Distributed gene clinical decision support system based on cloud computing

  • Bo Xu
  • , Changlong Li
  • , Hang Zhuang
  • , Jiali Wang
  • , Qingfeng Wang
  • , Chao Wang
  • , Xuehai Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The clinical decision support system can effectively solve the limitations of doctors' knowledge, reduce misdiagnosis and help enhance health. The traditional genetic data storage and analysis technology based on the stand-alone environment have limited scalability, which has been difficult to meet the computational requirements of rapid genetic data growth. In this paper, we propose a distributed gene clinical decision support system, which is named as GCDSS. We implemented a prototype based on cloud computing. To speed up the data processing of GCDSS, we present a novel distributed read mapping algorithm CloudBWA that leverages batch processing strategy to map reads on Apache Spark. Evaluations show that GCDSS and its component CloudBWA achieve outstanding performance and excellent scalability. Compared with distributed algorithms, CloudBWA achieves up to 2.63 times speedup over SparkBWA.

Original languageEnglish
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.
Pages485-490
Number of pages6
ISBN (Electronic)9781509030491
DOIs
StatePublished - 15 Dec 2017
Externally publishedYes
Event2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States
Duration: 13 Nov 201716 Nov 2017

Publication series

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

Conference

Conference2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Country/TerritoryUnited States
CityKansas City
Period13/11/1716/11/17

Keywords

  • Alluxio
  • Cloud computing
  • Gene Clinical Decision Support System
  • Genetic data analysis
  • Read mapping
  • Spark

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