scholarly journals DeepCAPE: a deep convolutional neural network for the accurate prediction of enhancers

2018 ◽  
Author(s):  
Shengquan Chen ◽  
Mingxin Gan ◽  
Hairong Lv ◽  
Rui Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cell Line ◽  
Cell Lines ◽  
Dna Sequences ◽  
Disease Status ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Deep Convolutional Neural Network ◽  
Typical Cell

AbstractThe establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, though having successfully reported enhancers in typical cell lines, are still too costly and time consuming to perform systematic identification of enhancers specific to different cell lines under a variety of disease status. Existing computational methods, though capable of predicting regulatory elements purely relying on DNA sequences, lack the power of cell line-specific screening. Recent studies have suggested that chromatin accessibility of a DNA segment is closely related to its potential function in regulation, and thus may provide useful information in identifying regulatory elements. Motivated by the above understanding, we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner. We proposed DeepCAPE, a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data. We demonstrate that our model not only consistently outperforms existing methods in the classification of enhancers against background sequences, but also accurately predicts enhancers across different cell lines. We further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known motifs. We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate disease-related enhancers.

scholarly journals Prediction of Enhancer-Promoter Interactions Using the Cross-Cell Information and Domain Adversarial Neural Network

2020 ◽  
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Neural Network ◽  
Cell Line ◽  
Cell Lines ◽  
Dna Sequences ◽  
Prediction Performance ◽  
Interaction Prediction ◽  
New Cell Line ◽  
Testing Data ◽  
Level Information ◽  
The Cross

Abstract Background: Enhancer-promoter interactions (EPIs) play key roles in transcriptional regulation and disease progression. Although several computational methods have been developed to predict such interactions, their performances are not satisfactory when training and testing data from different cell lines. Currently, it is still unclear what extent EPI prediction across cell lines can be made based on sequence-level information.Results: In this work, we present a novel Sequence-based method (called SEPT) to predict the Enhancer-Promoter interactions in the new cell line by using the cross-cell information and Transfer learning. SEPT first leans the features of enhancer and promoter from DNA sequences with convolutional neural network (CNN), then designing the gradient reversal layer of transfer leaning to reduce the cell line specific features meanwhile retaining the features associated with EPIs. When the locations of enhancers and promoters are provided in new cell line, SEPT can successfully recognize EPIs in this new cell line based on labeled data of other cell lines. The experiment results show that SEPT can effectively learn the latent import EPIs-related features between cell lines and achieves the best prediction performance in terms of AUC (the area under the receiver operating curves).Conclusion: SEPT is an effective method for predicting EPIs in the new cell line. Domain adversarial architecture of transfer leaning used in SEPT can learn the latent EPI features shared among cell lines from all other existing labeled data. It can be expected that SEPT will be of interest to researchers concerned with biological interaction prediction.

scholarly journals Prediction of enhancer-promoter interactions using the cross-cell information and domain adversarial neural network

2020 ◽  
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Neural Network ◽  
Cell Line ◽  
Transfer Learning ◽  
Cell Lines ◽  
Dna Sequences ◽  
Interaction Prediction ◽  
New Cell Line ◽  
Testing Data ◽  
Level Information ◽  
The Cross

Abstract Background: Enhancer-promoter interactions (EPIs) play key roles in transcriptional regulation and disease progression. Although several computational methods have been developed to predict such interactions, their performances are not satisfactory when training and testing data from different cell lines. Currently, it is still unclear what extent a across cell line prediction can be made based on sequence-level information.Results: In this work, we present a novel Sequence-based method (called SEPT) to predict the Enhancer-Promoter interactions in new cell line by using the cross-cell information and Transfer learning. SEPT first learns the features of enhancer and promoter from DNA sequences with convolutional neural network (CNN), then designing the gradient reversal layer of transfer learning to reduce the cell line specific features meanwhile retaining the features associated with EPIs. When the locations of enhancers and promoters are provided in new cell line, SEPT can successfully recognize EPIs in this new cell line based on labeled data of other cell lines. The experiment results show that SEPT can effectively learn the latent import EPIs-related features between cell lines and achieves the best prediction performance in terms of AUC (the area under the receiver operating curves).Conclusion: SEPT is an effective method for predicting the EPIs in new cell line. Domain adversarial architecture of transfer learning used in SEPT can learn the latent EPIs shared features among cell lines from all other existing labeled data. It can be expected that SEPT will be of interest to researchers concerned with biological interaction prediction.

scholarly journals Prediction of enhancer–promoter interactions using the cross-cell type information and domain adversarial neural network

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Neural Network ◽  
Cell Line ◽  
Transfer Learning ◽  
Cell Lines ◽  
Dna Sequences ◽  
New Cell Line ◽  
Testing Data ◽  
Level Information ◽  
The Cross ◽  
Type Information

Abstract Background Enhancer–promoter interactions (EPIs) play key roles in transcriptional regulation and disease progression. Although several computational methods have been developed to predict such interactions, their performances are not satisfactory when training and testing data from different cell lines. Currently, it is still unclear what extent a across cell line prediction can be made based on sequence-level information. Results In this work, we present a novel Sequence-based method (called SEPT) to predict the enhancer–promoter interactions in new cell line by using the cross-cell information and Transfer learning. SEPT first learns the features of enhancer and promoter from DNA sequences with convolutional neural network (CNN), then designing the gradient reversal layer of transfer learning to reduce the cell line specific features meanwhile retaining the features associated with EPIs. When the locations of enhancers and promoters are provided in new cell line, SEPT can successfully recognize EPIs in this new cell line based on labeled data of other cell lines. The experiment results show that SEPT can effectively learn the latent import EPIs-related features between cell lines and achieves the best prediction performance in terms of AUC (the area under the receiver operating curves). Conclusions SEPT is an effective method for predicting the EPIs in new cell line. Domain adversarial architecture of transfer learning used in SEPT can learn the latent EPIs shared features among cell lines from all other existing labeled data. It can be expected that SEPT will be of interest to researchers concerned with biological interaction prediction.

scholarly journals Prediction of enhancer-promoter interactions using the cross-cell information and domain adversarial neural network

2020 ◽  
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Neural Network ◽  
Cell Line ◽  
Transfer Learning ◽  
Cell Lines ◽  
Dna Sequences ◽  
Interaction Prediction ◽  
New Cell Line ◽  
Testing Data ◽  
Level Information ◽  
The Cross

Abstract Background: Enhancer-promoter interactions (EPIs) play key roles in transcriptional regulation and disease progression. Although several computational methods have been developed to predict such interactions, their performances are not satisfactory when training and testing data from different cell lines. Currently, it is still unclear what extent EPI prediction across cell lines can be made based on sequence-level information. Results: In this work, we present a novel Sequence-based method (called SEPT) to predict the Enhancer-Promoter interactions in the new cell line by using the cross-cell information and Transfer learning. SEPT first learns the features of enhancer and promoter from DNA sequences with convolutional neural network (CNN), then designing the gradient reversal layer of transfer learning to reduce the cell line specific features meanwhile retaining the features associated with EPIs. When the locations of enhancers and promoters are provided in new cell line, SEPT can successfully recognize EPIs in this new cell line based on labeled data of other cell lines. The experiment results show that SEPT can effectively learn the latent import EPIs-related features between cell lines and achieves the best prediction performance in terms of AUC. Conclusion: SEPT is an effective method for predicting EPIs in the new cell line. Domain adversarial architecture of transfer learning used in SEPT can learn the latent EPI features shared among cell lines from all other existing labeled data. It can be expected that SEPT will be of interest to researchers concerned with biological interaction prediction.

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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