scholarly journals Chromatin accessibility prediction via a hybrid deep convolutional neural network

2017 ◽  
Vol 34 (5) ◽  
pp. 732-738 ◽  
Author(s):  
Qiao Liu ◽  
Fei Xia ◽  
Qijin Yin ◽  
Rui Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Chromatin Accessibility ◽  
Deep Convolutional Neural Network

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.

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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