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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Gene mutation estimations via mutual information and Ewens sampling based CNN & machine learning algorithms.

¹Department of Mathematics and State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, People's Republic of China.

Journal of Applied Statistics

|September 10, 2025

Summary

This summary is machine-generated.

This study introduces a novel convolutional neural network (CNN) and machine learning approach for estimating gene mutation rates. This method optimizes protein production, aiding gene editing and protein structure prediction.

Keywords:

Ewens sampling Gene mutation rate convolutional neural network (CNN)machine learning multi-input multi-output (MIMO) mutual information stochastic gradient

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Identifying DNA Mutations in Purified Hematopoietic Stem/Progenitor Cells

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Published on: February 24, 2014

Area of Science:

Computational Biology
Bioinformatics
Machine Learning

Background:

Accurate gene mutation rate estimation is crucial for understanding protein production.
Current methods face challenges in balancing mutation rates for gene editing and structure prediction.

Purpose of the Study:

To develop a systematic methodology for gene mutation rate estimation using CNN and machine learning.
To address a two-stage optimization problem for balancing mutation rates during protein production.
To facilitate gene editing and protein structure prediction.

Main Methods:

Development of a CNN and two machine learning algorithms.
Utilizing mutual information, Ewens sampling, and Kuhn-Tucker conditions with boundary constraints.
Incorporating multi-input multi-output (MIMO) mutual information and codon optimization.

Main Results:

A numerical optimization scheme for CNNs was developed.
The algorithms were numerically implemented with mathematically proved convergence and optimality.
A real-world data implementation demonstrated the study's utility.

Conclusions:

The developed CNN and machine learning approach effectively estimates gene mutation rates.
This methodology optimizes protein production, reducing computational complexity for structure prediction.
The study provides a robust framework for gene editing and protein structure prediction applications.