Population-scale Genomic Data Augmentation Based on Conditional Generative Adversarial Networks

Chen, Junjie; Mowlaei, Mohammad Erfan; Shi, Xinghua

doi:10.1145/3388440.3412475

Cited by 8 publications

(5 citation statements)

References 29 publications

(22 reference statements)

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“…Chen et al. [47] developed a Population‐scale Genomic Data Augmentation based on conditional GAN (PG‐cGAN). The PG‐cGAN's task was to enhance the amount and diversity of genomic data by transforming current data instead of collecting new samples.…”

Section: Methodsmentioning

confidence: 99%

Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective

Smith

Feltus

2023

Comp Sys Onco

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The remarkable flexibility and adaptability of generative adversarial networks (GANs) have led to the proliferation of its models in bioinformatics research. Proteomic and transcriptomic profiles have been shown to be promising methods for discovering and identifying disease biomarkers. However, those analyses were performed by trained human examiners making the process tedious, time consuming, and hard to standardize. With the development of GANs, it is now possible to reduce computational costs and human time for bioinformatics analysis to produce effective biomarkers. Moreover, GANs help address the lack of phenotypic state transitional gene expression data as well as avoid protected human data constraints by generating RNA sequencing (RNA‐seq) data from random vectors. The purpose of this review is to summarize the use of GAN approaches and techniques to augment RNA‐seq expression data and identify clinically useful biomarkers. We compare different studies that use different types of GAN models to examine the biomarkers. Also, we identify research gaps and challenges that apply GANs to bio‐informatics. Finally, we propose potential directions for future research.

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Section: Methodsmentioning

confidence: 99%

Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective

Smith

Feltus

2023

Comp Sys Onco

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“…The generation of novel genomic data with the same statistical properties as the real databases could increase data accessibility immensely and accelerate research without breaching the privacy of biobank donors. In this context, GANs, VAEs, and their derivatives have recently been suggested for generating realistic human genome segments (26,(41)(42)(43)(44)(45). These models have learned not only the global population stratification in real datasets but also complex underlying structures, such as LD patterns along the genome, haplotype-based selection signals, and genomic local ancestry proportions; this indicates that they might be used as reliable second-best alternatives for real genomes in the future (26,41).…”

Section: Applications In Evolutionary Biology and Population Geneticsmentioning

confidence: 99%

“…These models have learned not only the global population stratification in real datasets but also complex underlying structures, such as LD patterns along the genome, haplotype-based selection signals, and genomic local ancestry proportions; this indicates that they might be used as reliable second-best alternatives for real genomes in the future (26,41). Furthermore, they can be conditioned on extra variables, such as population labels, to generate targeted genomes depending on the task (41,42). Finally, it was shown that the generated genomes could be good at preventing privacy leakage from genome donors in the training datasets, yet extensive research in this regard is still needed for further confirmation and improvements before these models can be applied in practical cases (26).…”

Section: Applications In Evolutionary Biology and Population Geneticsmentioning

confidence: 99%

An Overview of Deep Generative Models in Functional and Evolutionary Genomics

Yelmen

Jay

2023

Annu. Rev. Biomed. Data Sci.

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Following the widespread use of deep learning for genomics, deep generative modeling is also becoming a viable methodology for the broad field. Deep generative models (DGMs) can learn the complex structure of genomic data and allow researchers to generate novel genomic instances that retain the real characteristics of the original dataset. Aside from data generation, DGMs can also be used for dimensionality reduction by mapping the data space to a latent space, as well as for prediction tasks via exploitation of this learned mapping or supervised/semisupervised DGM designs. In this review, we briefly introduce generative modeling and two currently prevailing architectures, we present conceptual applications along with notable examples in functional and evolutionary genomics, and we provide our perspective on potential challenges and future directions. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 6 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…However, the model has a large number of layers, and some features may be lost during forward propagation, which results in the features being acquired incompletely. Ren et al [39][40][41][42] added a skip-layer connection among the network layers, which can promote function re-use between layers and preserve useful information. Even if some of them are lost in training, the key features can be well retained.…”

Section: Related Workmentioning

confidence: 99%

Conveyor-Belt Detection of Conditional Deep Convolutional Generative Adversarial Network

Hao¹,

Meng²,

Zhang³

et al. 2021

Computers, Materials &Amp; Continua

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In underground mining, the belt is a critical component, as its state directly affects the safe and stable operation of the conveyor. Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations. This tends to cause a large amount of calculation and low detection precision.To solve these problems, in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network (CDCGAN) was designed. In the traditional DCGAN, the image generated by the generator has a certain degree of randomness. Here, a small number of labeled belt images are taken as conditions and added them to the generator and discriminator, so the generator can generate images with the characteristics of belt damage under the aforementioned conditions. Moreover, because the discriminator cannot identify multiple types of damage, the multi-class softmax function is used as the output function of the discriminator to output a vector of class probabilities, and it can accurately classify cracks, scratches, and tears. To avoid the features learned incompletely, skiplayer connection is adopted in the generator and discriminator. This not only can minimize the loss of features, but also improves the convergence speed. Compared with other algorithms, experimental results show that the loss value of the generator and discriminator is the least. Moreover, its convergence speed is faster, and the mean average precision of the proposed algorithm is up to 96.2%, which is at least 6% higher than that of other algorithms.

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Population-scale Genomic Data Augmentation Based on Conditional Generative Adversarial Networks

Cited by 8 publications

References 29 publications

Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective

Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective

An Overview of Deep Generative Models in Functional and Evolutionary Genomics

Conveyor-Belt Detection of Conditional Deep Convolutional Generative Adversarial Network

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