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In-vitro Mutagenesis

In-vitro Mutagenesis

To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.

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Related Experiment Video

Updated: Dec 8, 2025

Combining Optogenetics with Artificial microRNAs to Characterize the Effects of Gene Knockdown on Presynaptic Function within Intact Neuronal Circuits

Combining Optogenetics with Artificial microRNAs to Characterize the Effects of Gene Knockdown on Presynaptic Function within Intact Neuronal Circuits

Published on: March 14, 2018

An embedded gene selection method using knockoffs optimizing neural network.

Juncheng Guo^1,2,3, Min Jin⁴, Yuanyuan Chen⁴

¹Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.

BMC Bioinformatics

|September 23, 2020

Summary

This summary is machine-generated.

This study introduces an interpretable neural network for gene selection, identifying key genes influencing traits. The method effectively selects important genes from complex biological data.

Keywords:

Gene mining Knockoffs Maize Neural network Nonlinear data

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Gene selection identifies discriminant genes from expression profiles.
Effective gene selection for phenotypic traits is crucial in biology.
Neural networks offer flexible feature capture for nonlinear data.

Purpose of the Study:

To propose an embedded gene selection method using neural networks.
To enhance the interpretability of neural network models in gene selection.
To identify key genes influencing biological decisions.

Main Methods:

Utilizing neural networks for automatic feature extraction.
Calculating weight coefficients post-training to identify important genes.
Employing knockoff feature construction for model interpretability and feature competition.

Main Results:

Verification using maize carotenoids, tocopherol methyltransferase, raffinose family oligosaccharides, and human breast cancer datasets.
Demonstrated effectiveness in detecting key genes within complex biological datasets.

Conclusions:

The knockoffs optimizing neural network method outperforms existing algorithms.
This approach is particularly effective for nonlinear gene expression and phenotype data analysis.