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Yang H.-M.,Shenzhen Huada Gene Research Institute
Journal of Xi'an Jiaotong University (Medical Sciences) | Year: 2015

Precision medicine is deliberate orchestrated by Obama's advisers, and it is based on DNA and human genome project. Double helix structure discovery and the human genome project completed are the first and the second revolution of life science. DNA sequencing and genome technology which drive precision medicine have a far-reaching influence. ©, 2015, Editorial Board of Journal of Xi'an Jiaotong University(Medical Sciences). All right reserved. Source


Cao Z.-B.,South China University of Technology | Dong S.-B.,South China University of Technology | Wang B.-Q.,Shenzhen Huada Gene Research Institute | Zuo L.-Y.,South China University of Technology
Ruan Jian Xue Bao/Journal of Software | Year: 2014

Biological gene sequencing is one of the most common high-performance computing tasks in Bioinformatics analysis. This paper aims to find the main workload characteristics of biological gene sequence trace (BGST) and construct a general model to analyze the biological gene sequence (BGS), which can be used in high-performance computing scheduling and performance optimization with the BGS. The study mainly analyzes the job arrival, runtime and parallelism characteristics in BGST. Based on the analysis, it constructs several local models with exponential, Gamma, Gaussian and linear regression, then combines all the local models into a final model. The experimental results obtained by applying two general evaluation methods show that the new model has uniform distributed trend with BGST, which demonstrates the good versatility of the model. ©2014 ISCAS. Source


Zhang F.,National University of Defense Technology | Liao X.-K.,National University of Defense Technology | Peng S.-L.,National University of Defense Technology | Zhu X.-Q.,National University of Defense Technology | And 2 more authors.
Ruan Jian Xue Bao/Journal of Software | Year: 2014

SGA is a tool based on string graph theory for DNA sequence de novo assembly. In this paper, the sequence de novo assembly problem based on SGA is proved to be an NP-complete problem, and detailed analysis on SGA is provided. According to the result, SGA outperforms other similar tools in memory consumption, but cost much more on time in which 60%~70% is spent by index construction. To tackle these issues, this paper introduces a deep parallel optimization strategy, and implements a Tianhe-2 architecture oriented parallel framework. Experiments are carried out on different data sizes on ordinary cluster and Tianhe-2. For data of small size, the optimized solution is 3.06 times as fast as before, and for data of medium size, it's 1.60 times. The results demonstrate the evident overall improvement and the linear scalability for parallel FM-index construction. This study can be beneficial to the optimization research of other relevant issues, and it also affirms the powerful computing ability of Tianhe-2 as a useful tool in life sciences research. ©2014 ISCAS. Source


Liu J.,South China University of Technology | Zhang B.-L.,South China University of Technology | Sun C.-L.,South China University of Technology | Wang J.,Shenzhen Huada Gene Research Institute | And 2 more authors.
Acta Biochimica et Biophysica Sinica | Year: 2016

High mobility group box1 (HMGB1), as a damage-associated inflammatory factor, contributes to the pathogenesis of numerous chronic inflammatory and autoimmune diseases. In this study, we explored the role of HMGB1 in CDI (Clostridium difficile infection) by in vivo and in vitro experiments. Our results showed that HMGB1 might play an important role in the acute inflammatory responses to C. difficile toxin A (TcdA), affect early inflammatory factors, and induce inflammation via the HMGB1-TLR4 pathway. Our study provides the essential information for better understanding the molecular mechanisms of CDI and the potential new therapeutic strategies for the treatment of this infection. © The Author 2016. Source


Chi Z.,Beijing University of Technology | Zhang S.,Beijing University of Technology | Wang Y.,Shenzhen Huada Gene Research Institute | Yang L.,Beijing University of Technology | And 2 more authors.
Technology and Health Care | Year: 2016

BACKGROUND: Gestational diabetes mellitus (GDM) is not easily detected. The difficulty in detecting GDM is largely due to the late onset of clinical symptoms as well as the various complications that result from GDM [1]. OBJECTIVE: GDM greatly influences both mother and child. Therefore, the purpose of this study was to reduce the morbidity of GDM. METHODS: In this study, risk factors that influence GDM were selected through statistical analysis. Multivariable logistic regression analysis was used to obtain the regression equation and Odds Ratio (OR) value. The risk score of each factor was obtained according to the OR value. RESULTS: The score of every pregnant woman could be very intuitively used to show the risk of getting GDM. CONCLUSION: Through the above methods, a comprehensive risk evaluation method of detecting GDM was developed. © 2016 - IOS Press and the authors. All rights reserved. Source

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