Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases.
| Title: | Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases. |
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| Authors: | Manandhar, Ishan1 (AUTHOR), Alimadadi, Ahmad1 (AUTHOR), Aryal, Sachin1 (AUTHOR), Munroe, Patricia B.2 (AUTHOR), Joe, Bina1 (AUTHOR) bina.joe@utoledo.edu, Xi Cheng1 (AUTHOR) Xi.Cheng@utoledo.edu |
| Source: | American Journal of Physiology: Gastrointestinal & Liver Physiology. Mar2021, Vol. 320 Issue 3, pG328-G337. 10p. 3 Charts, 5 Graphs. |
| Abstract: | Despite the availability of various diagnostic tests for inflammatory bowel diseases (IBD), misdiagnosis of IBD occurs frequently, and thus, there is a clinical need to further improve the diagnosis of IBD. As gut dysbiosis is reported in patients with IBD, we hypothesized that supervised machine learning (ML) could be used to analyze gut microbiome data for predictive diagnostics of IBD. To test our hypothesis, fecal 16S metagenomic data of 729 subjects with IBD and 700 subjects without IBD from the American Gut Project were analyzed using five different ML algorithms. Fifty differential bacterial taxa were identified [linear discriminant analysis effect size (LEfSe): linear discriminant analysis (LDA) score > 3] between the IBD and non-IBD groups, and ML classifications trained with these taxonomic features using random forest (RF) achieved a testing area under the receiver operating characteristic curves (AUC) of ∼0.80. Next, we tested if operational taxonomic units (OTUs), instead of bacterial taxa, could be used as ML features for diagnostic classification of IBD. Top 500 high-variance OTUs were used for ML training, and an improved testing AUC of ∼0.82 (RF) was achieved. Lastly, we tested if supervised ML could be used for differentiating Crohn's disease (CD) and ulcerative colitis (UC). Using 331 CD and 141 UC samples, 117 differential bacterial taxa (LEfSe: LDA score > 3) were identified, and the RF model trained with differential taxonomic features or high-variance OTU features achieved a testing AUC > 0.90. In summary, our study demonstrates the promising potential of artificial intelligence via supervised ML modeling for predictive diagnostics of IBD using gut microbiome data. NEW & NOTEWORTHY Our study demonstrates the promising potential of artificial intelligence via supervised machine learning modeling for predictive diagnostics of different types of inflammatory bowel diseases using fecal gut microbiome data. [ABSTRACT FROM AUTHOR] |
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| Items | – Name: Title Label: Title Group: Ti Data: Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Manandhar%2C+Ishan%22">Manandhar, Ishan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alimadadi%2C+Ahmad%22">Alimadadi, Ahmad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Aryal%2C+Sachin%22">Aryal, Sachin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Munroe%2C+Patricia+B%2E%22">Munroe, Patricia B.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Joe%2C+Bina%22">Joe, Bina</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> bina.joe@utoledo.edu</i><br /><searchLink fieldCode="AR" term="%22Xi+Cheng%22">Xi Cheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Xi.Cheng@utoledo.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22American+Journal+of+Physiology%3A+Gastrointestinal+%26+Liver+Physiology%22">American Journal of Physiology: Gastrointestinal & Liver Physiology</searchLink>. Mar2021, Vol. 320 Issue 3, pG328-G337. 10p. 3 Charts, 5 Graphs. – Name: Abstract Label: Abstract Group: Ab Data: Despite the availability of various diagnostic tests for inflammatory bowel diseases (IBD), misdiagnosis of IBD occurs frequently, and thus, there is a clinical need to further improve the diagnosis of IBD. As gut dysbiosis is reported in patients with IBD, we hypothesized that supervised machine learning (ML) could be used to analyze gut microbiome data for predictive diagnostics of IBD. To test our hypothesis, fecal 16S metagenomic data of 729 subjects with IBD and 700 subjects without IBD from the American Gut Project were analyzed using five different ML algorithms. Fifty differential bacterial taxa were identified [linear discriminant analysis effect size (LEfSe): linear discriminant analysis (LDA) score > 3] between the IBD and non-IBD groups, and ML classifications trained with these taxonomic features using random forest (RF) achieved a testing area under the receiver operating characteristic curves (AUC) of ∼0.80. Next, we tested if operational taxonomic units (OTUs), instead of bacterial taxa, could be used as ML features for diagnostic classification of IBD. Top 500 high-variance OTUs were used for ML training, and an improved testing AUC of ∼0.82 (RF) was achieved. Lastly, we tested if supervised ML could be used for differentiating Crohn's disease (CD) and ulcerative colitis (UC). Using 331 CD and 141 UC samples, 117 differential bacterial taxa (LEfSe: LDA score > 3) were identified, and the RF model trained with differential taxonomic features or high-variance OTU features achieved a testing AUC > 0.90. In summary, our study demonstrates the promising potential of artificial intelligence via supervised ML modeling for predictive diagnostics of IBD using gut microbiome data. NEW & NOTEWORTHY Our study demonstrates the promising potential of artificial intelligence via supervised machine learning modeling for predictive diagnostics of different types of inflammatory bowel diseases using fecal gut microbiome data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of American Journal of Physiology: Gastrointestinal & Liver Physiology is the property of American Physiological Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1152/ajpgi.00360.2020 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: G328 Titles: – TitleFull: Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Manandhar, Ishan – PersonEntity: Name: NameFull: Alimadadi, Ahmad – PersonEntity: Name: NameFull: Aryal, Sachin – PersonEntity: Name: NameFull: Munroe, Patricia B. – PersonEntity: Name: NameFull: Joe, Bina – PersonEntity: Name: NameFull: Xi Cheng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 01931857 Numbering: – Type: volume Value: 320 – Type: issue Value: 3 Titles: – TitleFull: American Journal of Physiology: Gastrointestinal & Liver Physiology Type: main |
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