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Bowen W. Research on nonlinear calibration of mine catalytic-combustion-based combustible-gas sensor based on RBF neural network. Heliyon 2023; 9:e14055. [PMID: 36915543 PMCID: PMC10006743 DOI: 10.1016/j.heliyon.2023.e14055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 03/02/2023] Open
Abstract
After using a catalytic-combustion-based combustible-gas sensor (catalytic sensor) underground for a period of time, the sensitivity drifts due to environmental factors such as coal dust, temperature, and humidity. It is necessary to adjust the sensor regularly to ensure its accuracy. In this paper, RBF neural network technology is introduced to fit a nonlinear continuous function to solve the problem of the output error of the sensor being too large due to linear adjustment. Through experimental analysis, it is demonstrated that the RBF neural network model has a higher convergence speed and smaller error than other network models. By embedding the RBF network model into a sensor microcontroller, the error of traditional linear calibration can be reduced by two orders of magnitude and the measurement accuracy of the catalytic sensor can be greatly improved.
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Affiliation(s)
- Wang Bowen
- China Coal Technology Engineering Group, Chongqing Research Institute, Chongqing, 410000, China
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Niu M, Zhang J, Li Y, Wang C, Liu Z, Ding H, Zou Q, Ma Q. CirRNAPL: A web server for the identification of circRNA based on extreme learning machine. Comput Struct Biotechnol J 2020; 18:834-42. [PMID: 32308930 DOI: 10.1016/j.csbj.2020.03.028] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Revised: 03/29/2020] [Accepted: 03/29/2020] [Indexed: 12/27/2022] Open
Abstract
Circular RNA (circRNA) plays an important role in the development of diseases, and it provides a novel idea for drug development. Accurate identification of circRNAs is important for a deeper understanding of their functions. In this study, we developed a new classifier, CirRNAPL, which extracts the features of nucleic acid composition and structure of the circRNA sequence and optimizes the extreme learning machine based on the particle swarm optimization algorithm. We compared CirRNAPL with existing methods, including blast, on three datasets and found CirRNAPL significantly improved the identification accuracy for the three datasets, with accuracies of 0.815, 0.802, and 0.782, respectively. Additionally, we performed sequence alignment on 564 sequences of the independent detection set of the third data set and analyzed the expression level of circRNAs. Results showed the expression level of the sequence is positively correlated with the abundance. A user-friendly CirRNAPL web server is freely available at http://server.malab.cn/CirRNAPL/.
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Key Words
- ACC, Accuracy
- CNN, Convolutional Neural Networks
- Circular RNA
- DAC, Dinucleotide-based auto-covariance
- DACC, Dinucleotide-based auto-cross-covariance
- DCC, Dinucleotide-based cross-covariance
- ELM, extreme learning machine
- Expression level
- Extreme learning machine
- GAC, Geary autocorrelation
- Identification
- MAC, Moran autocorrelation
- MCC, Matthews Correlation Coefficient
- MRMD, Maximum-Relevance-Maximum-Distance
- NMBAC, Normalized Moreau–Broto autocorrelation
- PC-PseDNC-General, General parallel correlation pseudo-dinucleotide composition
- PCGs, protein coding genes
- PSO, particle swarm optimization algorithm
- Particle swarm optimization algorithm
- PseDPC, Pseudo-distance structure status pair composition
- PseSSC, Pseudo-structure status composition
- RBF, radial basis function
- RF, random forest
- SC-PseDNC-General, General series correlation pseudo-dinucleotide composition
- SE, Sensitivity
- SP, Specifity
- SVM, support vector machine
- Triplet, Local structure-sequence triplet element
- circRNA, circular RNA
- lncRNAs, long non-coding RNAs
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Yang X, Yang S, Li Q, Wuchty S, Zhang Z. Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method. Comput Struct Biotechnol J 2019; 18:153-161. [PMID: 31969974 PMCID: PMC6961065 DOI: 10.1016/j.csbj.2019.12.005] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 11/29/2019] [Accepted: 12/10/2019] [Indexed: 12/11/2022] Open
Abstract
The identification of human-virus protein-protein interactions (PPIs) is an essential and challenging research topic, potentially providing a mechanistic understanding of viral infection. Given that the experimental determination of human-virus PPIs is time-consuming and labor-intensive, computational methods are playing an important role in providing testable hypotheses, complementing the determination of large-scale interactome between species. In this work, we applied an unsupervised sequence embedding technique (doc2vec) to represent protein sequences as rich feature vectors of low dimensionality. Training a Random Forest (RF) classifier through a training dataset that covers known PPIs between human and all viruses, we obtained excellent predictive accuracy outperforming various combinations of machine learning algorithms and commonly-used sequence encoding schemes. Rigorous comparison with three existing human-virus PPI prediction methods, our proposed computational framework further provided very competitive and promising performance, suggesting that the doc2vec encoding scheme effectively captures context information of protein sequences, pertaining to corresponding protein-protein interactions. Our approach is freely accessible through our web server as part of our host-pathogen PPI prediction platform (http://zzdlab.com/InterSPPI/). Taken together, we hope the current work not only contributes a useful predictor to accelerate the exploration of human-virus PPIs, but also provides some meaningful insights into human-virus relationships.
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Key Words
- AC, Auto Covariance
- ACC, Accuracy
- AUC, area under the ROC curve
- AUPRC, area under the PR curve
- Adaboost, Adaptive Boosting
- CT, Conjoint Triad
- Doc2vec
- Embedding
- Human-virus interaction
- LD, Local Descriptor
- MCC, Matthews correlation coefficient
- ML, machine learning
- MLP, Multiple Layer Perceptron
- MS, mass spectroscopy
- Machine learning
- PPIs, protein-protein interactions
- PR, Precision-Recall
- Prediction
- Protein-protein interaction
- RBF, radial basis function
- RF, Random Forest
- ROC, Receiver Operating Characteristic
- SGD, stochastic gradient descent
- SVM, Support Vector Machine
- Y2H, yeast two-hybrid
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Affiliation(s)
- Xiaodi Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Shiping Yang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Qinmengge Li
- National Demonstration Center for Experimental Biological Sciences Education, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Stefan Wuchty
- Dept. of Computer Science, University of Miami, Miami, FL 33146, USA
- Dept. of Biology, University of Miami, Miami, FL 33146, USA
- Center of Computational Science, University of Miami, Miami, FL 33146, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
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