Computational protein structure prediction is very important for many applications in bioinformatics. In the process of predicting protein structures, it is essential to accurately assess the quality of generated mode...
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ISBN:
(纸本)9781479914821
Computational protein structure prediction is very important for many applications in bioinformatics. In the process of predicting protein structures, it is essential to accurately assess the quality of generated models. Although many single-model quality assessment (QA) methods have been developed, their accuracy is not high enough for most real applications. In this paper, a new approach based on C-a atoms distance matrix and machine learning methods is proposed for single-model QA and the identification of native-like models. Different from existing energy/scoring functions and consensus approaches, this new approach is purely geometry based. Furthermore, a novel algorithm based on deep learning techniques, called DL-Pro, is proposed. For a protein model, DL-Pro uses its distance matrix that contains pairwise distances between two residues' C-α atoms in the model, which sometimes is also called contact map, as an orientation-independent representation. From training examples of distance matrices corresponding to good and bad models, DL-Pro learns a stacked autoencoder network as a classifier. In experiments on selected targets from the Critical Assessment of Structure Prediction (CASP) competition, DL-Pro obtained promising results, outperforming state-of-the-art energy/scoring functions, including OPUS-CA, DOPE, DFIRE, and RW.
Since industrial process data often presents complexity and nonlinearity,this study proposes a deep learning model based on semi-supervised Inter-Relational Mahalanobis stacked autoencoder(IRM-SAE) to learn deep fault...
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Since industrial process data often presents complexity and nonlinearity,this study proposes a deep learning model based on semi-supervised Inter-Relational Mahalanobis stacked autoencoder(IRM-SAE) to learn deep fault-relevant features of process data for fault ***,the Inter-Relational Mahalanobis loss function is introduced to learn meaningful inter-relational distribution features within the ***,active time-frame preprocessing is utilized to capture dynamic features of ***,to fully utilize both labeled and unlabeled data in industrial processes,the semi-supervised strategy is introduced to learn fault-related features for better fault ***,the Tennessee Eastman process is utilized to validate the effectiveness of the proposed *** experimental results show that IRM-SAE outperforms other deep learning models with an average fault classification accuracy of 96.59%.
Modern industrial process data often exhibit nonlinear and dynamic *** deep learning methods,such as stacked autoencoder(SAE),have excellent nonlinear feature learning capabilities,but they ignore the dynamic correlat...
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Modern industrial process data often exhibit nonlinear and dynamic *** deep learning methods,such as stacked autoencoder(SAE),have excellent nonlinear feature learning capabilities,but they ignore the dynamic correlation between process *** learning based on manifold learning using neighborhood structure preserving has been widely used in industrial dynamic process ***,most of them extract linear features and the complex nonlinearities in process data are largely ***,a spatial temporal neighborhood preserving stack autoencoder(STNP-SAE) is proposed to learn static neighborhood features and dynamic neighborhood features of process data simultaneously in this ***,STNP-SAE is utilized to construct a soft sensor framework for quality *** effectiveness and prediction performance of the proposed method are validated on a practical hydrocracking process.
Deep Learning is a field included in to Artificial Intelligence. It allows computational models to learn multiple levels of abstraction with multiple processing layers. This Artificial Neural Networks gives state-of-a...
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Deep Learning is a field included in to Artificial Intelligence. It allows computational models to learn multiple levels of abstraction with multiple processing layers. This Artificial Neural Networks gives state-of-art performance in various fields like Computer Vision, Speech recognition and different domain like bioinformatics. There are mainly three architectures of Deep Learning Convolution Neural Network, Deep Neural Network and Recurrent Neural Network which provides the higher level of representation of data at each next layer. Deep Learning is required to classify high dimensional data like images, audio, video and biological data.
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