Precise Prediction of Pathogenic Microorganisms Using 16S rRNA Gene Sequences

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

2 Scopus Citations
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Author(s)

  • Yu-An Huang
  • Zhu-Hong You
  • Pengwei Hu
  • Li-Ping Li
  • Zheng-Wei Li
  • Lei Wang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationIntelligent Computing Theories and Application
Subtitle of host publicationProceedings, Part II
EditorsDe-Shuang Huang, Kang-Hyun Jo, Zhi-Kai Huang
PublisherSpringer Nature Switzerland AG
Pages138-150
ISBN (Electronic)9783030269692
ISBN (Print)9783030269685
Publication statusPublished - Aug 2019

Publication series

NameLecture Notes in Computer Science
Volume11644 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title15th International Conference on Intelligent Computing (ICIC 2019)
PlaceChina
CityNanchang
Period3 - 6 August 2019

Abstract

Clinical observations show that human microorganisms get involved in various human biological processes. The disruption of a symbiotic balance for host-microbiota relationship is found to cause different types of human complex diseases. Discoverying the associations between microbes and the host health statuses that they affect could provide great insights into understanding the mechanisms of diseases caused by microbes. However, experimental approaches are time-consuming and expensive. Little effort has been done to develop computational models for predicting pathogenic microbes on a large scale. The prediction results yielded by such models are anticipated to boost the identification and characterization of potential human pathogenic microbes. Based on the assumption that microbes of similar characters tend to get involved in diseases of similar symptoms forming functional clusters, in this paper, we develop a group based computational model of Bayesian disease-oriented ranking for inferring the most potential microbes associated with human diseases. It is the first attempt to predict this kind of associations by using 16S rRNA gene sequences. Based on the sequence information of genes, we use two computational approaches (BLAST+ and MEGA 7) to measure how similar each pairs of microbes are from different aspects. On the other hand, the similarity of diseases is computed based on MeSH descriptors. Using the data collected from HMDAD database, the proposed model achieved AUCs of 0.9456, 0.8266, 0.8866 and 0.8926 in leave-one-out, 2-fold, 5-fold and 10-fold cross validations, respectively. Besides, we conducted a case study on colorectal carcinoma and found that 16 out of top-20 predicted microbes can be confirmed by the published literatures. The prediction result is publicly released and anticipated to help researchers to preferentially validate these promising pathogenic microbe candidates via biological experiments.

Research Area(s)

  • 16S rRNA sequence analysis, Computational prediction model, Microbe–disease associations, Microflora, Pathogenic microorganisms

Citation Format(s)

Precise Prediction of Pathogenic Microorganisms Using 16S rRNA Gene Sequences. / Huang, Yu-An; Huang, Zhi-An; You, Zhu-Hong; Hu, Pengwei; Li, Li-Ping; Li, Zheng-Wei; Wang, Lei.

Intelligent Computing Theories and Application: Proceedings, Part II. ed. / De-Shuang Huang; Kang-Hyun Jo; Zhi-Kai Huang. Springer Nature Switzerland AG, 2019. p. 138-150 (Lecture Notes in Computer Science; Vol. 11644 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review