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|Title:||App for Gene Analysis Modules|
|Department:||Department of Computer Science|
|Supervisor:||Supervisor: Dr. Li, Shuaicheng; First Reader: Dr. Kwok, Lam For; Second Reader: Prof. Wang, Lusheng|
|Abstract:||Development of big data techniques triggers the popularity of genetic technologies. Gene, as the basic physical and functional unit of heredity, contains most of the genetic information of a person. A man's appearance or the probability of getting a certain illness can be estimated according to his gene. The genome-wide association study (GWAS, or GWA study) already pointed out the associations between the singlenucleotide polymorphisms (SNP) and human traits. These findings are valuable for the public to explore the information contained in their gene. Moreover, people can be aware of their risk of having a certain disease and take actions to prevent it in advance. The challenges for the public to utilize the research findings come from the deficiency of bioinformatics knowledge and the fast update speed of research. Inspired by the current situation, my final year project comes up with a solution which is a gene analysis application that involves both users and gene module developers. Considering the deficiency of professional knowledge of the public, the findings in latest researches are interpreted into gene analysis modules which are easy to access even for those with little knowledge of bioinformatics. In order to adapt to the fast update speed of new studies, modules are designed to be flexible for updating by developers. Currently, the system provides an integrated flow for users from uploading gene sequence file to viewing the interpretation reports of genetic information in the mobile application. As one of the main components, currently available gene analysis modules covers the analyses on inherited characteristics, health risk, hereditary diseases and ancestry. To ensure the accuracy of the analysis results, mathematics prediction models including probabilities model and multinomial logistic regression model are also adopted in the project.|
|Appears in Collections:||Computer Science - Undergraduate Final Year Projects |
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