Creating artificial human genomes using generative neural networks

Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create realistic synthetic data is still under-exploited in genetics and absent from population genetics. Yet a known limitation in the field is the reduced access to many genetic databases due to concerns about violations of individual privacy, although they would provide a rich resource for data mining and integration towards advancing genetic studies. In this study, we demonstrated that deep generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be trained to learn the complex distributions of real genomic datasets and generate novel high-quality artificial genomes (AGs) with none to little privacy loss. We show that our generated AGs replicate characteristics of the source dataset such as allele frequencies, linkage disequilibrium, pairwise haplotype distances and population structure. Moreover, they can also inherit complex features such as signals of selection. To illustrate the promising outcomes of our method, we showed that imputation quality for low frequency alleles can be improved by data augmentation to reference panels with AGs and that the RBM latent space provides a relevant encoding of the data, hence allowing further exploration of the reference dataset and features for solving supervised tasks. Generative models and AGs have the potential to become valuable assets in genetic studies by providing a rich yet compact representation of existing genomes and high-quality, easy-access and anonymous alternatives for private databases.

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Creating Artificial Human Genomes Using Generative Models

10.1101/769091 ◽

2019 ◽

Cited By ~ 6

Author(s):

Burak Yelmen ◽

Aurélien Decelle ◽

Linda Ongaro ◽

Davide Marnetto ◽

Corentin Tallec ◽

...

Keyword(s):

Wide Spectrum ◽

Synthetic Data ◽

Low Frequency ◽

Generative Models ◽

Machine Learning Algorithms ◽

Generative Adversarial Networks ◽

Restricted Boltzmann Machines ◽

High Quality ◽

Genetic Studies ◽

Individual Privacy

AbstractGenerative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create realistic synthetic data is still under-exploited in genetics and absent from population genetics.Yet a known limitation of this field is the reduced access to many genetic databases due to concerns about violations of individual privacy, although they would provide a rich resource for data mining and integration towards advancing genetic studies. In this study, we demonstrated that deep generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be trained to learn the high dimensional distributions of real genomic datasets and create high quality artificial genomes (AGs) with none to little privacy loss. To illustrate the promising outcomes of our method, we showed that (i) imputation quality for low frequency alleles can be improved by augmenting reference panels with AGs, (ii) scores obtained from selection tests on AGs and real genomes are highly correlated and (iii) AGs can inherit genotype-phenotype associations. AGs have the potential to become valuable assets in genetic studies by providing high quality anonymous substitutes for private databases.

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Generative adversarial networks for generating synthetic features for Wi-Fi signal quality

PLoS ONE ◽

10.1371/journal.pone.0260308 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0260308

Author(s):

Mauro Castelli ◽

Luca Manzoni ◽

Tatiane Espindola ◽

Aleš Popovič ◽

Andrea De Lorenzo

Keyword(s):

Synthetic Data ◽

Real Data ◽

Generative Models ◽

Generative Adversarial Networks ◽

Signal Quality ◽

Quality Service ◽

High Quality ◽

The Real ◽

Adversarial Networks ◽

High Quality Service

Wireless networks are among the fundamental technologies used to connect people. Considering the constant advancements in the field, telecommunication operators must guarantee a high-quality service to keep their customer portfolio. To ensure this high-quality service, it is common to establish partnerships with specialized technology companies that deliver software services in order to monitor the networks and identify faults and respective solutions. A common barrier faced by these specialized companies is the lack of data to develop and test their products. This paper investigates the use of generative adversarial networks (GANs), which are state-of-the-art generative models, for generating synthetic telecommunication data related to Wi-Fi signal quality. We developed, trained, and compared two of the most used GAN architectures: the Vanilla GAN and the Wasserstein GAN (WGAN). Both models presented satisfactory results and were able to generate synthetic data similar to the real ones. In particular, the distribution of the synthetic data overlaps the distribution of the real data for all of the considered features. Moreover, the considered generative models can reproduce the same associations observed for the synthetic features. We chose the WGAN as the final model, but both models are suitable for addressing the problem at hand.

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StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications

Symmetry ◽

10.3390/sym13081497 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1497

Author(s):

Harold Achicanoy ◽

Deisy Chaves ◽

Maria Trujillo

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Data Augmentation ◽

Industrial Applications ◽

Generative Models ◽

Training Data ◽

Generative Adversarial Networks ◽

Augmentation Strategy ◽

Synthetic Images ◽

The Impact

Deep learning applications on computer vision involve the use of large-volume and representative data to obtain state-of-the-art results due to the massive number of parameters to optimise in deep models. However, data are limited with asymmetric distributions in industrial applications due to rare cases, legal restrictions, and high image-acquisition costs. Data augmentation based on deep learning generative adversarial networks, such as StyleGAN, has arisen as a way to create training data with symmetric distributions that may improve the generalisation capability of built models. StyleGAN generates highly realistic images in a variety of domains as a data augmentation strategy but requires a large amount of data to build image generators. Thus, transfer learning in conjunction with generative models are used to build models with small datasets. However, there are no reports on the impact of pre-trained generative models, using transfer learning. In this paper, we evaluate a StyleGAN generative model with transfer learning on different application domains—training with paintings, portraits, Pokémon, bedrooms, and cats—to generate target images with different levels of content variability: bean seeds (low variability), faces of subjects between 5 and 19 years old (medium variability), and charcoal (high variability). We used the first version of StyleGAN due to the large number of publicly available pre-trained models. The Fréchet Inception Distance was used for evaluating the quality of synthetic images. We found that StyleGAN with transfer learning produced good quality images, being an alternative for generating realistic synthetic images in the evaluated domains.

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Beyond Deepfake Technology Fear: On its Positive Uses for Livestock Farming

10.20944/preprints202107.0326.v1 ◽

2021 ◽

Author(s):

Suresh Neethirajan

Keyword(s):

Digital Media ◽

Data Augmentation ◽

Animal Health ◽

Real Data ◽

Machine Learning Algorithms ◽

Generative Adversarial Networks ◽

Livestock Farming ◽

Affective Science ◽

Digital Twins ◽

Farming Industry

Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby animal welfare, productivity, and sustainability of the farming industry.

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Improving protein function prediction with synthetic feature samples created by generative adversarial networks

10.1101/730143 ◽

2019 ◽

Author(s):

Cen Wan ◽

David T. Jones

Keyword(s):

Protein Function ◽

Data Augmentation ◽

Protein Function Prediction ◽

Function Prediction ◽

Generative Adversarial Networks ◽

Prediction Methods ◽

High Quality ◽

Adversarial Networks ◽

Synthetic Protein ◽

Protein Feature

AbstractProtein function prediction is a challenging but important task in bioinformatics. Many prediction methods have been developed, but are still limited by the bottleneck on training sample quantity. Therefore, it is valuable to develop a data augmentation method that can generate high-quality synthetic samples to further improve the accuracy of prediction methods. In this work, we propose a novel generative adversarial networks-based method, namely FFPred-GAN, to accurately learn the high-dimensional distributions of protein sequence-based biophysical features and also generate high-quality synthetic protein feature samples. The experimental results suggest that the synthetic protein feature samples are successful in improving the prediction accuracy for all three domains of the Gene Ontology through augmentation of the original training protein feature samples.

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Data Augmentation Using Generative Adversarial Networks (GANs) For GAN-Based Detection Of Pneumonia And COVID-19 In Chest X-Ray Images

10.21203/rs.3.rs-146161/v1 ◽

2021 ◽

Author(s):

Saman Motamed ◽

Patrik Rogalla ◽

Farzad Khalvati

Keyword(s):

Neural Networks ◽

Data Augmentation ◽

Generative Models ◽

Training Data ◽

Generative Adversarial Networks ◽

X Rays ◽

X Ray ◽

Standard Data ◽

Adversarial Networks ◽

Augmentation Techniques

Abstract Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs. Nevertheless, data augmentation techniques for training GANs are under-explored compared to CNNs. In this work, we propose a new GAN architecture for augmentation of chest X-rays for semi-supervised detection of pneumonia and COVID-19 using generative models. We show that the proposed GAN can be used to effectively augment data and improve classification accuracy of disease in chest X-rays for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc) on two different X-ray datasets and show our GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images.

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Data Augmentation for EEG-Based Emotion Recognition Using Generative Adversarial Networks

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2021.723843 ◽

2021 ◽

Vol 15 ◽

Author(s):

Guangcheng Bao ◽

Bin Yan ◽

Li Tong ◽

Jun Shu ◽

Linyuan Wang ◽

...

Keyword(s):

Emotion Recognition ◽

Data Augmentation ◽

Generative Models ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Training Samples ◽

Using Data ◽

Effective Models

One of the greatest limitations in the field of EEG-based emotion recognition is the lack of training samples, which makes it difficult to establish effective models for emotion recognition. Inspired by the excellent achievements of generative models in image processing, we propose a data augmentation model named VAE-D2GAN for EEG-based emotion recognition using a generative adversarial network. EEG features representing different emotions are extracted as topological maps of differential entropy (DE) under five classical frequency bands. The proposed model is designed to learn the distributions of these features for real EEG signals and generate artificial samples for training. The variational auto-encoder (VAE) architecture can learn the spatial distribution of the actual data through a latent vector, and is introduced into the dual discriminator GAN to improve the diversity of the generated artificial samples. To evaluate the performance of this model, we conduct a systematic test on two public emotion EEG datasets, the SEED and the SEED-IV. The obtained recognition accuracy of the method using data augmentation shows as 92.5 and 82.3%, respectively, on the SEED and SEED-IV datasets, which is 1.5 and 3.5% higher than that of methods without using data augmentation. The experimental results show that the artificial samples generated by our model can effectively enhance the performance of the EEG-based emotion recognition.

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Dynamic GAN for High-Quality Sign Language Video Generation from Skeletal poses using Generative Adversarial Networks

10.21203/rs.3.rs-766083/v1 ◽

2021 ◽

Author(s):

B Natarajan ◽

Elakkiya R

Keyword(s):

Sign Language ◽

Performance Metrics ◽

Random Noise ◽

Video Quality ◽

Generative Models ◽

Generative Adversarial Networks ◽

Generation Process ◽

High Quality ◽

Adversarial Networks ◽

Proposed Model

Abstract The emergence of unsupervised generative models has resulted in greater performance in image and video generation tasks. However, existing generative models pose huge challenges in high-quality video generation process due to blurry and inconsistent results. In this paper, we introduce a novel generative framework named Dynamic Generative Adversarial Networks (Dynamic GAN) model for regulating the adversarial training and generating photorealistic high-quality sign language videos from skeletal poses. The proposed model comprises three stages of development such as generator network, classification and image quality enhancement and discriminator network. In the generator fold, the model generates samples similar to real images using random noise vectors, the classification of generated samples are carried out using the VGG-19 model and novel techniques are employed for improving the quality of generated samples in the second fold of the model and finally the discriminator networks fold identifies the real or fake samples. Unlike, existing approaches the proposed novel framework produces photo-realistic video quality results without using any animation or avatar approaches. To evaluate the model performance qualitatively and quantitatively, the proposed model has been evaluated using three benchmark datasets that yield plausible results. The datasets are RWTH-PHOENIX-Weather 2014T dataset, and our self-created dataset for Indian Sign Language (ISL-CSLTR), and the UCF-101 Action Recognition dataset. The output samples and performance metrics show the outstanding performance of our model.

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Is Seeing Still Believing? Leveraging Deepfake Technology for Livestock Farming

Frontiers in Veterinary Science ◽

10.3389/fvets.2021.740253 ◽

2021 ◽

Vol 8 ◽

Author(s):

Suresh Neethirajan

Keyword(s):

Digital Media ◽

Data Augmentation ◽

Animal Health ◽

Real Data ◽

Machine Learning Algorithms ◽

Farm Animals ◽

Generative Adversarial Networks ◽

Farm Animal Welfare ◽

Livestock Farming ◽

Digital Twins

Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics (digital avatars or metaverse) where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby productivity, and sustainability of the farming industry. The interactive 3D avatars and the digital twins of farm animals enabled by deepfake technology offers a timely and essential way in the digital transformation toward exploring the subtle nuances of animal behavior and cognition in enhancing farm animal welfare. Without offering conclusive remarks, the presented mini review is exploratory in nature due to the nascent stages of the deepfake technology.

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Seismic Data Augmentation Based on Conditional Generative Adversarial Networks

Sensors ◽

10.3390/s20236850 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6850

Author(s):

Yuanming Li ◽

Bonhwa Ku ◽

Shou Zhang ◽

Jae-Kwang Ahn ◽

Hanseok Ko

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Synthetic Data ◽

Generative Adversarial Networks ◽

High Quality ◽

Seismic Waveforms ◽

Adversarial Networks ◽

Seismic Waveform ◽

Proposed Model

Realistic synthetic data can be useful for data augmentation when training deep learning models to improve seismological detection and classification performance. In recent years, various deep learning techniques have been successfully applied in modern seismology. Due to the performance of deep learning depends on a sufficient volume of data, the data augmentation technique as a data-space solution is widely utilized. In this paper, we propose a Generative Adversarial Networks (GANs) based model that uses conditional knowledge to generate high-quality seismic waveforms. Unlike the existing method of generating samples directly from noise, the proposed method generates synthetic samples based on the statistical characteristics of real seismic waveforms in embedding space. Moreover, a content loss is added to relate high-level features extracted by a pre-trained model to the objective function to enhance the quality of the synthetic data. The classification accuracy is increased from 96.84% to 97.92% after mixing a certain amount of synthetic seismic waveforms, and results of the quality of seismic characteristics derived from the representative experiment show that the proposed model provides an effective structure for generating high-quality synthetic seismic waveforms. Thus, the proposed model is experimentally validated as a promising approach to realistic high-quality seismic waveform data augmentation.

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