109/BigData.2016.7840731

Cite this

Yazdani, M., Taylor, B. C., Debelius, J. W., Li, W., Knight, R., & Smarr, L. (2016). Using machine learning to identify major shifts in human gut microbiome protein family abundance in disease. In R. Ak, G. Karypis, Y. Xia, X. T. Hu, P. S. Yu, J. Joshi, L. Ungar, L. Liu, A.-H. Sato, T. Suzumura, S. Rachuri, R. Govindaraju, & W. Xu (Eds.), Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 (pp. 1272-1280). Article 7840731 (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2016.7840731

Using machine learning to identify major shifts in human gut microbiome protein family abundance in disease. / Yazdani, Mehrdad; Taylor, Bryn C.; Debelius, Justine W. et al.
Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. ed. / Ronay Ak; George Karypis; Yinglong Xia; Xiaohua Tony Hu; Philip S. Yu; James Joshi; Lyle Ungar; Ling Liu; Aki-Hiro Sato; Toyotaro Suzumura; Sudarsan Rachuri; Rama Govindaraju; Weijia Xu. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1272-1280 7840731 (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yazdani, M, Taylor, BC, Debelius, JW, Li, W, Knight, R & Smarr, L 2016, Using machine learning to identify major shifts in human gut microbiome protein family abundance in disease. in R Ak, G Karypis, Y Xia, XT Hu, PS Yu, J Joshi, L Ungar, L Liu, A-H Sato, T Suzumura, S Rachuri, R Govindaraju & W Xu (eds), Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016., 7840731, Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1272-1280, 4th IEEE International Conference on Big Data, Big Data 2016, Washington, United States, 12/5/16. https://doi.org/10.1109/BigData.2016.7840731

Yazdani M, Taylor BC, Debelius JW, Li W, Knight R, Smarr L. Using machine learning to identify major shifts in human gut microbiome protein family abundance in disease. In Ak R, Karypis G, Xia Y, Hu XT, Yu PS, Joshi J, Ungar L, Liu L, Sato AH, Suzumura T, Rachuri S, Govindaraju R, Xu W, editors, Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1272-1280. 7840731. (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016). doi: 10.1109/BigData.2016.7840731

Yazdani, Mehrdad ; Taylor, Bryn C. ; Debelius, Justine W. et al. / Using machine learning to identify major shifts in human gut microbiome protein family abundance in disease. Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. editor / Ronay Ak ; George Karypis ; Yinglong Xia ; Xiaohua Tony Hu ; Philip S. Yu ; James Joshi ; Lyle Ungar ; Ling Liu ; Aki-Hiro Sato ; Toyotaro Suzumura ; Sudarsan Rachuri ; Rama Govindaraju ; Weijia Xu. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1272-1280 (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016).

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abstract = "Inflammatory Bowel Disease (IBD) is an autoimmune condition that is observed to be associated with major alterations in the gut microbiome taxonomic composition. Here we classify major changes in microbiome protein family abundances between healthy subjects and IBD patients. We use machine learning to analyze results obtained previously from computing relative abundance of ∼10,000 KEGG orthologous protein families in the gut microbiome of a set of healthy individuals and IBD patients. We develop a machine learning pipeline, involving the Kolomogorv-Smirnov test, to identify the 100 most statistically significant entries in the KEGG database. Then we use these 100 as a training set for a Random Forest classifier to determine ∼5% the KEGGs which are best at separating disease and healthy states. Lastly, we developed a Natural Language Processing classifier of the KEGG description files to predict KEGG relative over-or under-abundance. As we expand our analysis from 10,000 KEGG protein families to one million proteins identified in the gut microbiome, scalable methods for quickly identifying such anomalies between health and disease states will be increasingly valuable for biological interpretation of sequence data.",

keywords = "ibd, kegg, machine learning, microbiome, pca, random forest",

author = "Mehrdad Yazdani and Taylor, {Bryn C.} and Debelius, {Justine W.} and Weizhong Li and Rob Knight and Larry Smarr",

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AU - Knight, Rob

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AB - Inflammatory Bowel Disease (IBD) is an autoimmune condition that is observed to be associated with major alterations in the gut microbiome taxonomic composition. Here we classify major changes in microbiome protein family abundances between healthy subjects and IBD patients. We use machine learning to analyze results obtained previously from computing relative abundance of ∼10,000 KEGG orthologous protein families in the gut microbiome of a set of healthy individuals and IBD patients. We develop a machine learning pipeline, involving the Kolomogorv-Smirnov test, to identify the 100 most statistically significant entries in the KEGG database. Then we use these 100 as a training set for a Random Forest classifier to determine ∼5% the KEGGs which are best at separating disease and healthy states. Lastly, we developed a Natural Language Processing classifier of the KEGG description files to predict KEGG relative over-or under-abundance. As we expand our analysis from 10,000 KEGG protein families to one million proteins identified in the gut microbiome, scalable methods for quickly identifying such anomalies between health and disease states will be increasingly valuable for biological interpretation of sequence data.

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ER -

Using machine learning to identify major shifts in human gut microbiome protein family abundance in disease

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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