Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network

Abstract Motivation Emerging evidence indicates that circular RNA (circRNA) plays a crucial role in human disease. Using circRNA as biomarker gives rise to a new perspective regarding our diagnosing of diseases and understanding of disease pathogenesis. However, detection of circRNA–disease associations by biological experiments alone is often blind, limited to small scale, high cost and time consuming. Therefore, there is an urgent need for reliable computational methods to rapidly infer the potential circRNA–disease associations on a large scale and to provide the most promising candidates for biological experiments. Results In this article, we propose an efficient computational method based on multi-source information combined with deep convolutional neural network (CNN) to predict circRNA–disease associations. The method first fuses multi-source information including disease semantic similarity, disease Gaussian interaction profile kernel similarity and circRNA Gaussian interaction profile kernel similarity, and then extracts its hidden deep feature through the CNN and finally sends them to the extreme learning machine classifier for prediction. The 5-fold cross-validation results show that the proposed method achieves 87.21% prediction accuracy with 88.50% sensitivity at the area under the curve of 86.67% on the CIRCR2Disease dataset. In comparison with the state-of-the-art SVM classifier and other feature extraction methods on the same dataset, the proposed model achieves the best results. In addition, we also obtained experimental support for prediction results by searching published literature. As a result, 7 of the top 15 circRNA–disease pairs with the highest scores were confirmed by literature. These results demonstrate that the proposed model is a suitable method for predicting circRNA–disease associations and can provide reliable candidates for biological experiments. Availability and implementation The source code and datasets explored in this work are available at https://github.com/look0012/circRNA-Disease-association. Supplementary information Supplementary data are available at Bioinformatics online.

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VoroCNN: Deep convolutional neural network built on 3D Voronoi tessellation of protein structures

Bioinformatics ◽

10.1093/bioinformatics/btab118 ◽

2021 ◽

Author(s):

Ilia Igashov ◽

liment Olechnovič ◽

Maria Kadukova ◽

Česlovas Venclovas ◽

Sergei Grudinin

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Computational Models ◽

Voronoi Tessellation ◽

Protein Structures ◽

Data Representation ◽

Deep Convolutional Neural Network ◽

Molecular Structures ◽

Supplementary Information ◽

Evolutionary Information

Abstract Motivation Effective use of evolutionary information has recently led to tremendous progress in computational prediction of three-dimensional (3D) structures of proteins and their complexes. Despite the progress, the accuracy of predicted structures tends to vary considerably from case to case. Since the utility of computational models depends on their accuracy, reliable estimates of deviation between predicted and native structures are of utmost importance. Results For the first time, we present a deep convolutional neural network (CNN) constructed on a Voronoi tessellation of 3D molecular structures. Despite the irregular data domain, our data representation allows us to efficiently introduce both convolution and pooling operations and train the network in an end-to-end fashion without precomputed descriptors. The resultant model, VoroCNN, predicts local qualities of 3D protein folds. The prediction results are competitive to state of the art and superior to the previous 3D CNN architectures built for the same task. We also discuss practical applications of VoroCNN, for example, in recognition of protein binding interfaces. Availability The model, data, and evaluation tests are available at https://team.inria.fr/nano-d/software/vorocnn/. Supplementary information Supplementary data are available at Bioinformatics online.

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VoroCNN: Deep convolutional neural network built on 3D Voronoi tessellation of protein structures

10.1101/2020.04.27.063586 ◽

2020 ◽

Cited By ~ 3

Author(s):

Ilia Igashov ◽

Kliment Olechnovic ◽

Maria Kadukova ◽

Česlovas Venclovas ◽

Sergei Grudinin

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Computational Models ◽

Voronoi Tessellation ◽

Protein Structures ◽

Computational Prediction ◽

Data Representation ◽

Deep Convolutional Neural Network ◽

Molecular Structures ◽

Evolutionary Information

MotivationEffective use of evolutionary information has recently led to tremendous progress in computational prediction of three-dimensional (3D) structures of proteins and their complexes. Despite the progress, the accuracy of predicted structures tends to vary considerably from case to case. Since the utility of computational models depends on their accuracy, reliable estimates of deviation between predicted and native structures are of utmost importance.ResultsFor the first time we present a deep convolutional neural network (CNN) constructed on a Voronoi tessellation of 3D molecular structures. Despite the irregular data domain, our data representation allows to efficiently introduce both convolution and pooling operations of the network. We trained our model, called VoroCNN, to predict local qualities of 3D protein folds. The prediction results are competitive to the state of the art and superior to the previous 3D CNN architectures built for the same task. We also discuss practical applications of VoroCNN, for example, in the recognition of protein binding interfaces.AvailabilityThe model, data, and evaluation tests are available at https://team.inria.fr/nano-d/software/vorocnn/[email protected], [email protected]

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Acupoint Detection Based on Deep Convolutional Neural Network

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9188367 ◽

2020 ◽

Author(s):

Lingyao Sun ◽

Shiying Sun ◽

Yuanbo Fu ◽

Xiaoguang Zhao

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Deep Convolutional Neural Network

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

Automation and modeling in design and management of ◽

10.30987/2658-6436-2020-4-4-14 ◽

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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