MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks
Abstract Background Microbiome profiles in the human body and environment niches have become publicly available due to recent advances in high-throughput sequencing technologies. Indeed, recent studies have already identified different microbiome profiles in healthy and sick individuals for a variet...
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| Format: | Article |
| Language: | English |
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BMC
2019-06-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-019-2833-2 |
| _version_ | 1818341622289530880 |
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| author | Chieh Lo Radu Marculescu |
| author_facet | Chieh Lo Radu Marculescu |
| author_sort | Chieh Lo |
| collection | DOAJ |
| description | Abstract Background Microbiome profiles in the human body and environment niches have become publicly available due to recent advances in high-throughput sequencing technologies. Indeed, recent studies have already identified different microbiome profiles in healthy and sick individuals for a variety of diseases; this suggests that the microbiome profile can be used as a diagnostic tool in identifying the disease states of an individual. However, the high-dimensional nature of metagenomic data poses a significant challenge to existing machine learning models. Consequently, to enable personalized treatments, an efficient framework that can accurately and robustly differentiate between healthy and sick microbiome profiles is needed. Results In this paper, we propose MetaNN (i.e., classification of host phenotypes from Metagenomic data using Neural Networks), a neural network framework which utilizes a new data augmentation technique to mitigate the effects of data over-fitting. Conclusions We show that MetaNN outperforms existing state-of-the-art models in terms of classification accuracy for both synthetic and real metagenomic data. These results pave the way towards developing personalized treatments for microbiome related diseases. |
| first_indexed | 2024-12-13T16:01:43Z |
| format | Article |
| id | doaj.art-f227ca42da6841efa11fbee1ebfa68cc |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-13T16:01:43Z |
| publishDate | 2019-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-f227ca42da6841efa11fbee1ebfa68cc2022-12-21T23:39:08ZengBMCBMC Bioinformatics1471-21052019-06-0120S1211410.1186/s12859-019-2833-2MetaNN: accurate classification of host phenotypes from metagenomic data using neural networksChieh Lo0Radu Marculescu1Department of Electrical and Computer Engineering, Carnegie Mellon UniversityDepartment of Electrical and Computer Engineering, Carnegie Mellon UniversityAbstract Background Microbiome profiles in the human body and environment niches have become publicly available due to recent advances in high-throughput sequencing technologies. Indeed, recent studies have already identified different microbiome profiles in healthy and sick individuals for a variety of diseases; this suggests that the microbiome profile can be used as a diagnostic tool in identifying the disease states of an individual. However, the high-dimensional nature of metagenomic data poses a significant challenge to existing machine learning models. Consequently, to enable personalized treatments, an efficient framework that can accurately and robustly differentiate between healthy and sick microbiome profiles is needed. Results In this paper, we propose MetaNN (i.e., classification of host phenotypes from Metagenomic data using Neural Networks), a neural network framework which utilizes a new data augmentation technique to mitigate the effects of data over-fitting. Conclusions We show that MetaNN outperforms existing state-of-the-art models in terms of classification accuracy for both synthetic and real metagenomic data. These results pave the way towards developing personalized treatments for microbiome related diseases.http://link.springer.com/article/10.1186/s12859-019-2833-2MetagenomicsNeural networksHost phenotypesMachine learning |
| spellingShingle | Chieh Lo Radu Marculescu MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks BMC Bioinformatics Metagenomics Neural networks Host phenotypes Machine learning |
| title | MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks |
| title_full | MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks |
| title_fullStr | MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks |
| title_full_unstemmed | MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks |
| title_short | MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks |
| title_sort | metann accurate classification of host phenotypes from metagenomic data using neural networks |
| topic | Metagenomics Neural networks Host phenotypes Machine learning |
| url | http://link.springer.com/article/10.1186/s12859-019-2833-2 |
| work_keys_str_mv | AT chiehlo metannaccurateclassificationofhostphenotypesfrommetagenomicdatausingneuralnetworks AT radumarculescu metannaccurateclassificationofhostphenotypesfrommetagenomicdatausingneuralnetworks |