Abstract
The microbiome, the community of microorganisms living within an individual, is a promising avenue for developing non-invasive methods for disease screening and diagnosis. Here, we utilize 5643 aggregated, annotated whole-community metagenomes to implement the first multiclass microbiome disease classifier of this scale, able to discriminate between 18 different diseases and healthy. We compared three different machine learning models: random forests, deep neural nets, and a novel graph convolutional architecture which exploits the graph structure of phylogenetic trees as its input. We show that the graph convolutional model outperforms deep neural nets in terms of accuracy (achieving 75% average test-set accuracy), receiver-operator-characteristics (92.1% average area-under-ROC (AUC)), and precision-recall (50% average area-under-precision-recall (AUPR)). Additionally, the convolutional net's performance complements that of the random forest, showing a lower propensity for Type-I errors (false-positives) while the random forest makes less Type-II errors (false-negatives). Lastly, we are able to achieve over 90% average top-3 accuracy across all of our models. Together, these results indicate that there are predictive, disease-specific signatures across microbiomes that can be used for diagnostic purposes.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 55-66 |
| Number of pages | 12 |
| Journal | Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing |
| Volume | 25 |
| State | Published - Jan 1 2020 |
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ASJC Scopus subject areas
- Biomedical Engineering
- Computational Theory and Mathematics
Cite this
Multiclass Disease Classification from Microbial Whole-Community Metagenomes. / Khan, Saad; Kelly, Libusha.
In: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, Vol. 25, 01.01.2020, p. 55-66.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Multiclass Disease Classification from Microbial Whole-Community Metagenomes
AU - Khan, Saad
AU - Kelly, Libusha
PY - 2020/1/1
Y1 - 2020/1/1
N2 - The microbiome, the community of microorganisms living within an individual, is a promising avenue for developing non-invasive methods for disease screening and diagnosis. Here, we utilize 5643 aggregated, annotated whole-community metagenomes to implement the first multiclass microbiome disease classifier of this scale, able to discriminate between 18 different diseases and healthy. We compared three different machine learning models: random forests, deep neural nets, and a novel graph convolutional architecture which exploits the graph structure of phylogenetic trees as its input. We show that the graph convolutional model outperforms deep neural nets in terms of accuracy (achieving 75% average test-set accuracy), receiver-operator-characteristics (92.1% average area-under-ROC (AUC)), and precision-recall (50% average area-under-precision-recall (AUPR)). Additionally, the convolutional net's performance complements that of the random forest, showing a lower propensity for Type-I errors (false-positives) while the random forest makes less Type-II errors (false-negatives). Lastly, we are able to achieve over 90% average top-3 accuracy across all of our models. Together, these results indicate that there are predictive, disease-specific signatures across microbiomes that can be used for diagnostic purposes.
AB - The microbiome, the community of microorganisms living within an individual, is a promising avenue for developing non-invasive methods for disease screening and diagnosis. Here, we utilize 5643 aggregated, annotated whole-community metagenomes to implement the first multiclass microbiome disease classifier of this scale, able to discriminate between 18 different diseases and healthy. We compared three different machine learning models: random forests, deep neural nets, and a novel graph convolutional architecture which exploits the graph structure of phylogenetic trees as its input. We show that the graph convolutional model outperforms deep neural nets in terms of accuracy (achieving 75% average test-set accuracy), receiver-operator-characteristics (92.1% average area-under-ROC (AUC)), and precision-recall (50% average area-under-precision-recall (AUPR)). Additionally, the convolutional net's performance complements that of the random forest, showing a lower propensity for Type-I errors (false-positives) while the random forest makes less Type-II errors (false-negatives). Lastly, we are able to achieve over 90% average top-3 accuracy across all of our models. Together, these results indicate that there are predictive, disease-specific signatures across microbiomes that can be used for diagnostic purposes.
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M3 - Article
C2 - 31797586
AN - SCOPUS:85076098634
VL - 25
SP - 55
EP - 66
JO - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
JF - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
SN - 2335-6936
ER -