Deep learning-based identification of genetic variants: application to Alzheimer’s disease classification

Jo, Taeho; Nho, Kwangsik; Bice, Paula J.; Saykin, Andrew J.

doi:10.1093/bib/bbac022

Cited by 37 publications

(32 citation statements)

References 76 publications

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“…Based on our XGBoost algorithm model, the results are quite di erent when applied to di erent datasets. e important features correspond to the following AAL regions, i.e., superior parietal gyrus (59), parahippocampal gyrus (40), right cerebellum 7b (102), left cerebellum 6 (99), left postcentral gyrus (57), right inferior frontal gyrus, opercular part (12), right middle temporal gyrus (86), left middle temporal gyrus (85), left temporal pole: middle temporal gyrus (87), left cerebellum 10 (107), vermis 6 (112), left cerebellum 9 (105), right amygdala (42), and left supramarginal gyrus (63). We counted the top 20 features in ve SHAP algorithm graphs.…”

Section: Discussionmentioning

confidence: 99%

“…e resulting accuracy by the XGBoost algorithm is 0.76 ± 0.01, and the AUC is 0.86 ± 0.01. Jo et al [59] proposed a novel three-step approach (SWAT-CNN) for the identi cation of genetic variants using deep learning to identify phenotyperelated single nucleotide polymorphisms (SNPs) that can be applied to develop accurate disease classi cation models, and the AUC of this model is 0.82. A machine learning framework proposed in this paper for MCI detection achieved an accuracy of 65.14% when using the mPerAF dataset.…”

Section: Discussionmentioning

confidence: 99%

“…SHAP force plot (Figure 3) shows the interpretability of a single model prediction used for error analysis to identify an interpretation for a particular instance prediction. e output value of the XGBoost classi er using DC datasets was −0.10, and the characteristics of the left insula (29), right middle frontal gyrus (8), and right cuneus (46) positively correlated with the output value, whereas the characteristics of the right cerebellum 7b (102), left superior parietal gyrus (59), right superior frontal gyrus, and orbital part (6) negatively correlated with the output value.…”

Section: Model Interpretation: Shapley Additive Explanations (Shap) A...mentioning

confidence: 99%

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Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning

et al. 2022

Computational Intelligence and Neuroscience

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The combination and integration of multimodal imaging and clinical markers have introduced numerous classifiers to improve diagnostic accuracy in detecting and predicting AD; however, many studies cannot ensure the homogeneity of data sets and consistency of results. In our study, the XGBoost algorithm was used to classify mild cognitive impairment (MCI) and normal control (NC) populations through five rs-fMRI analysis datasets. Shapley Additive exPlanations (SHAP) is used to analyze the interpretability of the model. The highest accuracy for diagnosing MCI was 65.14% (using the mPerAF dataset). The characteristics of the left insula, right middle frontal gyrus, and right cuneus correlated positively with the output value using DC datasets. The characteristics of left cerebellum 6, right inferior frontal gyrus, opercular part, and vermis 6 correlated positively with the output value using fALFF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, left temporal pole, and middle temporal gyrus correlated positively with the output value using mPerAF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, and left hippocampus correlated positively with the output value using PerAF datasets. The characteristics of left cerebellum 9, vermis 9, and right precentral gyrus, right amygdala, and left middle occipital gyrus correlated positively with the output value using Wavelet-ALFF datasets. We found that the XGBoost algorithm constructed from rs-fMRI data is effective for the diagnosis and classification of MCI. The accuracy rates obtained by different rs-fMRI data analysis methods are similar, but the important features are different and involve multiple brain regions, which suggests that MCI may have a negative impact on brain function.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Model Interpretation: Shapley Additive Explanations (Shap) A...mentioning

confidence: 99%

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Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning

et al. 2022

Computational Intelligence and Neuroscience

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“…Classification of cases and controls for certain diseases is also performed using DNA methylation data. Examples of machine learning applications using epigenetic data include classification of coronary heart disease, neurodevelopmental syndromes, schizophrenia, Alzheimer's disease, psychiatric disorders and others [33][34][35][36][37][38][39].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Disease classification for whole blood DNA methylation: meta-analysis, missing values imputation, and XAI

Kalyakulina

Yusipov

Bacalini

et al. 2022

Preprint

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Background: DNA methylation has a significant effect on gene expression and can be associated with various diseases. Meta-analysis of available DNA methylation datasets requires development of a specific pipeline for joint data processing. Results: We propose a comprehensive approach of combined DNA methylation datasets to classify controls and patients. The solution includes data harmonization, construction of machine learning classification models, dimensionality reduction of models, imputation of missing values, and explanation of model predictions by explainable artificial intelligence (XAI) algorithms. We show that harmonization can improve classification accuracy by up to 20% when preprocessing methods of the training and test datasets are different. The best accuracy results were obtained with tree ensembles, reaching above 95% for Parkinson's disease. Dimensionality reduction can substantially decrease the number of features, without detriment to the classification accuracy. The best imputation methods achieve almost the same classification accuracy for data with missing values as for the original data. Explainable artificial intelligence approaches have allowed us to explain model predictions from both populational and individual perspectives. Conclusions: We propose a methodologically valid and comprehensive approach to the classification of healthy individuals and patients with various diseases based on whole blood DNA methylation data using Parkinson's disease and schizophrenia as examples. The proposed algorithm works better for the former pathology, characterized by a complex set of symptoms. It allows to solve data harmonization problems for meta-analysis of many different datasets, impute missing values, and build classification models of small dimensionality.

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“…Different approaches [2][3][4] like polygenic risk score (PRS) and wide-range of linear models have been proposed for risk prediction of complex diseases based on the genotype-phenotype associations for variants identified by GWAS. More recently, with increased data availability, nonlinear methods like deep-learning 5 have been considered for constructing prediction models 6 .…”

Section: Introductionmentioning

confidence: 99%

Improving genetic risk prediction across diverse population by disentangling ancestry representations

Gyawali

Guen

Liu

et al. 2022

Preprint

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Risk prediction models using genetic data have seen increasing traction in genomics. However, most of the polygenic risk models were developed using data from participants with similar (mostly European) ancestry. This can lead to biases in the risk predictors resulting in poor generalization when applied to minority populations and admixed individuals such as African Americans. To address this bias, largely due to the prediction models being confounded by the underlying population structure, we propose a novel deep-learning framework that leverages data from diverse population and disentangles ancestry from the phenotype-relevant information in its representation. The ancestry disentangled representation can be used to build risk predictors that perform better across minority populations. We applied the proposed method to the analysis of Alzheimer’s disease genetics. Comparing with standard linear and nonlinear risk prediction methods, the proposed method substantially improves risk prediction in minority populations, including admixed individuals, without needing self-reported ancestry information.

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Deep learning-based identification of genetic variants: application to Alzheimer’s disease classification

Cited by 37 publications

References 76 publications

Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning

Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning

Disease classification for whole blood DNA methylation: meta-analysis, missing values imputation, and XAI

Improving genetic risk prediction across diverse population by disentangling ancestry representations

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