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

2020 ◽  
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]

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

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.

Deep Convolutional Neural Network for Object Classification

2021 ◽  
pp. 317-343
Author(s):  
Amira Ahmad Al-Sharkawy ◽  
Gehan A. Bahgat ◽  
Elsayed E. Hemayed ◽  
Samia Abdel-Razik Mashali
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Computational Models ◽  
Human Performance ◽  
Deep Neural Networks ◽  
Object Classification ◽  
Classification Problem ◽  
Deep Convolutional Neural Network ◽  
Object Appearance

Object classification problem is essential in many applications nowadays. Human can easily classify objects in unconstrained environments easily. Classical classification techniques were far away from human performance. Thus, researchers try to mimic the human visual system till they reached the deep neural networks. This chapter gives a review and analysis in the field of the deep convolutional neural network usage in object classification under constrained and unconstrained environment. The chapter gives a brief review on the classical techniques of object classification and the development of bio-inspired computational models from neuroscience till the creation of deep neural networks. A review is given on the constrained environment issues: the hardware computing resources and memory, the object appearance and background, and the training and processing time. Datasets that are used to test the performance are analyzed according to the images environmental conditions, besides the dataset biasing is discussed.

scholarly journals Prediction of protein function using a deep convolutional neural network ensemble

2017 ◽  
Author(s):  
Evangelia I Zacharaki
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Convolutional Neural Network ◽  
Protein Function ◽  
Protein Structures ◽  
Function Prediction ◽  
Deep Convolutional Neural Network ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Feature Maps

Background. The availability of large databases containing high resolution three-dimensional (3D) models of proteins in conjunction with functional annotation allows the exploitation of advanced supervised machine learning techniques for automatic protein function prediction. Methods. In this work, novel shape features are extracted representing protein structure in the form of local (per amino acid) distribution of angles and amino acid distances, respectively. Each of the multi-channel feature maps is introduced into a deep convolutional neural network (CNN) for function prediction and the outputs are fused through Support Vector Machines (SVM) or a correlation-based k-nearest neighbor classifier. Two different architectures are investigated employing either one CNN per multi-channel feature set, or one CNN per image channel. Results. Cross validation experiments on enzymes (n = 44,661) from the PDB database achieved 90.1% correct classification demonstrating the effectiveness of the proposed method for automatic function annotation of protein structures. Discussion. The automatic prediction of protein function can provide quick annotations on extensive datasets opening the path for relevant applications, such as pharmacological target identification.

scholarly journals CN-waterfall: a deep convolutional neural network for multimodal physiological affect detection

2021 ◽  
Author(s):  
Nazanin Fouladgar ◽  
Marjan Alirezaie ◽  
Kary Främling
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Affective Computing ◽  
Expert Knowledge ◽  
Data Representation ◽  
Deep Convolutional Neural Network ◽  
Learning Methods ◽  
Level Data ◽  
Affect Detection

AbstractAffective computing solutions, in the literature, mainly rely on machine learning methods designed to accurately detect human affective states. Nevertheless, many of the proposed methods are based on handcrafted features, requiring sufficient expert knowledge in the realm of signal processing. With the advent of deep learning methods, attention has turned toward reduced feature engineering and more end-to-end machine learning. However, most of the proposed models rely on late fusion in a multimodal context. Meanwhile, addressing interrelations between modalities for intermediate-level data representation has been largely neglected. In this paper, we propose a novel deep convolutional neural network, called CN-Waterfall, consisting of two modules: Base and General. While the Base module focuses on the low-level representation of data from each single modality, the General module provides further information, indicating relations between modalities in the intermediate- and high-level data representations. The latter module has been designed based on theoretically grounded concepts in the Explainable AI (XAI) domain, consisting of four different fusions. These fusions are mainly tailored to correlation- and non-correlation-based modalities. To validate our model, we conduct an exhaustive experiment on WESAD and MAHNOB-HCI, two publicly and academically available datasets in the context of multimodal affective computing. We demonstrate that our proposed model significantly improves the performance of physiological-based multimodal affect detection.

scholarly journals Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Hua Xie ◽  
Minghua Zhang ◽  
Jiaming Ge ◽  
Xinfang Dong ◽  
Haiyan Chen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Data Representation ◽  
Air Traffic ◽  
Deep Convolutional Neural Network ◽  
Basic Unit ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Traffic Scenario

A sector is a basic unit of airspace whose operation is managed by air traffic controllers. The operation complexity of a sector plays an important role in air traffic management system, such as airspace reconfiguration, air traffic flow management, and allocation of air traffic controller resources. Therefore, accurate evaluation of the sector operation complexity (SOC) is crucial. Considering there are numerous factors that can influence SOC, researchers have proposed several machine learning methods recently to evaluate SOC by mining the relationship between factors and complexity. However, existing studies rely on hand-crafted factors, which are computationally difficult, specialized background required, and may limit the evaluation performance of the model. To overcome these problems, this paper for the first time proposes an end-to-end SOC learning framework based on deep convolutional neural network (CNN) specifically for free of hand-crafted factors environment. A new data representation, i.e., multichannel traffic scenario image (MTSI), is proposed to represent the overall air traffic scenario. A MTSI is generated by splitting the airspace into a two-dimension grid map and filled with navigation information. Motivated by the applications of deep learning network, the specific CNN model is introduced to automatically extract high-level traffic features from MTSIs and learn the SOC pattern. Thus, the model input is determined by combining multiple image channels composed of air traffic information, which are used to describe the traffic scenario. The model output is SOC levels for the target sector. The experimental results using a real dataset from the Guangzhou airspace sector in China show that our model can effectively extract traffic complexity information from MTSIs and achieve promising performance than traditional machine learning methods. In practice, our work can be flexibly and conveniently applied to SOC evaluation without the additional calculation of hand-crafted factors.

A Deep Convolutional Neural Network to Improve the Prediction of Protein Secondary Structure

2020 ◽  
Vol 15 (7) ◽  
pp. 767-777
Author(s):  
Lin Guo ◽  
Qian Jiang ◽  
Xin Jin ◽  
Lin Liu ◽  
Wei Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Secondary Structure ◽  
Convolutional Neural Network ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Deep Convolutional Neural Network ◽  
Dimensional Structure ◽  
Evolutionary Information ◽  
Protein Secondary Structures

Background: Protein secondary structure prediction (PSSP) is a fundamental task in bioinformatics that is helpful for understanding the three-dimensional structure and biological function of proteins. Many neural network-based prediction methods have been developed for protein secondary structures. Deep learning and multiple features are two obvious means to improve prediction accuracy. Objective: To promote the development of PSSP, a deep convolutional neural network-based method is proposed to predict both the eight-state and three-state of protein secondary structure. Methods: In this model, sequence and evolutionary information of proteins are combined as multiple input features after preprocessing. A deep convolutional neural network with no pooling layer and connection layer is then constructed to predict the secondary structure of proteins. L2 regularization, batch normalization, and dropout techniques are employed to avoid over-fitting and obtain better prediction performance, and an improved cross-entropy is used as the loss function. Results: Our proposed model can obtain Q3 prediction results of 86.2%, 84.5%, 87.8%, and 84.7%, respectively, on CullPDB, CB513, CASP10 and CASP11 datasets, with corresponding Q8 prediction results of 74.1%, 70.5%, 74.9%, and 71.3%. Conclusion: We have proposed the DCNN-SS deep convolutional-network-based PSSP method, and experimental results show that DCNN-SS performs competitively with other methods.

scholarly journals Prediction of protein function using a deep convolutional neural network ensemble

2017 ◽  
Author(s):  
Evangelia I Zacharaki
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Convolutional Neural Network ◽  
Protein Function ◽  
Protein Structures ◽  
Function Prediction ◽  
Deep Convolutional Neural Network ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Feature Maps

Background. The availability of large databases containing high resolution three-dimensional (3D) models of proteins in conjunction with functional annotation allows the exploitation of advanced supervised machine learning techniques for automatic protein function prediction. Methods. In this work, novel shape features are extracted representing protein structure in the form of local (per amino acid) distribution of angles and amino acid distances, respectively. Each of the multi-channel feature maps is introduced into a deep convolutional neural network (CNN) for function prediction and the outputs are fused through Support Vector Machines (SVM) or a correlation-based k-nearest neighbor classifier. Two different architectures are investigated employing either one CNN per multi-channel feature set, or one CNN per image channel. Results. Cross validation experiments on enzymes (n = 44,661) from the PDB database achieved 90.1% correct classification demonstrating the effectiveness of the proposed method for automatic function annotation of protein structures. Discussion. The automatic prediction of protein function can provide quick annotations on extensive datasets opening the path for relevant applications, such as pharmacological target identification.

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.

Sign in / Sign up

Export Citation Format

Share Document