Predicting protein–protein interactions from protein sequences by a stacked sparse autoencoder deep neural network

2017 ◽  
Vol 13 (7) ◽  
pp. 1336-1344 ◽  
Author(s):  
Yan-Bin Wang ◽  
Zhu-Hong You ◽  
Xiao Li ◽  
Tong-Hai Jiang ◽  
Xing Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Deep Neural Network ◽  
Protein Sequences ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
P Protein ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Protein–protein interactions (PPIs) play an important role in most of the biological processes.

scholarly journals Construction of a Sensitive and Speed Invariant Gearbox Fault Diagnosis Model Using an Incorporated Utilizing Adaptive Noise Control and a Stacked Sparse Autoencoder-Based Deep Neural Network

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 18
Author(s):  
Cong Dai Nguyen ◽  
Alexander E. Prosvirin ◽  
Cheol Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Noise Control ◽  
Vibration Characteristics ◽  
Vibration Signals ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Diagnosis Model ◽  
Adaptive Noise

Gearbox fault diagnosis based on the analysis of vibration signals has been a major research topic for a few decades due to the advantages of vibration characteristics. Such characteristics are used for early fault detection to guarantee the enhanced safety of complex systems and their cost-effective operation. There exist many fault diagnosis models that have been developed for classifying various fault types in gearboxes. However, the classification results of the conventional fault classification models degrade when they are applied to gearbox systems with multi-level tooth cut gear (MTCG) faults operating under variable shaft speeds. These conditions cause difficulty in discriminating the gear fault types. Due to the improved computational capabilities of modern systems, the application of deep neural networks (DNNs) is getting popular in a variety of research fields, such as image and natural language processing. DNNs are capable of improving the classification results even when addressing complex problems such as diagnosing gearbox MTCG faults. In this research, an adaptive noise control (ANC) and a stacked sparse autoencoder–based deep neural network (SSA-DNN) are used to construct a sensitive fault diagnosis model that can diagnose a gearbox system with MTCG fault types under varying shaft rotation speeds, despite its complicatedness. An ANC is applied to gear vibration characteristics to remove a significant level of noise along the frequency spectrum of vibration signals to fix the most fault-informative components of each fault case. Next, the autoencoder learns the gear faults characteristic features from these fault-informative components to separate the fault types considered in this study. Furthermore, the implementation of the SSA-DNN is substituted for feature extraction, feature selection, and the classification processes in traditional fault diagnosis schemes by high-performance unity. The experimental results show that the proposed model outperforms conventional methodologies with higher classification accuracy.

scholarly journals Mapping molecular binding by means of conformational dynamics measurements

RSC Advances ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. 867-876 ◽  
Author(s):  
Noelle M. do Nascimento ◽  
Augusto Juste-Dolz ◽  
Paulo R. Bueno ◽  
Isidro Monzó ◽  
Roberto Tejero ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Conformational Dynamics ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
Molecular Binding ◽  
P Protein

Protein–protein interactions are key in virtually all biological processes.

scholarly journals Deep Neural Network Hardware Implementation Based on Stacked Sparse Autoencoder

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 40674-40694 ◽  
Author(s):  
Maria G. F. Coutinho ◽  
Matheus F. Torquato ◽  
Marcelo A. C. Fernandes
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Hardware Implementation ◽  
Neural Network Hardware ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Detecting reliable non interacting proteins (NIPs) significantly enhancing the computational prediction of protein–protein interactions using machine learning methods

2016 ◽  
Vol 12 (3) ◽  
pp. 778-785 ◽  
Author(s):  
A. Srivastava ◽  
G. Mazzocco ◽  
A. Kel ◽  
L. S. Wyrwicz ◽  
D. Plewczynski
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Computational Prediction ◽  
Vital Role ◽  
Biological Processes ◽  
Interacting Proteins ◽  
Protein Protein Interactions ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
P Protein

Protein–protein interactions (PPIs) play a vital role in most biological processes.

An Integrated Prediction Method for Identifying Protein-Protein Interactions

2020 ◽  
Vol 17 (4) ◽  
pp. 271-286
Author(s):  
Chang Xu ◽  
Limin Jiang ◽  
Zehua Zhang ◽  
Xuyao Yu ◽  
Renhai Chen ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Feature Vector ◽  
Prediction Method ◽  
Feature Representation ◽  
Protein Interaction Networks ◽  
Learning Approach ◽  
Biological Processes ◽  
Integrated Learning ◽  
Protein Protein Interactions ◽  
Training Process

Background: Protein-Protein Interactions (PPIs) play a key role in various biological processes. Many methods have been developed to predict protein-protein interactions and protein interaction networks. However, many existing applications are limited, because of relying on a large number of homology proteins and interaction marks. Methods: In this paper, we propose a novel integrated learning approach (RF-Ada-DF) with the sequence-based feature representation, for identifying protein-protein interactions. Our method firstly constructs a sequence-based feature vector to represent each pair of proteins, viaMultivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC). Then, we feed the 638- dimentional features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Meanwhile, we also employ double fault detection in order to suppress over-adaptation during the training process. Results: To evaluate the performance of our method, we conduct several comprehensive tests for PPIs prediction. On the H. pyloridataset, our method achieves 88.16% accuracy and 87.68% sensitivity, the accuracy of our method is increased by 0.57%. On the S. cerevisiaedataset, our method achieves 95.77% accuracy and 93.36% sensitivity, the accuracy of our method is increased by 0.76%. On the Humandataset, our method achieves 98.16% accuracy and 96.80% sensitivity, the accuracy of our method is increased by 0.6%. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. The datasets and codes are available at https://github.com/guofei-tju/RF-Ada-DF.git.

scholarly journals Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Muhammad Aqeel Aslam ◽  
Cuili Xue ◽  
Yunsheng Chen ◽  
Amin Zhang ◽  
Manhua Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Gastric Cancer ◽  
Feature Extraction ◽  
Early Gastric Cancer ◽  
Breath Analysis ◽  
Cancer Classification ◽  
Unlabeled Data ◽  
Softmax Classifier ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

AbstractDeep learning is an emerging tool, which is regularly used for disease diagnosis in the medical field. A new research direction has been developed for the detection of early-stage gastric cancer. The computer-aided diagnosis (CAD) systems reduce the mortality rate due to their effectiveness. In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples. A Softmax classifier was then integrated to the proposed method of feature extraction, to classify gastric cancer from the breath samples. Precisely, we identified fifty peaks in each spectrum to distinguish the EGC, AGC, and healthy persons. This CAD system reduces the distance between the input and output by learning the features and preserve the structure of the input data set of breath samples. The features were extracted from the unlabeled data of the breath samples. After the completion of unsupervised training, autoencoders with Softmax classifier were cascaded to develop a deep stacked sparse autoencoder neural network. In last, fine-tuning of the developed neural network was carried out with labeled training data to make the model more reliable and repeatable. The proposed deep stacked sparse autoencoder neural network architecture exhibits excellent results, with an overall accuracy of 98.7% for advanced gastric cancer classification and 97.3% for early gastric cancer detection using breath analysis. Moreover, the developed model produces an excellent result for recall, precision, and f score value, making it suitable for clinical application.

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