When technology meets technology: Retrained 'Inception V3' classifier for NGS based pathogen detection

Rohita Sinha, Jennifer Clarke

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Accurate characterization of pathogenic microbes that may be present in food or clinical samples is essential in the design of appropriate intervention strategies. Inherent genomic patterns (codon-biases and rate of evolution) do simplify the classification of microbes at most taxonomic levels (genus and above), but mostly blur classification at Species/Strain levels. Hence, their classification at these finer taxonomic levels requires high-resolution genomic-data that provide SNP (Single Nucleotide Polymorphism) level precision. Existing classification methods involve either targeted amplification of sero-specific genes (serotyping and MLST) or sequencing of the entire microbial genome, both of which require extra time and resources. We present a computational approach, which harnesses the power of the metagenomic NGS-data and object-detection abilities of Convolutional Neural Networks (CNN)(Inception V3), for precise classification of pathogens by converting genomic-data (NGS-reads) into images (nucleotide-by-color). A small scale retraining (<50 images/class) of 'Inception V3' resulted in a classifier with 100% and 96% validation and test accuracies, respectively, when classifying pathogens such as Campylobacter coli/jejuni and Escherichia coli (O157:H7 and Non O157-STECs). We aim to extend this protocol to the detection of several microbes (multiple-objects) in a metagenomic image (genomic image of an entire microbial community).

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
EditorsIllhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781509030491
DOIs
StatePublished - Dec 15 2017
Event2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States
Duration: Nov 13 2017Nov 16 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Volume2017-January

Other

Other2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
CountryUnited States
CityKansas City
Period11/13/1711/16/17

Fingerprint

Pathogens
Classifiers
Technology
Metagenomics
Nucleotides
Genes
Microbial Genome
Campylobacter coli
Shiga-Toxigenic Escherichia coli
Serotyping
Campylobacter jejuni
Escherichia coli O157
Polymorphism
Codon
Escherichia coli
Single Nucleotide Polymorphism
Amplification
Color
Neural networks
Food

Keywords

  • Deep learning
  • NGS
  • Pathogen detection
  • TensorFlow

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

Cite this

Sinha, R., & Clarke, J. (2017). When technology meets technology: Retrained 'Inception V3' classifier for NGS based pathogen detection. In I. Yoo, J. H. Zheng, Y. Gong, X. T. Hu, C-R. Shyu, Y. Bromberg, J. Gao, ... D. Korkin (Eds.), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (pp. 1-5). (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2017.8217942

When technology meets technology : Retrained 'Inception V3' classifier for NGS based pathogen detection. / Sinha, Rohita; Clarke, Jennifer.

Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. ed. / Illhoi Yoo; Jane Huiru Zheng; Yang Gong; Xiaohua Tony Hu; Chi-Ren Shyu; Yana Bromberg; Jean Gao; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-5 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sinha, R & Clarke, J 2017, When technology meets technology: Retrained 'Inception V3' classifier for NGS based pathogen detection. in I Yoo, JH Zheng, Y Gong, XT Hu, C-R Shyu, Y Bromberg, J Gao & D Korkin (eds), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, Kansas City, United States, 11/13/17. https://doi.org/10.1109/BIBM.2017.8217942
Sinha R, Clarke J. When technology meets technology: Retrained 'Inception V3' classifier for NGS based pathogen detection. In Yoo I, Zheng JH, Gong Y, Hu XT, Shyu C-R, Bromberg Y, Gao J, Korkin D, editors, Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-5. (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017). https://doi.org/10.1109/BIBM.2017.8217942
Sinha, Rohita ; Clarke, Jennifer. / When technology meets technology : Retrained 'Inception V3' classifier for NGS based pathogen detection. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. editor / Illhoi Yoo ; Jane Huiru Zheng ; Yang Gong ; Xiaohua Tony Hu ; Chi-Ren Shyu ; Yana Bromberg ; Jean Gao ; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-5 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017).
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