DeepHiC: A generative adversarial network for enhancing Hi-C data resolution

Hong, Huasheng; Jiang, Shuai; Li, Hao; Du, Guangwei; Sun, Yu; Huan, Tao; Cheng, Qimin; Zhao, Chenghui; Li, Ruijiang; Li, Wanying; Yin, Xiaoyao; Huang, Yangchen; Cheng, Li; Chen, Hebing; Bo, Xiaochen

doi:10.1371/journal.pcbi.1007287

Cited by 63 publications

(79 citation statements)

References 43 publications

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“…The work associated with the higher level of predictions and knowledge discovery with the analytics Hao Hong et al [14] created DeepHiC whereby the high performance generative adversarial network is used. The study indicated that DeepHiC is able to mate high-resolution Hi-C data from as few down sampled reads as 1 percent.…”

Section: Review Of Existing Workmentioning

confidence: 99%

WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

Kumar

Harshavardhan²,

Bhukya

et al. 2020

Materials Today: Proceedings

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The domain of medical diagnosis and predictive analytics is one of the key domains of research with enormous dimensions whereby the diseases of different types can be predicted. Nowadays, there is a huge panic of impact and rapid mutation of the COVID-19 virus impression. The world is getting affected by this virus to a huge extent and there is no vaccine developed so far. India is also having more than 10,000 patients with than 300 deceased. The global human community is having around 20 lacs of Coronavirus patients. The Generative Adversarial Network (GAN) is the contemporary high-performance approach in which the use of advanced neural networks is done for the cavernous analytics of the images and multimedia data. In this research work, the analytics of key points from medical images of the COVID-19 dataset is to be presented using which the diagnosis and predictions can be done for the patients. The GANs are used for the generation, transformation as well as presentation of the dataset and key points using advanced deep learning models which can analyze the patterns in the medical images including X-Ray, CT Scan, and many others. Using such approaches with the integration of GANs, the overall predictive analytics can be made high performance aware as compared to the classical neural networks with multiple layers. In this research manuscript, the inscription of work is projected on the benchmark datasets with the advanced scripting so that the predictive mining and knowledge discovery can be done effectively with more accuracy.

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Section: Review Of Existing Workmentioning

confidence: 99%

WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

Kumar

Harshavardhan²,

Bhukya

et al. 2020

Materials Today: Proceedings

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“…Hi-C is commonly used to study three-dimensional genome organization. Hong et al (105) developed a GAN, namely DeepHiC, to predict high-resolution Hi-C contact maps from low-coverage sequencing data. DeepHiC can reproduce highresolution Hi-C data from as few as 1% down sampled reads.…”

Section: Application Of Gan In Bioinformaticsmentioning

confidence: 99%

Generative Adversarial Networks and Its Applications in Biomedical Informatics

Lan

You

Zhang

et al. 2020

Front. Public Health

149

104

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The basic Generative Adversarial Networks (GAN) model is composed of the input vector, generator, and discriminator. Among them, the generator and discriminator are implicit function expressions, usually implemented by deep neural networks. GAN can learn the generative model of any data distribution through adversarial methods with excellent performance. It has been widely applied to different areas since it was proposed in 2014. In this review, we introduced the origin, specific working principle, and development history of GAN, various applications of GAN in digital image processing, Cycle-GAN, and its application in medical imaging analysis, as well as the latest applications of GAN in medical informatics and bioinformatics.

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“…Using Micro-C contact maps from hESC, mESC, and HFF as the prediction target, the model was trained with backpropagation [10], in which the aforementioned convolutional features were learned adaptively. Other than leveraging a number of epigenomic features, our model architecture differs from HiCPlus [11] and DeepHiC [12] which treats Hi-C contact maps as images and performs grid-convolution to improve the resolution. With the graph convolutional networks and additional epigenomic features, CAESAR not only enhances the resolution of contact maps, but also predicts the structures which are not captured by Hi-C, including polycomb repressive regions, short-range loops and stripes ( Figure 1b).…”

Section: A Deep Learning Model Imputing High-resolution Chromatin Conmentioning

confidence: 99%

Connecting high-resolution 3D chromatin organization with epigenomics

Feng

Yao

Wang

et al. 2020

Preprint

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The resolution of chromatin conformation capture technologies keeps increasing, and the recent nucleosome resolution chromatin contact maps allow us to explore how fine-scale 3D chromatin organization is related to epigenomic states in human cells. Using publicly available Micro-C datasets, we have developed a deep learning model, CAESAR, to learn a mapping function from epigenomic features to 3D chromatin organization. The model accurately predicts fine-scale structures, such as short-range chromatin loops and stripes, that Hi-C fails to detect. With existing epigenomic datasets from ENCODE and Roadmap Epigenomics Project, we successfully imputed high-resolution 3D chromatin contact maps for 91 human tissues and cell lines. In the imputed high-resolution contact maps, we identified the spatial interactions between genes and their experimentally validated regulatory elements, demonstrating CAESAR's potential in coupling transcriptional regulation with 3D chromatin organization at high resolution.

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DeepHiC: A generative adversarial network for enhancing Hi-C data resolution

Cited by 63 publications

References 43 publications

WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

Generative Adversarial Networks and Its Applications in Biomedical Informatics

Connecting high-resolution 3D chromatin organization with epigenomics

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