Human-computer interactions for systems identification of gene regulatory networks

• Main Author: Zhang, Mengxuan
• Other Authors: Zheng Jie
• Format: Final Year Project
• Language: English
• Published: 2015
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/62886
• Institution: Nanyang Technological University

Nowadays, information technology has reached out into many different areas to support various applications, researches and studies. Among them, gene network inference takes a large portion .There are plenty of software applications developed to analyse Gene Regulatory Networks (GRN) and Meta-GRN is one of them with its useful functionalities and interactive Graphical User Interface (GUI). In this project, functionality of Meta-GRN has been largely enhanced by adding more features into it. With these integrated features, the efficiency and effectiveness of the application will be increased significantly. Important features include allowing users to modify gene regulatory network according to their preferences, by editing nodes (genes) or edges (interactions). Another essential feature we have developed is to evaluate the modified gene regulatory network developed by users and give users some feedback interactively. In this way, users will have a direct view about how their modified network impacts the simulation of gene expression data matrix. Moreover, principal component analysis (PCA) will be applied to gene expression data matrix to reduce potential noises. Furthermore, a new function allows users to compare standard networks with modified or inferred networks directly by calculating F-score of the interactions. At the same time, user logic is refined to improve usability and reduce overhead. Last but not least, to help users explore the full functionalities of Meta-GRN, a very detailed user manual is created and recorded for future deployment purpose. These new features and documentation will offer great help for users to gain further understanding about the functions of Meta-GRN.