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

arXiv: https://arxiv.org/abs/2505.19740 · HTML: https://mlsanity.com/paper/2505.19740/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 3 of 3)

- [CM-Net: Concentric Mask based Arbitrary-Shaped Text Detection](https://mlsanity.com/paper/2011.14714/) (2020)
- [FTS: A Framework to Find a Faithful TimeSieve](https://mlsanity.com/paper/2405.19647/) (2024)
- [Optimization of Transformer heart disease prediction model based on particle swarm optimization algorithm](https://mlsanity.com/paper/2412.02801/) (2024)

## Abstract (arXiv)

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.