Author response: Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals

Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict epigenome features from DNA sequence - to support inference concerning the regulatory effects of disease-associated variants. Here, we evaluate the advantages of training convolutional neural network (CNN) models on a broad set of epigenomic features collected in a single disease-relevant tissue – pancreatic islets in the case of type 2 diabetes (T2D) - as opposed to models trained on multiple human tissues. We report convergence of CNN-based metrics of regulatory function with conventional approaches to variant prioritization – genetic fine-mapping and regulatory annotation enrichment. We demonstrate that CNN-based analyses can refine association signals at T2D-associated loci and provide experimental validation for one such signal. We anticipate that these approaches will become routine in downstream analyses of GWAS.

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Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals

10.1101/760868 ◽

2019 ◽

Author(s):

Agata Wesolowska-Andersen ◽

Grace Zhuo Yu ◽

Vibe Nylander ◽

Fernando Abaitua ◽

Matthias Thurner ◽

...

Keyword(s):

Type 2 Diabetes ◽

Deep Learning ◽

Pancreatic Islets ◽

Regulatory Function ◽

Learning Models ◽

Association Analyses ◽

Regulatory Variants ◽

Regulatory Effects ◽

Genomic Regions

AbstractGenome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict epigenome features from DNA sequence - to support inference concerning the regulatory effects of disease-associated variants. Here, we evaluate the advantages of training convolutional neural network (CNN) models on a broad set of epigenomic features collected in a single disease-relevant tissue – pancreatic islets in the case of type 2 diabetes (T2D) - as opposed to models trained on multiple human tissues. We report convergence of CNN-based metrics of regulatory function with conventional approaches to variant prioritization – genetic fine-mapping and regulatory annotation enrichment. We demonstrate that CNN-based analyses can refine association signals at T2D-associated loci and provide experimental validation for one such signal. We anticipate that these approaches will become routine in downstream analyses of GWAS.

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A Novel Intelligent System for Detection of Type 2 Diabetes with Modified Loss Function and Regularization

Proceedings of the Institute for System Programming of RAS ◽

10.15514/ispras-2021-33(2)-5 ◽

2021 ◽

Vol 33 (2) ◽

pp. 93-114

Author(s):

Mallika G.C. ◽

Abeer Alsadoon ◽

Duong Thu Hang Pham ◽

Salma Hameedi Abdullah ◽

Ha Thi Mai ◽

...

Keyword(s):

Machine Learning ◽

Type 2 Diabetes ◽

Deep Learning ◽

Intelligent System ◽

Model Solution ◽

Majority Voting ◽

Ensemble Model ◽

Pima Indians ◽

Learning Models

Type 2 Diabetes (T2DM) makes up about 90% of diabetes cases, as well as tough restriction on continuous monitoring and detecting become one of key aspects in T2DM. This research aims to develop an ensemble of several machine learning and deep learning models for early detection of T2DM with high accuracy. With high diversity of models, the ensemble will provide more excessive performance than single models. Methodology: The proposed system is modified enhanced ensemble of machine learning models for T2DM prediction. It is composed of Logistic Regression, Random Forest, SVM and Deep Neural Network models to generate a modified ensemble model. Results: The output of each model in the modified ensemble is used to figure out the final output of the system. The datasets being used for these models include Practice Fusion HER, Pima Indians diabetic's data, UCI AIM94 Dataset and CA Diabetes Prevalence 2014. In comparison to the previous solutions, the proposed ensemble model solution exposes the effectiveness of accuracy, sensitivity, and specificity. It provides an accuracy of 87.5% from 83.51% in average, sensitivity of 35.8% from 29.59% as well as specificity of 98.9% from 96.27%. The processing time of the proposed model solution with 96.6ms is faster than the state-of-the-art with 97.5ms. Conclusion: The proposed modified enhanced system in this work improves the overall prediction capability of T2DM using an ensemble of several machine learning and deep learning models. A majority voting scheme utilizes the output from several models to make the final accurate prediction. Regularization function in this work is modified in order to include the regularization of all the models in ensemble, that helps prevent the overfitting and encourages the generalization capacity of the proposed system.

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Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images

Nature Biomedical Engineering ◽

10.1038/s41551-021-00745-6 ◽

2021 ◽

Cited By ~ 1

Author(s):

Kang Zhang ◽

Xiaohong Liu ◽

Jie Xu ◽

Jin Yuan ◽

Wenjia Cai ◽

...

Keyword(s):

Type 2 Diabetes ◽

Chronic Kidney Disease ◽

Deep Learning ◽

Kidney Disease ◽

Fundus Images ◽

Learning Models ◽

Retinal Fundus Images ◽

Retinal Fundus

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Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data Using Deep Learning Models

Artificial Neural Networks and Machine Learning – ICANN 2018 - Lecture Notes in Computer Science ◽

10.1007/978-3-030-01424-7_46 ◽

2018 ◽

pp. 468-478 ◽

Cited By ~ 4

Author(s):

Zakhriya Alhassan ◽

A. Stephen McGough ◽

Riyad Alshammari ◽

Tahani Daghstani ◽

David Budgen ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Time Series ◽

Type 2 Diabetes Mellitus ◽

Deep Learning ◽

Clinical Data ◽

Learning Models ◽

Diabetes Mellitus Diagnosis

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Single cell ATAC-seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures

10.1101/749283 ◽

2019 ◽

Cited By ~ 1

Author(s):

Vivek Rai ◽

Daniel X. Quang ◽

Michael R. Erdos ◽

Darren A. Cusanovich ◽

Riza M. Daza ◽

...

Keyword(s):

Type 2 Diabetes ◽

Deep Learning ◽

Single Cell ◽

Pancreatic Islets ◽

Cell Populations ◽

Open Chromatin ◽

Cell Type ◽

Genome Wide ◽

Delta Cell

ABSTRACTObjectiveType 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome wide association studies (GWAS) have identified >400 independent signals that encode genetic predisposition. More than 90% of the associated single nucleotide polymorphisms (SNPs) localize to non-coding regions and are enriched in chromatin-defined islet enhancer elements, indicating a strong transcriptional regulatory component to disease susceptibility. Pancreatic islets are a mixture of cell types that express distinct hormonal programs, and so each cell type may contribute differentially to the underlying regulatory processes that modulate T2D-associated transcriptional circuits. Existing chromatin profiling methods such as ATAC-seq and DNase-seq, applied to islets in bulk, produce aggregate profiles that mask important cellular and regulatory heterogeneity.MethodsWe present genome-wide single cell chromatin accessibility profiles in >1,600 cells derived from a human pancreatic islet sample using single-cell-combinatorial-indexing ATAC-seq (sci-ATAC-seq). We also developed a deep learning model based on the U-Net architecture to accurately predict open chromatin peak calls in rare cell populations.ResultsWe show that sci-ATAC-seq profiles allow us to deconvolve alpha, beta, and delta cell populations and identify cell-type-specific regulatory signatures underlying T2D. Particularly, we find that T2D GWAS SNPs are significantly enriched in beta cell-specific and cross cell-type shared islet open chromatin, but not in alpha or delta cell-specific open chromatin. We also demonstrate, using less abundant delta cells, that deep-learning models can improve signal recovery and feature reconstruction of rarer cell populations. Finally, we use co-accessibility measures to nominate the cell-specific target genes at 104 non-coding T2D GWAS signals.ConclusionsCollectively, we identify the islet cell type of action across genetic signals of T2D predisposition and provide higher-resolution mechanistic insights into genetically encoded risk pathways.

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