2025/05/26 by Weichen Si, Si, Weichen, Youming Ou +3
Biochemistry, Genetics and Molecular Biology · #FOS: Computer and information sciences #Feature (linguistics) #Feature extraction #Gene expression and cancer classification #Genetic algorithm #Genetics, Bioinformatics, and Biomedical Research #Key (lock) #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #Noise (video) #Noise reduction #Pattern recognition (psychology) #Reduction (mathematics)
paper · pdf · doi:10.48550/arxiv.2505.19740
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/05/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.