Title: Developmental trend of microfluidic chip and biosensor technologies and the integration mode with machine learning model and wearable device
Authors: Ding Tang; Dingbang Huang; Zhongbao Yang; Qingge Ji
Addresses: Biomedical Engineering, School of Engineering, Sun Yat-Sen University, Guangzhou, China ' School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China ' Mathematics and Applied Mathematics, School of Mathematics, Sun Yat-Sen University, Guangzhou, China ' School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
Abstract: In this paper, we analyse the developmental trend of the microfluidic chip and biosensor technologies and the integration mode with machine learning model and wearable device. Bioinformatics is a use of computer technology and information theory methods for the protein and nucleic acid sequence and many kinds of general biological information acquisition, processing, storage, transmission, retrieval, the analysis and the interpretation of the science. Our research discusses the combination modes with neural network, support vector machine, genetic algorithm and the decision tree to provide systematic analysis on the issues. Then, we analyse developmental trend of microfluidic chip and the wearable devices to serve as the nucleus of this research. We establish a database group of scientists are specialists in the field of the bioinformatics, and those who get the biology knowledge from these data are in terms of the special biology experts. In the experiment part, we analyse the systematic architecture of mode that will be basis of further research. The experiment result reflects the systematic framework of proposed general structure of the bioinformatics-related hardware topology.
Keywords: microfluidic chips; biosensors; integration mode; machine learning; wearable devices; microfluidics; bioinformatics; neural networks; support vector machines; SVM; genetic algorithms; hardware topology.
International Journal of Biomedical Engineering and Technology, 2017 Vol.23 No.2/3/4, pp.281 - 302
Received: 09 Jun 2016
Accepted: 26 Aug 2016
Published online: 25 Feb 2017 *