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SiRCle (Signature Regulatory Clustering) model integration reveals mechanisms of phenotype regulation in renal cancer

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GENOME MEDICINE 2024年第1期16卷 1-26页

作者： Mora, Ariane Schmidt, Christina Balderson, Brad Frezza, Christian Boden, Mikael Univ Queensland Sch Chem & Mol Biosci Mol Biosci Bldg 76 St Lucia Qld 4072 Australia Univ Cambridge Hutchison MRC Res Ctr Med Res Council Canc Unit Box 197Cambridge Biomed Campus Cambridge CB2 0X2 England Univ Cologne Fac Med & Univ Hosp Cologne Inst Metabol Ageing Cluster Excellence Cellular Stress Responses Aging Joseph Stelzmann Str 26 D-50931 Cologne Germany Univ Cologne Inst Genet Fac Math & Nat Sci Cluster Excellence Cellular Stress Responses Aging Cologne Germany

BackgroundClear cell renal cell carcinoma (ccRCC) tumours develop and progress via complex remodelling of the kidney epigenome, transcriptome, proteome and metabolome. Given the subsequent tumour and inter-patient heterogeneity, drug-based treatments report limited success, calling for multi-omics studies to extract regulatory relationships, and ultimately, to develop targeted therapies. Yet, methods for multi-omics integration to reveal mechanisms of phenotype regulation are ***, we present SiRCle (Signature Regulatory Clustering), a method to integrate DNA methylation, RNA-seq and proteomics data at the gene level by following central dogma of biology, i.e. genetic information proceeds from DNA, to RNA, to protein. To identify regulatory clusters across the different omics layers, we group genes based on the layer where the gene's dysregulation first occurred. We combine the SiRCle clusters with a variational autoencoder (VAE) to reveal key features from omics' data for each SiRCle cluster and compare patient subpopulations in a ccRCC and a PanCan *** SiRCle to a ccRCC cohort, we showed that glycolysis is upregulated by DNA hypomethylation, whilst mitochondrial enzymes and respiratory chain complexes are translationally suppressed. Additionally, we identify metabolic enzymes associated with survival along with the possible molecular driver behind the gene's perturbations. By using the VAE to integrate omics' data followed by statistical comparisons between tumour stages on the integrated space, we found a stage-dependent downregulation of proximal renal tubule genes, hinting at a loss of cellular identity in cancer cells. We also identified the regulatory layers responsible for their suppression. Lastly, we applied SiRCle to a PanCan cohort and found common signatures across ccRCC and PanCan in addition to the regulatory layer that defines tissue *** results highlight SiRCle's ability to reveal mechanisms of p

关键词： Integration Multi-omics Regulation Machine learning variational autoencoder Clear cell renal cell carcinoma PanCan Metabolism

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SyntheVAEiser: augmenting traditional machine learning methods with VAE-based gene expression sample generation for improved cancer subtype predictions

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GENOME BIOLOGY 2024年第1期25卷 1-18页

作者： Karlberg, Brian Kirchgaessner, Raphael Lee, Jordan Peterkort, Matthew Beckman, Liam Goecks, Jeremy Ellrott, Kyle Oregon Hlth & Sci Univ Biomed Engn 3181 SW Sam Jackson Pk Rd Portland OR 97239 USA H Lee Moffitt Canc Ctr & Res Inst Dept Machine Learning Tampa FL USA

The accuracy of machine learning methods is often limited by the amount of training data that is available. We proposed to improve machine learning training regimes by augmenting datasets with synthetically generated samples. We present a method for synthesizing gene expression samples and test the system's capabilities for improving the accuracy of categorical prediction of cancer subtypes. We developed SyntheVAEiser, a variational autoencoder based tool that was trained and tested on over 8000 cancer samples. We have shown that this technique can be used to augment machine learning tasks and increase performance of recognition of underrepresented cohorts.

关键词： Sample synthesis Synthetic data Data augmentation Generative modeling Feature engineering Cancer subtyping Molecular subtyping variational autoencoder Transcriptomics Gene expression

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Hybrid 3D Medical Image Segmentation Using CNN and Frequency Transformer Fusion

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年 1-14页

作者： Labbihi, Ismayl Meslouhi, Othmane El Elassad, Zouhair Elamrani Abou Benaddy, Mohamed Kardouchi, Mustapha Akhloufi, Moulay Ibn Zohr Univ Fac Sci LabSI Lab Agadir 80000 Morocco Cadi Ayyad Univ Natl Sch Appl Sci SARS Grp Safi 46000 Morocco Univ Moncton Dept Comp Sci PRIME Lab 18 Antonine Maillet Ave Moncton NB E1A 3E9 Canada

Medical image segmentation poses a significant challenge, particularly with 3D images. This study presents a novel hybrid network for 3D medical image segmentation, which sequentially integrates convolutional neural networks (CNNs) and transformer models. Structured with an encoder-decoder architecture, the network comprises a frequency transformer branch and a variational autoencoder branch. Initially, the CNN encoder extracts the features of volumetric spatial from the input 3D image. These features are subsequently converted into vectors and incorporated into a frequency transformer. This transformer employs fast Fourier transforms instead of self-attention to mitigate computational complexity. Significantly, this research marks the inaugural utilization of the Fourier transform on transformers for image segmentation employing 3D datasets. The network's efficacy was evaluated on kidney, tumor, and brain 3D datasets. It achieved remarkable results, such as a 97.54% dice score and a 95%-Hausdorff distance (95%-HD) of 3.26 mm for kidney segmentation, and an 86.92% dice score with a 95%-HD of 2.57 mm for brain tumor segmentation. These results underscore the network's potential for accurate and reliable segmentation in diverse medical imaging applications.

关键词： 3D medical image segmentation Convolutional neural networks Frequency transformer variational autoencoder Fourier transforms

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A Two-Stage Cascade Model with variational autoencoders and Attention Gates for MRI Brain Tumor Segmentation 6th

A Two-Stage Cascade Model with Variational Autoencoders and ...

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6th International MICCAI Brain-Lesion Workshop (BrainLes)

作者： Lyu, Chenggang Shu, Hai NYU Dept Biostat Sch Global Publ Hlth New York NY 10003 USA

ISBN: (纸本)9783030720834;9783030720841

Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning. In this paper, we propose a two-stage encoder-decoder based model for brain tumor subregional segmentation. variational autoencoder regularization is utilized in both stages to prevent the overfitting issue. The second-stage network adopts attention gates and is trained additionally using an expanded dataset formed by the first-stage outputs. On the BraTS 2020 validation dataset, the proposed method achieves the mean Dice score of 0.9041, 0.8350, and 0.7958, and Hausdorff distance (95%) of 4.953, 6.299, 23.608 for the whole tumor, tumor core, and enhancing tumor, respectively. The corresponding results on the BraTS 2020 testing dataset are 0.8729, 0.8357, and 0.8205 for Dice score, and 11.4288, 19.9690, and 15.6711 for Hausdorff distance. The code is publicly available at https://***/shu-hai/two-stage-VAE-Attention-gate-BraTS2020.

关键词： Attention gate Brain tumor segmentation Encoder-decoder network variational autoencoder

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Modality Completion via Gaussian Process Prior variational autoencoders for Multi-modal Glioma Segmentation 24th

Modality Completion via Gaussian Process Prior Variational A...

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International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Hamghalam, Mohammad Frangi, Alejandro F. Lei, Baiying Simpson, Amber L. Queens Univ Sch Comp Kingston ON Canada Islamic Azad Univ Fac Elect Qazvin Branch Qazvin Iran Queens Univ Dept Biomed & Mol Sci Kingston ON Canada Univ Leeds CISTIB Ctr Computat Imaging & Simulat Technol Bio Sch Comp Leeds LS2 9LU W Yorkshire England LICAMM Leeds Inst Cardiovasc & Metab Med Sch Med Leeds LS2 9LU W Yorkshire England Katholieke Univ Leuven Med Imaging Res Ctr MIRC Univ Hosp Gasthuisberg Herestr 49 B-3000 Leuven Belgium Shenzhen Univ Hlth Sci Ctr Sch Biomed EngnGuangdong Key Lab Biomed Measurem Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China

ISBN: (纸本)9783030872342;9783030872335

In large studies involving multi protocol Magnetic Resonance Imaging (MRI), it can occur to miss one or more sub-modalities for a given patient owing to poor quality (e.g. imaging artifacts), failed acquisitions, or hallway interrupted imaging examinations. In some cases, certain protocols are unavailable due to limited scan time or to retrospectively harmonise the imaging protocols of two independent studies. Missing image modalities pose a challenge to segmentation frameworks as complementary information contributed by the missing scans is then lost. In this paper, we propose a novel model, Multi-modal Gaussian Process Prior variational autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the Gaussian Process (GP) prior on the variational autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations. Instead of designing one network for each possible subset of present sub-modalities or using frameworks to mix feature maps, missing data can be generated from a single model based on all the available samples. We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing. Our experiments against competitive segmentation baselines with missing sub-modality on BraTS'19 dataset indicate the effectiveness of the MGP-VAE model for segmentation tasks.

关键词： Missing modality Gaussian process variational autoencoder Glioma segmentation MRI

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The Use of Conditional variational autoencoders for Simulation of EAS Images from IACTs

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MOSCOW UNIVERSITY PHYSICS BULLETIN 2023年第SUPPL 1期78卷 S37-S44页

作者： Kryukov, A. P. Polyakov, S. P. Vlaskina, A. A. Gres, E. O. Demichev, A. P. Dubenskaya, Yu. Yu. Zhurov, D. P. Lomonosov Moscow State Univ Skobeltsyn Inst Nucl Phys Moscow 119991 Russia Lomonosov Moscow State Univ Fac Phys Moscow 119991 Russia Irkutsk State Univ Appl Phys Inst Irkutsk 664003 Russia

Imaging atmospheric Cherenkov telescopes are used to record images of extensive area showers caused by high-energy particles colliding with the upper atmosphere. The images are analyzed to determine events' physical parameters, such as the type and the energy of the primary particles. The distributions of some of the physical parameters can be used as well, for example, to determine the properties of a gamma ray source. The key problem of any experiment is the calibration of experimental data. For this purpose, Monte Carlo simulated data with known values of the physical parameters are used. The main disadvantage of this method is its extremely high requirements for computing resources and the large amount of time spent on modelling. In this paper, we use an alternative approach: Cherenkov telescope images are simulated with conditional variational autoencoders. We compare the characteristics of both the individual images and their Hillas parameter distributions with those of the images generated by the Monte Carlo method.

关键词： machine learning variational autoencoder image generation gamma astronomy extensive air shower IACT

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Generative Deep Learning Techniques for Traffic Matrix Estimation From Link Load Measurements

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 1029-1046页

作者： Kakkavas, Grigorios Fryganiotis, Nikolaos Karyotis, Vasileios Papavassiliou, Symeon Natl Tech Univ Athens Sch Elect & Comp Engn Zografos 15780 Greece Ionian Univ Dept Informat Corfu 49132 Greece

Traffic matrices (TMs) contain crucial information for managing networks, optimizing traffic flow, and detecting anomalies. However, directly measuring traffic to construct a TM is resource-intensive and computationally expensive. A more practical approach involves estimating the TM from readily available link load measurements, which falls under the category of inferential network monitoring based on indirect measurements known as network tomography. This paper focuses on solving the problem of estimating the traffic matrix from link loads by utilizing deep generative models. The proposed models are trained using historical data-specifically, previously observed TMs-and are then leveraged to transform traffic matrix estimation (TME) into a simpler minimization problem in a lower-dimensional latent space. This transformed problem can be efficiently solved using a gradient-based optimizer. Our work aims to examine and test different model architectures and optimization approaches. The performance of the proposed methods is comparatively evaluated over a comprehensive set of suitable metrics on two publicly available datasets comprising actual traffic matrices obtained from real backbone networks. In addition, we compare our approach with a state-of-the-art method previously published in the literature.

关键词： Estimation Routing Data models Mathematical models Load modeling Optimization Deep learning tomography traffic matrix estimation variational autoencoder

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A Generative Verification Framework on Statistical Stability for Data-Driven Controllers

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IEEE ACCESS 2023年 11卷 5267-5280页

作者： Lee, Suwon Kookmin Univ Dept Future Mobil Seoul 02707 South Korea

This study proposes a novel framework for evaluating the stability of data-driven controllers and the concept of statistical stability. The proposed framework can be used when it is challenging to show stability through conventional control theory. The novelty of this paper lies in that it provides a method for scientifically analyzing the stability of data-driven controllers, thereby improving the quality of data-driven controllers. The proposed framework consists of three parts: the generative model, controller optimizer, and verification model. A variational autoencoder is used to classify and randomly generate data, and the generated data are used to train the controller. A support vector machine is used to classify areas where the controller is statistically stable. The statistical stability of an optimal controller designed using a deep neural network structure is analyzed using the proposed framework.

关键词： Neural networks Deep learning Control systems Numerical stability Stability criteria Adaptive control Lyapunov methods Data-driven analysis deep neural network nonlinear controller nonlinear system verification optimal control performance analysis statistical stability variational autoencoder

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Synthesizing Retinal Images using End-To-End VAEs-GAN Pipeline-Based Sharpening and Varying Layer

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第1期83卷 1283-1307页

作者： Saeed, Ali Q. Abdullah, Siti Norul Huda Sheikh Che-Hamzah, Jemaima Ghani, Ahmad Tarmizi Abdul Abu-ain, Waleed Abdel karim Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Cyber Secur Bangi 43650 Selangor Malaysia Northern Tech Univ Comp Ctr Ninevah 41001 Iraq Univ Kebangsaan Malaysia Fac Med Dept Ophthalmol Kuala Lumpur 43650 Malaysia Taibah Univ Appl Coll Dept Comp Sci Al Madinah Al Munawwara 46429 Saudi Arabia

This study attempts to synthesize a realistic-looking fundus image from a morphologically changed vessel structure using the newly proposed sharpening and varying vessels technique (SVV). This technique sharpens the reconstructed vessels and introduces variation to their structure to generate multiple images from a single input mask. This helps to reduce the reliance on expensive and scarce annotated medical data. The study also aims to overcome the limitations of current methods, such as unrealistic optic disc boundaries, extreme vessel tortuosity, and missed optic discs. This is mainly due to the fact that existing models penalize their weights based on the difference between real and synthetic images using only a single mask. Therefore, their emphasis is on generating the input mask while disregarding other important fundoscopic features. Inspired by the recent progress in Generative Adversarial Nets (GANs) and variational autoencoder (VAE), the proposed approach was able to preserve the geometrical shape of critical fundus characteristics. Visual and quantitative results indicate that the produced images are considerably distinct from the ones used for training. However, they also exhibit anatomical coherence and a reasonable level of visual. The data utilized in this study and the programming code necessary to recreate the experiment can be accessed at https://***/AliSaeed86/SVV_GAN.

关键词： Image synthesis Generative Adversarial Networks Retinal fundus image variational autoencoder Deep learning

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Uncertainty involved drag divergence characteristic predicting method based on VAE

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JOURNAL OF MEMBRANE COMPUTING 2024年第2期6卷 1-14页

作者： Liu, Wei Xie, Hairun Wang, Jing Xiang, Jixin Huang, Zenghui Zhang, Miao Shanghai Aircraft Design & Res Inst 5188 JinKe Rd Shanghai 201210 Peoples R China Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut 800 Dongchuan Rd Shanghai 200240 Peoples R China

Effective access to obtain the drag divergence characteristic of an airfoil is crucial for improving the economy, safety, and comfort of the aircraft. Current methods face challenges in providing satisfactory performance concerning efficiency, reliability and generalizability. In this study, an uncertainty-based network is developed, aiming to realize the prediction of the drag characteristic curves and drag divergence characteristics for different supercritical airfoil. To be more specific, a VAE-based network is established, with an encoder to convert the airfoil geometry into low-dimensional latent variables. These latent variables are then simultaneously fed into both the decoder and the drag characteristic prediction network. The decoder is responsible for geometric reconstruction, ensuring that the latent variables maintain a correlation with the geometric features of the airfoil. The prediction network maps the latent variables to the drag characteristic curve. Once trained, the uncertainty distribution of the drag can be realized through sampling from the distribution of latent variables. The analysis of results indicates the model's outstanding performance, with a mean absolute error of 7.407x10-5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.407\times 10<^>{-5}$$\end{document} for the airfoil geometric reconstruction and a mean absolute error of 0.455 count for the drag coefficient prediction. The mean error of the drag divergence Mach number prediction reaches 2.805x10-4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.805\times 10<^>{-4}$$\end{document}. The results show that the model has cons

关键词： Deep learning Supercritical airfoil Drag divergence variational autoencoder

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