Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121674
A. Jaya, M. Muhamad, Md. Nizam Abd Rahman, Z. Napiah, S. Hashim, H. Haron
In this paper, a new approach in predicting the hardness of Titanium Aluminum Nitrite (TiAlN) coatings using hybrid RSM-fuzzy model is implemented. TiAlN coatings are usually used in high-speed machining due to its excellent surface hardness and wear resistance. The TiAlN coatings were produced using Physical Vapor Deposition (PVD) magnetron sputtering process. A statistical design of experiment called Response Surface Methodology (RSM) was used in collecting optimized data. The fuzzy rules were constructed using actual experimental data. Meanwhile, the hardness values were generated using the RSM hardness model. Triangular shape of membership functions were used for inputs as well as output. The substrate sputtering power, bias voltage and temperature were selected as the input parameters and the coating hardness as an output of the process. The results of hybrid RSM-fuzzy model were compared against the experimental result and fuzzy single model based on the percentage error, mean square error (MSE), co-efficient determination (R2) and model accuracy. The result indicated that the hybrid RSM-fuzzy model obtained the better result compared to the fuzzy single model. The hybrid model with seven triangular membership functions gave an excellent result with respective average percentage error, MSE, R2 and model accuracy were 11.5%, 1.09, 0.989 and 88.49%. The good performance of the hybrid model showed that the RSM hardness model could be embedded in fuzzy rule-based model to assist in generating more fuzzy rules in order to obtain better prediction result.
{"title":"Hybrid RSM-fuzzy modeling for hardness prediction of TiAlN coatings","authors":"A. Jaya, M. Muhamad, Md. Nizam Abd Rahman, Z. Napiah, S. Hashim, H. Haron","doi":"10.1109/ISDA.2011.6121674","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121674","url":null,"abstract":"In this paper, a new approach in predicting the hardness of Titanium Aluminum Nitrite (TiAlN) coatings using hybrid RSM-fuzzy model is implemented. TiAlN coatings are usually used in high-speed machining due to its excellent surface hardness and wear resistance. The TiAlN coatings were produced using Physical Vapor Deposition (PVD) magnetron sputtering process. A statistical design of experiment called Response Surface Methodology (RSM) was used in collecting optimized data. The fuzzy rules were constructed using actual experimental data. Meanwhile, the hardness values were generated using the RSM hardness model. Triangular shape of membership functions were used for inputs as well as output. The substrate sputtering power, bias voltage and temperature were selected as the input parameters and the coating hardness as an output of the process. The results of hybrid RSM-fuzzy model were compared against the experimental result and fuzzy single model based on the percentage error, mean square error (MSE), co-efficient determination (R2) and model accuracy. The result indicated that the hybrid RSM-fuzzy model obtained the better result compared to the fuzzy single model. The hybrid model with seven triangular membership functions gave an excellent result with respective average percentage error, MSE, R2 and model accuracy were 11.5%, 1.09, 0.989 and 88.49%. The good performance of the hybrid model showed that the RSM hardness model could be embedded in fuzzy rule-based model to assist in generating more fuzzy rules in order to obtain better prediction result.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121506877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121839
Pablo Bermejo, L. D. L. Ossa, J. A. Gamez, J. M. Puerta
Feature subset selection has become an expensive process due to the relatively recent appearance of high-dimensional databases. Thus, not only the need has arisen for reducing the dimensionality of these datasets, but also for doing it in an efficient way. We propose a new backward search, where attributes are removed given several smart criteria found in the literature and, besides, it is guided using a heuristic which reduces the cost and needed number of evaluations commonly expected from a backward search. Besides, we do not only propose the design of a new forward-backward algorithm but we also provide an experimental study of different criteria to decide the removal of attributes. The result is a very competitive algorithm which does not exceed the in-practice linear complexity while obtaining selected subsets of features with lower cardinality than other state-of-the-art algorithms.
{"title":"A study on different backward feature selection criteria over high-dimensional databases","authors":"Pablo Bermejo, L. D. L. Ossa, J. A. Gamez, J. M. Puerta","doi":"10.1109/ISDA.2011.6121839","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121839","url":null,"abstract":"Feature subset selection has become an expensive process due to the relatively recent appearance of high-dimensional databases. Thus, not only the need has arisen for reducing the dimensionality of these datasets, but also for doing it in an efficient way. We propose a new backward search, where attributes are removed given several smart criteria found in the literature and, besides, it is guided using a heuristic which reduces the cost and needed number of evaluations commonly expected from a backward search. Besides, we do not only propose the design of a new forward-backward algorithm but we also provide an experimental study of different criteria to decide the removal of attributes. The result is a very competitive algorithm which does not exceed the in-practice linear complexity while obtaining selected subsets of features with lower cardinality than other state-of-the-art algorithms.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115800366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121652
A. Akbarimajd, B. Sobhani, H. Shayeghi
A neural-network based nonlinear autoregressive moving average controller is proposed for a DG in its islanding mode. When the DG is disconnected from the utility grid, its grid connected dq-current controller is disabled and the proposed controller undertakes control task. In the proposed controller, dynamics of the system is modeled and identified by two MLP neural networks. Then the controller is designed based on the constructed model. The performance of the controller is evaluated by simulations in SIMULINK/MATLAB.
{"title":"Neural network based NARMA controller for a DG in islanding mode","authors":"A. Akbarimajd, B. Sobhani, H. Shayeghi","doi":"10.1109/ISDA.2011.6121652","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121652","url":null,"abstract":"A neural-network based nonlinear autoregressive moving average controller is proposed for a DG in its islanding mode. When the DG is disconnected from the utility grid, its grid connected dq-current controller is disabled and the proposed controller undertakes control task. In the proposed controller, dynamics of the system is modeled and identified by two MLP neural networks. Then the controller is designed based on the constructed model. The performance of the controller is evaluated by simulations in SIMULINK/MATLAB.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122745014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121689
S. Cateni, V. Colla, M. Vannucci
The paper deals a novel resampling method in order to cope with imbalanced dataset in binary classification problems. Imbalanced datasets are frequently found in many industrial applications: for instance, the occurrence of particular product defects or machine faults are rare events whose detection is of utmost importance. In this paper a new resampling method combining an oversampling and an undersampling techniques is treated. In order to prove the effectiveness of the proposed approach, several tests have been developed. Two classifiers based on Support Vector Machine and Decision Tree have been designed, which are applied for binary classification on four datasets: a synthetic dataset, a widely used public dataset and two industrial datasets. The obtained results are presented and discussed in the paper; in particular, the performance that is achieved by the two classifiers through our resampling approach is compared to the ones that are obtained without any resampling and through the classical SMOTE approach, respectively.
{"title":"Novel resampling method for the classification of imbalanced datasets for industrial and other real-world problems","authors":"S. Cateni, V. Colla, M. Vannucci","doi":"10.1109/ISDA.2011.6121689","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121689","url":null,"abstract":"The paper deals a novel resampling method in order to cope with imbalanced dataset in binary classification problems. Imbalanced datasets are frequently found in many industrial applications: for instance, the occurrence of particular product defects or machine faults are rare events whose detection is of utmost importance. In this paper a new resampling method combining an oversampling and an undersampling techniques is treated. In order to prove the effectiveness of the proposed approach, several tests have been developed. Two classifiers based on Support Vector Machine and Decision Tree have been designed, which are applied for binary classification on four datasets: a synthetic dataset, a widely used public dataset and two industrial datasets. The obtained results are presented and discussed in the paper; in particular, the performance that is achieved by the two classifiers through our resampling approach is compared to the ones that are obtained without any resampling and through the classical SMOTE approach, respectively.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133574265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/isda.2011.6121768
David Gutiérrez-Avilés, Cristina Rubio-Escudero, José Cristóbal Riquelme Santos
The analysis of microarray data is a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested. Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping allowing genes to be evaluated only under a subset of the conditions and not under all of them. However, this technique is not appropriate for the analysis of temporal microarray data in which the genes are evaluated under certain conditions at several time points. In this paper, we propose the TriGen algorithm, which finds triclusters that take into account the experimental conditions and the time points, using evolutionary computation, in particular genetic algorithms, enabling the evaluation of the gene's behavior under subsets of conditions and of time points.
{"title":"Triclustering on temporary microarray data using the TriGen algorithm","authors":"David Gutiérrez-Avilés, Cristina Rubio-Escudero, José Cristóbal Riquelme Santos","doi":"10.1109/isda.2011.6121768","DOIUrl":"https://doi.org/10.1109/isda.2011.6121768","url":null,"abstract":"The analysis of microarray data is a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested. Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping allowing genes to be evaluated only under a subset of the conditions and not under all of them. However, this technique is not appropriate for the analysis of temporal microarray data in which the genes are evaluated under certain conditions at several time points. In this paper, we propose the TriGen algorithm, which finds triclusters that take into account the experimental conditions and the time points, using evolutionary computation, in particular genetic algorithms, enabling the evaluation of the gene's behavior under subsets of conditions and of time points.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121744415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121840
Et-Tahir Zemouri, Y. Chibani
Discrimination of machine printed and handwritten text is deemed as major problem in the recognition of the mixed texts. In this paper, we address the problem of identifying each type by using the Radon transform and Support Vector Machines, which is conducted at three steps: preprocessing, feature generation and classification. New set of features is generated from each word using the Radon transform. Classification is used to distinguish printed text from handwritten. The proposed system is tested on IAM databases. The recognition rate of the proposed method is calculated to be over 98%.
{"title":"Machine printed handwritten text discrimination using Radon transform and SVM classifier","authors":"Et-Tahir Zemouri, Y. Chibani","doi":"10.1109/ISDA.2011.6121840","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121840","url":null,"abstract":"Discrimination of machine printed and handwritten text is deemed as major problem in the recognition of the mixed texts. In this paper, we address the problem of identifying each type by using the Radon transform and Support Vector Machines, which is conducted at three steps: preprocessing, feature generation and classification. New set of features is generated from each word using the Radon transform. Classification is used to distinguish printed text from handwritten. The proposed system is tested on IAM databases. The recognition rate of the proposed method is calculated to be over 98%.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133553169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121720
M. Flores, J. A. Gamez, Ana M. Martínez, A. Salmerón
The Averaged One-Dependence Estimators (AODE) classifier is one of the most attractive semi-naive Bayesian classifiers and hence a good alternative to Naive Bayes (NB), as it obtains fairly low error rates maintaining under control the computational complexity. Unfortunately, as most of the methods designed within the framework of Bayesian networks, AODE is exclusively defined to deal with discrete variables. Several approaches to avoid the use of discretization pre-processing techniques have already been presented, all of them involving in lower or greater degree the assumption of (conditional) Gaussian distributions. In this paper, we propose the use of Mixture of Truncated Exponentials (MTEs), whose expressive power to accurately approximate the most commonly used distributions for hybrid networks has already been demonstrated. We perform experiments on the use of MTEs over a large group of datasets for the first time, and we analyze the importance of selecting a proper number of points when learning MTEs for NB and AODE, as we believe, it is decisive to provide accurate results.
{"title":"Mixture of truncated exponentials in supervised classification: Case study for the naive bayes and averaged one-dependence estimators classifiers","authors":"M. Flores, J. A. Gamez, Ana M. Martínez, A. Salmerón","doi":"10.1109/ISDA.2011.6121720","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121720","url":null,"abstract":"The Averaged One-Dependence Estimators (AODE) classifier is one of the most attractive semi-naive Bayesian classifiers and hence a good alternative to Naive Bayes (NB), as it obtains fairly low error rates maintaining under control the computational complexity. Unfortunately, as most of the methods designed within the framework of Bayesian networks, AODE is exclusively defined to deal with discrete variables. Several approaches to avoid the use of discretization pre-processing techniques have already been presented, all of them involving in lower or greater degree the assumption of (conditional) Gaussian distributions. In this paper, we propose the use of Mixture of Truncated Exponentials (MTEs), whose expressive power to accurately approximate the most commonly used distributions for hybrid networks has already been demonstrated. We perform experiments on the use of MTEs over a large group of datasets for the first time, and we analyze the importance of selecting a proper number of points when learning MTEs for NB and AODE, as we believe, it is decisive to provide accurate results.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134130759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121809
F. Zaro, M. Abido
In this paper, a multi-objective particle swarm optimization (MOPSO) technique is proposed for solving the optimal power flow (OPF) problem in a deregulated environment. The OPF problem is formulated as a nonlinear constrained multi-objective optimization problem where the fuel cost and wheeling cost are to be optimized simultaneously. MVA-km method is used to calculate the wheeling cost in the system. The proposed approach handles the problem as a true multi-objective optimization problem. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal solutions of the multi-objective OPF problem in one single run. In addition, the effectiveness of the proposed approach and its potential to solve the multi-objective OPF problem are confirmed. IEEE 30 bus system is considered to demonstrate the suitability of this algorithm
{"title":"Multi-objective particle swarm optimization for optimal power flow in a deregulated environment of power systems","authors":"F. Zaro, M. Abido","doi":"10.1109/ISDA.2011.6121809","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121809","url":null,"abstract":"In this paper, a multi-objective particle swarm optimization (MOPSO) technique is proposed for solving the optimal power flow (OPF) problem in a deregulated environment. The OPF problem is formulated as a nonlinear constrained multi-objective optimization problem where the fuel cost and wheeling cost are to be optimized simultaneously. MVA-km method is used to calculate the wheeling cost in the system. The proposed approach handles the problem as a true multi-objective optimization problem. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal solutions of the multi-objective OPF problem in one single run. In addition, the effectiveness of the proposed approach and its potential to solve the multi-objective OPF problem are confirmed. IEEE 30 bus system is considered to demonstrate the suitability of this algorithm","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133889630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121662
M. Laurino, Andrea Piarulli, R. Bedini, A. Gemignani, A. Pingitore, A. L'Abbate, A. Landi, P. Piaggi, D. Menicucci
Several methods for automatic heartbeat classification have been developed, but few efforts have been devoted to the recognition of the small ECG changes occurring in healthy people as a response to stimuli. Herein, we describe a procedure for the extraction, selection and classification of features summarizing morphological ECG changes. The proposed procedure is composed by the following stages: 1) extraction of a set of heartbeat morphological features; 2) selection of a subset of features; 3) subject normalization 4) classification. The selection of a subset of features enabled us to summarize ECG changes with only three non redundant features. In addition we performed a comparison between four classificators: k-nearest-neighbors (k-NN), artificial neural networks (ANN), support vector machines (SVM) and naive Bayes classifiers (nB). In order to cope with the possible non linear separation problem, we evaluated two strategies: a subject factor normalization on feature space and the usage of kernel functions for classifiers. The results of the comparison recommended the usage of subject normalization, irrespectively from the classificator: with or without normalization we had the best performance of classification for the linear-SVM and ANN.
{"title":"Comparative study of morphological ECG features classificators: An application on athletes undergone to acute physical stress","authors":"M. Laurino, Andrea Piarulli, R. Bedini, A. Gemignani, A. Pingitore, A. L'Abbate, A. Landi, P. Piaggi, D. Menicucci","doi":"10.1109/ISDA.2011.6121662","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121662","url":null,"abstract":"Several methods for automatic heartbeat classification have been developed, but few efforts have been devoted to the recognition of the small ECG changes occurring in healthy people as a response to stimuli. Herein, we describe a procedure for the extraction, selection and classification of features summarizing morphological ECG changes. The proposed procedure is composed by the following stages: 1) extraction of a set of heartbeat morphological features; 2) selection of a subset of features; 3) subject normalization 4) classification. The selection of a subset of features enabled us to summarize ECG changes with only three non redundant features. In addition we performed a comparison between four classificators: k-nearest-neighbors (k-NN), artificial neural networks (ANN), support vector machines (SVM) and naive Bayes classifiers (nB). In order to cope with the possible non linear separation problem, we evaluated two strategies: a subject factor normalization on feature space and the usage of kernel functions for classifiers. The results of the comparison recommended the usage of subject normalization, irrespectively from the classificator: with or without normalization we had the best performance of classification for the linear-SVM and ANN.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115721669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121826
R. Romero-Záliz, J. Arnedo-Fdez, I. Zwir, C. Val
Gene expression regulation is an intricate, dynamic phenomenon essential for all biological functions. The necessary instructions for gene expression are encoded in cis-regulatory elements that work together and interact with the RNA polymerase to confer spatial and temporal patterns of transcription. Therefore, the identification of these elements is currently an active area of research in computational analysis of regulatory sequences. However, the problem is difficult since the combinatorial interactions between the regulating factors can be very complex. Here we present a web server that identifies cis-regulatory modules given a set of transcription factor binding sites and, additionally, also RNA polymerase sites for a group of genes.
{"title":"Finding promoter profiles with multiobjective identification of cis-regulatory modules based on constraints","authors":"R. Romero-Záliz, J. Arnedo-Fdez, I. Zwir, C. Val","doi":"10.1109/ISDA.2011.6121826","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121826","url":null,"abstract":"Gene expression regulation is an intricate, dynamic phenomenon essential for all biological functions. The necessary instructions for gene expression are encoded in cis-regulatory elements that work together and interact with the RNA polymerase to confer spatial and temporal patterns of transcription. Therefore, the identification of these elements is currently an active area of research in computational analysis of regulatory sequences. However, the problem is difficult since the combinatorial interactions between the regulating factors can be very complex. Here we present a web server that identifies cis-regulatory modules given a set of transcription factor binding sites and, additionally, also RNA polymerase sites for a group of genes.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125171754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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