Machine Learning Algorithm for Noise Reduction and Disease-Causing Gene Feature Extraction in Gene Sequencing Data

TL;DR

This paper introduces DeepSeqDenoise, a machine learning method combining CNN and RNN for noise reduction and gene feature extraction, achieving high accuracy in identifying disease-causing genes from sequencing data.

Contribution

The study presents a novel deep learning approach that enhances noise reduction and gene prediction accuracy in gene sequencing analysis.

Findings

Signal-to-noise ratio improved by 9.4 dB

94.3% accuracy in disease gene prediction

Identified 57 new candidate disease-causing genes

Abstract

In this study, we propose a machine learning-based method for noise reduction and disease-causing gene feature extraction in gene sequencing DeepSeqDenoise algorithm combines CNN and RNN to effectively remove the sequencing noise, and improves the signal-to-noise ratio by 9.4 dB. We screened 17 key features by feature engineering, and constructed an integrated learning model to predict disease-causing genes with 94.3% accuracy. We successfully identified 57 new candidate disease-causing genes in a cardiovascular disease cohort validation, and detected 3 missed variants in clinical applications. The method significantly outperforms existing tools and provides strong support for accurate diagnosis of genetic diseases.