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MRI-based mild cognitive impairment and Alzheimer's disease classification using an algorithm of combination of variational autoencoder and other machine learning classifiers

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JOURNAL OF ALZHEIMERS DISEASE REPORTS 2024年第1期8卷 1434-1452页

作者： Bit, Subhrangshu Dey, Pritam Maji, Arnab Khan, Tapan K. BioImaginix LLC 410 Mallard Run Morgantown WV 26508 USA Indian Inst Technol Dept Chem Kanpur Uttar Pradesh India

Background: Correctly diagnosing mild cognitive impairment (MCI) and Alzheimer's disease (AD) is important for patient selection in drug discovery. Research outcomes on stage diagnosis using neuroimages combined with cerebrospinal fluid and genetic biomarkers are expensive and time-consuming. Only structural magnetic resonance imaging (sMRI) scans from two internationally recognized datasets are employed as input as well as test and independent validation to determine the classification of dementia by the machine learning algorithm. Objective: We extract the reduced dimensional latent feature vector from the sMRI scans using a variational autoencoder (VAE). The objective is to classify AD, MCI, and control (CN) using MRI and without any other information. Methods: The extracted feature vectors from MRI scans by VAE are used as input conditions for different advanced machine-learning classifiers. Classification of AD/CN/MCI are conducted using the output of VAE from MRI images and different artificial intelligence/machine learning classifier models in two cohorts. Results: Using only MRI scans, the primary goal of the study is to test the ability to classify AD from CN and MCI cases. The current study achieved classification accuracies of AD versus CN 75.45% (F1-score=79.52%), AD versus MCI 81.41% (F1-Score=87.06%), and autopsy-confirmed AD versus MCI 92.75% (F1-Score=95.52%) in test sets and AD versus CN 86.16% (F1-score=92.03%) and AD versus MCI 70.03% (F1-Score=82.1%) in validation data set. Conclusions: By overcoming the data leakage problem, the autopsy-confirmed machine learning classification model is tested in two independent cohorts. External validation by an independent cohort improved the quality and novelty of the classification algorithm.

关键词： Alzheimer's disease extra tree light gradient boosting model machine learning magnetic resonance imaging mild cognitive impairment random forest support vector machine linear kernel variational autoencoder XGB classifier

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Contextual anomaly detection for high-dimensional data using Dirichlet process variational autoencoder

A I I E Transactions

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A I I E Transactions 2023年第5期55卷 433-444页

作者： Hyojoong Kim Heeyoung Kim Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

Due to recent advances in sensing technologies, response measurements of various sensors are frequently used for system monitoring purposes. However, response data are often affected by some contextual variables, such as equipment settings and time, resulting in different patterns, even when the system is in the normal state. In this case, anomaly detection methods that do not consider contextual variables may be unable to distinguish between abnormal and normal patterns of the response data affected by the contextual variables. Motivated by this problem, we propose a method for contextual anomaly detection, particularly in the case where the response and contextual variables are both high-dimensional and complex. The proposed method is based on variational autoencoders (VAEs), which are neural-network-based generative models suitable for modeling high-dimensional and complex data. The proposed method combines two VAEs: one for response variables and the other for contextual variables. Specifically, in the latent space of the VAE for contextual variables, we model the latent variables using a Dirichlet process Gaussian mixture model. Consequently, the effects of the contextual variables can be modeled using several clusters, each representing a different contextual environment. The latent contextual variables are then used as additional inputs to the other VAE’s decoder for reconstructing response data from their latent representations. We then detect the anomalies based on the negative reconstruction loss of a new response observation. The effectiveness of the proposed method is demonstrated using several benchmark datasets and a case study based on a global tire company.

关键词： Contextual anomaly detection Dirichlet process mixture model high-dimensional data variational autoencoder

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Dimensionality reduction and visualization of single-cell RNA-seq data with an improved deep variational autoencoder

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BRIEFINGS IN BIOINFORMATICS 2023年第3期24卷 bbad152-bbad152页

作者： Jiang, Jing Xu, Junlin Liu, Yuansheng Song, Bosheng Guo, Xiulan Zeng, Xiangxiang Zou, Quan Hunan Univ Changsha Peoples R China Zhangjiajie Peoples Hosp Zhangjiajie Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu Peoples R China

Single-cell RNA sequencing (scRNA-seq) is a revolutionary breakthrough that determines the precise gene expressions on individual cells and deciphers cell heterogeneity and subpopulations. However, scRNA-seq data are much noisier than traditional high-throughput RNA-seq data because of technical limitations, leading to many scRNA-seq data studies about dimensionality reduction and visualization remaining at the basic data-stacking stage. In this study, we propose an improved variational autoencoder model (termed DREAM) for dimensionality reduction and a visual analysis of scRNA-seq data. Here, DREAM combines the variational autoencoder and Gaussian mixture model for cell type identification, meanwhile explicitly solving 'dropout' events by introducing the zero-inflated layer to obtain the low-dimensional representation that describes the changes in the original scRNA-seq dataset. Benchmarking comparisons across nine scRNA-seq datasets show that DREAM outperforms four state-of-the-art methods on average. Moreover, we prove that DREAM can accurately capture the expression dynamics of human preimplantation embryonic development.

关键词： dropout variational autoencoder visualization dimensionality reduction single-cell RNA-seq

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Spatiotemporal trajectories in resting-state FMRI revealed by convolutional variational autoencoder

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NEUROIMAGE 2021年 244卷 118588-118588页

作者： Zhang, Xiaodi Maltbie, Eric A. Keilholz, Shella D. Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA 30322 USA Emory Univ Hlth Sci Res Bldg1760 Haygood DrSuiteW200 Atlanta GA 30322 USA

Recent resting-state fMRI studies have shown that brain activity exhibits temporal variations in functional connectivity by using various approaches including sliding window correlation, co-activation patterns, independent component analysis, quasi-periodic patterns, and hidden Markov models. These methods often model the brain activity as a discretized hopping among several brain states that are defined by the spatial configurations of network activity. However, the discretized states are merely a simplification of what is likely to be a continuous process, where each network evolves over time following its unique path. To model these characteristic spatiotemporal trajectories, we trained a variational autoencoder using rs-fMRI data and evaluated the spatiotemporal features of the latent variables obtained from the trained networks. Our results suggest that there are a relatively small number of approximately orthogonal whole-brain spatiotemporal patterns that capture the most prominent features of rs-fMRI data, which can serve as the building blocks to construct all possible spatiotemporal dynamics in resting state fMRI. These spatiotemporal patterns provide insight into how activity flows across the brain in concordance with known network structures and functional connectivity gradients.

关键词： Resting state fMRI Spatiotemporal dynamics Deep learning variational autoencoder Resting state networks

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Crash data augmentation using variational autoencoder

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ACCIDENT ANALYSIS AND PREVENTION 2021年 151卷 105950-105950页

作者： Islam, Zubayer Abdel-Aty, Mohamed Cai, Qing Yuan, Jinghui Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA

In this paper, we present a data augmentation technique to reproduce crash data. The dataset comprising crash and non-crash events are extremely imbalanced. For instance, the dataset used in this paper consists of only 625 crash events for over 6.5 million non-crash events. Thus, learning algorithms tend to perform poorly on these datasets. We have used variational autoencoder to encode all the events into a latent space. After training, the model could successfully separate crash and non-crash events. To generate data, we sampled from the latent space containing crash data. The generated data was compared with the real data from different statistical aspects. t-Test, Levene-test and Kolmogrove Smirnov test showed that the generated data was statistically similar to the real data. It was also compared to some of the minority oversampling techniques like SMOTE and ADASYN as well as the GAN framework for generating data. Crash prediction models based on Logistic Regression (LR), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were used to compare the generated data from the different oversampling techniques. Overall, variational autoencoder (VAE) showed excellent results compared to the other data augmentation methods. Specificity is improved by 8% and 4% for VAE-LR and VAE-SVM respectively when compared to SMOTE while the sensitivity is improved by 6% and 5% when compared to ADASYN. Moreover, VAE generated data also helps to overcome the overfitting problem in SMOTE and ADASYN since there is flexibility in choosing the decision boundary.

关键词： variational autoencoder Data augmentation Crash prediction

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Geophysical Inversion Using a variational autoencoder to Model an Assembled Spatial Prior Uncertainty

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JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 2022年第3期127卷 e2021JB022581-e2021JB022581页

作者： Lopez-Alvis, J. Nguyen, F. Looms, M. C. Hermans, T. Univ Liege Urban & Environm Engn Appl Geophys Liege Belgium Univ Copenhagen Dept Geosci & Nat Resource Management Copenhagen Denmark Univ Ghent Dept Geol Ghent Belgium

Prior information regarding subsurface spatial patterns may be used in geophysical inversion to obtain realistic subsurface models. Field experiments require prior information with sufficiently diverse patterns to accurately estimate the spatial distribution of geophysical properties in the sensed subsurface domain. A variational autoencoder (VAE) provides a way to assemble all patterns deemed possible in a single prior distribution. Such patterns may include those defined by different base training images and also their perturbed versions, for example, those resulting from geologically consistent operations such as erosion/dilation, local deformation, and intrafacies variability. Once the VAE is trained, inversion may be done in the latent space which ensures that inverted models have the patterns defined by the assembled prior. Gradient-based inversion with both a synthetic and a field case of cross-borehole GPR traveltime data shows that using the VAE assembled prior performs as good as using the VAE trained on the pattern with the best fit, but it has the advantage of lower computation cost and more realistic prior uncertainty. Moreover, the synthetic case shows an adequate estimation of most small-scale structures. The absolute values of wave velocity are computed by assuming a linear mixing model which involves two additional parameters that effectively shift and scale velocity values and are included in the inversion.

关键词： prior information geophysical inversion variational autoencoder deep learning ground-penetrating radar traveltime tomography

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Representation learning of resting state fMRI with variational autoencoder

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NEUROIMAGE 2021年 241卷 118423-118423页

作者： Kim, Jung-Hoon Zhang, Yizhen Han, Kuan Wen, Zheyu Choi, Minkyu Liu, Zhongming Univ Michigan Dept Biomed Engn Ann Arbor MI 48109 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Purdue Univ Weldon Sch Biomed Engn W Lafayette IN 47907 USA

Resting state functional magnetic resonance imaging (rsfMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rsfMRI data. Here we establish a variational auto encoder, as a generative model trainable with unsupervised learning, to disentangle the unknown sources of rsfMRI activity. After being trained with large data from the Human Connectome Project, the model has learned to represent and generate patterns of cortical activity and connectivity using latent variables. The latent representation and its trajectory represent the spatiotemporal characteristics of rsfMRI activity. The latent variables reflect the principal gradients of the latent trajectory and drive activity changes in cortical networks. Representational geometry captured as covariance or correlation between latent variables, rather than cortical connectivity, can be used as a more reliable feature to accurately identify subjects from a large group, even if only a short period of data is available in each subject. Our results demonstrate that VAE is a valuable addition to existing tools, particularly suited for unsupervised representation learning of resting state fMRI activity.

关键词： variational autoencoder Deep generative model Unsupervised learning Latent gradients

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Nearest Neighbours Graph variational autoencoder

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ALGORITHMS 2023年第3期16卷 143-143页

作者： Arsini, Lorenzo Caccia, Barbara Ciardiello, Andrea Giagu, Stefano Mancini Terracciano, Carlo Sapienza Univ Rome Dept Phys I-00185 Rome Italy INFN Sect Rome I-00185 Rome Italy Ist Super Sanita I-00161 Rome Italy

Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems with excellent results. However, both the generation of graphs and the handling of large graphs still remain open challenges. This work aims to introduce techniques for generating large graphs and test the approach on a complex problem such as the calculation of dose distribution in oncological radiotherapy applications. To this end, we introduced a pooling technique (ReNN-Pool) capable of sampling nodes that are spatially uniform without computational requirements in both model training and inference. By construction, the ReNN-Pool also allows the definition of a symmetric un-pooling operation to recover the original dimensionality of the graphs. We also present a variational autoencoder (VAE) for generating graphs, based on the defined pooling and un-pooling operations, which employs convolutional graph layers in both encoding and decoding phases. The performance of the model was tested on both the realistic use case of a cylindrical graph dataset for a radiotherapy application and the standard benchmark dataset sprite. Compared to other graph pooling techniques, ReNN-Pool proved to improve both performance and computational requirements.

关键词： graph neural network variational autoencoder pooling nearest neighbours

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MC-RVAE: Multi-channel recurrent variational autoencoder for multimodal Alzheimer's disease progression modelling

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NEUROIMAGE 2023年 268卷 119892-119892页

作者： Marti-Juan, Gerard Lorenzi, Marco Piella, Gemma Univ Pompeu Fabra Dept Tecnol Informacio & Comunicac BCN MedTech Barcelona Spain Univ Cote Azur Epione Res Project Inria Sophia Antipolis Nice France

The progression of neurodegenerative diseases, such as Alzheimer's Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with N = 897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5% (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores.

关键词： Alzheimer's disease Longitudinal Multimodal variational autoencoder Recurrent neural network Disease progression modelling

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An ensemble approach for circular RNA-disease association prediction using variational autoencoder and genetic algorithm

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JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 2024年第4期22卷 2450018页

作者： Salooja, C. M. Sanker, Arjun Deepthi, K. Jereesh, A. S. Cochin Univ Sci & Technol Dept Comp Sci Bioinformat Lab Kochi 682022 Kerala India Cent Univ Kerala Dept Comp Sci Cent Govt India Chalingal 671316 Kerala India

Circular RNAs (circRNAs) are endogenous non-coding RNAs with a covalently closed loop structure. They have many biological functions, mainly regulatory ones. They have been proven to modulate protein-coding genes in the human genome. CircRNAs are linked to various diseases like Alzheimer's disease, diabetes, atherosclerosis, Parkinson's disease and cancer. Identifying the associations between circular RNAs and diseases is essential for disease diagnosis, prevention, and treatment. The proposed model, based on the variational autoencoder and genetic algorithm circular RNA disease association (VAGA-CDA), predicts novel circRNA-disease associations. First, the experimentally verified circRNA-disease associations are augmented with the synthetic minority oversampling technique (SMOTE) and regenerated using a variational autoencoder, and feature selection is applied to these vectors by a genetic algorithm (GA). The variational autoencoder effectively extracts features from the augmented samples. The optimized feature selection of the genetic algorithm effectively carried out dimensionality reduction. The sophisticated feature vectors extracted are then given to a Random Forest classifier to predict new circRNA-disease associations. The proposed model yields an AUC value of 0.9644 and 0.9628 under 5-fold and 10-fold cross-validations, respectively. The results of the case studies indicate the robustness of the proposed model.

关键词： Circular RNA variational autoencoder genetic algorithm disease associations random forest

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