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2. Evolutionary Algorithm for RNA Secondary Structure Prediction Based on Simulated SHAPE Data.
- Author
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Montaseri, Soheila, Ganjtabesh, Mohammad, and Zare-Mirakabad, Fatemeh
- Subjects
MOLECULAR structure of RNA ,BIOLOGICAL evolution ,GENETICS ,NON-coding RNA ,GENETIC algorithms ,THERMODYNAMICS ,COMPUTER simulation - Abstract
Background: Non-coding RNAs perform a wide range of functions inside the living cells that are related to their structures. Several algorithms have been proposed to predict RNA secondary structure based on minimum free energy. Low prediction accuracy of these algorithms indicates that free energy alone is not sufficient to predict the functional secondary structure. Recently, the obtained information from the SHAPE experiment greatly improves the accuracy of RNA secondary structure prediction by adding this information to the thermodynamic free energy as pseudo-free energy. Method: In this paper, a new method is proposed to predict RNA secondary structure based on both free energy and SHAPE pseudo-free energy. For each RNA sequence, a population of secondary structures is constructed and their SHAPE data are simulated. Then, an evolutionary algorithm is used to improve each structure based on both free and pseudo-free energies. Finally, a structure with minimum summation of free and pseudo-free energies is considered as the predicted RNA secondary structure. Results and Conclusions: Computationally simulating the SHAPE data for a given RNA sequence requires its secondary structure. Here, we overcome this limitation by employing a population of secondary structures. This helps us to simulate the SHAPE data for any RNA sequence and consequently improves the accuracy of RNA secondary structure prediction as it is confirmed by our experiments. The source code and web server of our proposed method are freely available at . [ABSTRACT FROM AUTHOR]
- Published
- 2016
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3. Fuzzy jump wavelet neural network based on rule induction for dynamic nonlinear system identification with real data applications
- Author
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Miguel Angel Mañanas, Mohsen Kharazihai Isfahani, Hamid Reza Marateb, Maryam Zekri, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, and Universitat Politècnica de Catalunya. BIOART - BIOsignal Analysis for Rehabilitation and Therapy
- Subjects
Computer science ,02 engineering and technology ,Systems Science ,Wavelet ,Informàtica [Àrees temàtiques de la UPC] ,Animal Cells ,0202 electrical engineering, electronic engineering, information engineering ,Neurons ,Multidisciplinary ,Fuzzy rule ,Artificial neural network ,Nonlinear system identification ,Approximation Methods ,Physics ,Linear model ,Classical Mechanics ,Bioassays and Physiological Analysis ,Physical Sciences ,Piecewise ,Medicine ,020201 artificial intelligence & image processing ,Cellular Types ,Cellular Structures and Organelles ,Algorithm ,Muscle Electrophysiology ,Algorithms ,Research Article ,Computer and Information Sciences ,Neural Networks ,Science ,Research and Analysis Methods ,Fuzzy logic ,Neural networks (Computer science) ,Motion ,Fuzzy Logic ,Artificial Intelligence ,Genetic algorithm ,Xarxes neuronals (Informàtica) ,Computer Simulation ,Rule induction ,Electromyography ,020208 electrical & electronic engineering ,Electrophysiological Techniques ,Biology and Life Sciences ,Fuzzy systems ,Cell Biology ,Nonlinear system ,Nonlinear Dynamics ,Torque ,Cell Signaling Structures ,Sistemes borrosos ,Cellular Neuroscience ,Linear Models ,Neural Networks, Computer ,Nonlinear Systems ,Mathematics ,Neuroscience - Abstract
Aim Fuzzy wavelet neural network (FWNN) has proven to be a promising strategy in the identification of nonlinear systems. The network considers both global and local properties, deals with imprecision present in sensory data, leading to desired precisions. In this paper, we proposed a new FWNN model nominated “Fuzzy Jump Wavelet Neural Network” (FJWNN) for identifying dynamic nonlinear-linear systems, especially in practical applications. Methods The proposed FJWNN is a fuzzy neural network model of the Takagi-Sugeno-Kang type whose consequent part of fuzzy rules is a linear combination of input regressors and dominant wavelet neurons as a sub-jump wavelet neural network. Each fuzzy rule can locally model both linear and nonlinear properties of a system. The linear relationship between the inputs and the output is learned by neurons with linear activation functions, whereas the nonlinear relationship is locally modeled by wavelet neurons. Orthogonal least square (OLS) method and genetic algorithm (GA) are respectively used to purify the wavelets for each sub-JWNN. In this paper, fuzzy rule induction improves the structure of the proposed model leading to less fuzzy rules, inputs of each fuzzy rule and model parameters. The real-world gas furnace and the real electromyographic (EMG) signal modeling problem are employed in our study. In the same vein, piecewise single variable function approximation, nonlinear dynamic system modeling, and Mackey–Glass time series prediction, ratify this method superiority. The proposed FJWNN model is compared with the state-of-the-art models based on some performance indices such as RMSE, RRSE, Rel ERR%, and VAF%. Results The proposed FJWNN model yielded the following results: RRSE (mean±std) of 10e-5±6e-5 for piecewise single-variable function approximation, RMSE (mean±std) of 2.6–4±2.6e-4 for the first nonlinear dynamic system modelling, RRSE (mean±std) of 1.59e-3±0.42e-3 for Mackey–Glass time series prediction, RMSE of 0.3421 for gas furnace modelling and VAF% (mean±std) of 98.24±0.71 for the EMG modelling of all trial signals, indicating a significant enhancement over previous methods. Conclusions The FJWNN demonstrated promising accuracy and generalization while moderating network complexity. This improvement is due to applying main useful wavelets in combination with linear regressors and using fuzzy rule induction. Compared to the state-of-the-art models, the proposed FJWNN yielded better performance and, therefore, can be considered a novel tool for nonlinear system identification
- Published
- 2019
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