ArticleScientific reports2018
Heterogeneity Analysis and Diagnosis of Complex Diseases Based on Deep Learning Method.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Mixed prototype correction for causal inference in medical image classification.Scientific reports · 2025Article
- Considerations in the search for epistasis.Genome biology · 2024Review
- Microfluidics-based patient-derived disease detection tool for deep learning-assisted precision medicine.Biomicrofluidics · 2024Article
- Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical Data.Translational vision science & technology · 2023Article
- Article
- Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.Genetic epidemiology · 2022Review
- Empirical mode decomposition using deep learning model for financial market forecasting.PeerJ. Computer science · 2022Article
- Diagnosis of Leukaemia in Blood Slides Based on a Fine-Tuned and Highly Generalisable Deep Learning Model.Sensors (Basel, Switzerland) · 2021Article
- A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning.BioMed research international · 2021Article
- Extracellular Vesicle Identification Using Label-Free Surface-Enhanced Raman Spectroscopy: Detection and Signal Analysis Strategies.Molecules (Basel, Switzerland) · 2020Review
- Uncovering the prognostic gene signatures for the improvement of risk stratification in cancers by using deep learning algorithm coupled with wavelet transform.BMC bioinformatics · 2020Article
- Photosynthetic protein classification using genome neighborhood-based machine learning feature.Scientific reports · 2020Article
- Genetics of Congenital Heart Disease.Biomolecules · 2019Review
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4 authors.
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Abstract
Understanding genetic mechanism of complex diseases is a serious challenge. Existing methods often neglect the heterogeneity phenomenon of complex diseases, resulting in lack of power or low reproducibility. Addressing heterogeneity when detecting epistatic single nucleotide polymorphisms (SNPs) can enhance the power of association studies and improve prediction performance of complex diseases diagnosis. In this study, we propose a three-stage framework including epistasis detection, clustering and prediction to address both epistasis and heterogeneity of complex diseases based on deep learning method. The epistasis detection stage applies a multi-objective optimization method to find several candidate sets of epistatic SNPs which contribute to different subtypes of complex diseases. Then, a K-means clustering algorithm is used to define subtypes of the case group. Finally, a deep learning model has been trained for disease prediction based on graphics processing unit (GPU). Experimental results on pure and heterogeneous datasets show that our method has potential practicality and can serve as a possible alternative to other methods. Therefore, when epistasis and heterogeneity exist at the same time, our method is especially suitable for diagnosis of complex diseases.
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