Prediction of a gene regulatory network from gene expression Profiles with Linear Regression and Pearson Correlation Coefficient

Hasan, Mehedi; Nobin, Shakhawat Ahmmed

dc.contributor.author	Hasan, Mehedi
dc.contributor.author	Nobin, Shakhawat Ahmmed
dc.date.accessioned	2021-09-13T09:25:46Z
dc.date.available	2021-09-13T09:25:46Z
dc.date.issued	2014-11-15
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dc.identifier.uri	http://hdl.handle.net/123456789/986
dc.description	Supervised by Tareque Mohmud Chowdhury, Assistant Professor, Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh.	en_US
dc.description.abstract	Reconstruction of gene regulatory networks is the process of identifying gene dependency from gene expression profile through some computation techniques. In our human body, all cells contain same genetic material but the same genes may or may not be active. This variation in the activation of genes assists researchers to understand more about the function of the cells. Microarray technology helps researchers to get insight about many different diseases such as various cancer disease, heart disease, mental illness, and infectious disease, etc. In this study, a cancer-specific gene regulatory network has been constructed using a simple and novel machine learning approach. First, significant genes differentially expressing them self in the disease condition has been identified using linear regression algorithm. Next, regulatory relationships between the identified genes has been computed using Pearson correlation coefficient. Finally The obtained results has been validated with the available databases and literatures. We can identify the hub genes and can be targeted for the cancer diagnosis.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Prediction of a gene regulatory network from gene expression Profiles with Linear Regression and Pearson Correlation Coefficient	en_US
dc.type	Thesis	en_US