# Prediction of a Gene Regulatory Network from Gene Expression Profiles With Linear Regression and Pearson Correlation Coefficient

arXiv: https://arxiv.org/abs/1805.01506 · HTML: https://mlsanity.com/paper/1805.01506/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2018.

## Built on by (most cited first, 1 of 1)

- [Breast Cancer Histopathology Image based Gene Expression Prediction using Spatial Transcriptomics data and Deep Learning](https://mlsanity.com/paper/2303.09987/) (2023)

## Abstract (arXiv)

Reconstruction of gene regulatory networks is the process of identifying gene dependency from gene expression profile through some computation techniques. In our human body, though all cells pose similar genetic material but the activation state may vary. This variation in the activation of genes helps researchers to understand more about the function of the cells. Researchers get insight about diseases like mental illness, infectious disease, cancer disease and heart disease from microarray technology, etc. In this study, a cancer-specific gene regulatory network has been constructed using a simple and novel machine learning approach. In First Step, linear regression algorithm provided us the significant genes those expressed themselves differently. Next, regulatory relationships between the identified genes has been computed using Pearson correlation coefficient. Finally, the obtained results have been validated with the available databases and literatures. We can identify the hub genes and can be targeted for the cancer diagnosis.