The Experts below are selected from a list of 3744 Experts worldwide ranked by ideXlab platform
Luis Rueda - One of the best experts on this subject based on the ideXlab platform.
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The predictive performance of short-linear motif features in the prediction of Calmodulin-Binding Proteins
BMC Bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:Background The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins. Results We propose a new method for the prediction of CaM-binding Proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding Proteins and 193 mitochondrial Proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k -nearest neighbors ( k -NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF). Conclusions Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding Proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding Proteins.
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the predictive performance of short linear motif features in the prediction of calmodulin binding Proteins
BMC Bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:Background The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins.
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The predictive performance of short-linear motif features in the prediction of Calmodulin-Binding Proteins.
BMC bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins. We propose a new method for the prediction of CaM-binding Proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding Proteins and 193 mitochondrial Proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF). Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding Proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding Proteins.
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IWBBIO (2) - Prediction of Calmodulin-Binding Proteins Using Short-Linear Motifs
Bioinformatics and Biomedical Engineering, 2017Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Luis Rueda, Alioune NgomAbstract:Prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because Calmodulin binds and regulates a multitude of protein targets affecting different cellular processes. Short linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been used in the prediction of CaM-binding Proteins. In this study, we propose a new method for prediction of CaM-binding Proteins based on both the total and average scores of SLiMs in protein sequences using a new scoring method, which we call Sliding Window Scoring (SWS) as features for the prediction. A dataset of 194 manually curated human CaM-binding Proteins and 193 Mitochondrial Proteins have been obtained and used for testing the proposed model. Multiple EM for Motif Elucidation (MEME) has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with Random Forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbor (k-NN), support vector machine (SVM), and Random Forest (RF), on a 3-fold cross-validation setup. Our proposed method shows promising prediction results and demonstrates how information contained in SLiMs is highly relevant for prediction of CaM-binding Proteins.
Yixun Li - One of the best experts on this subject based on the ideXlab platform.
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The predictive performance of short-linear motif features in the prediction of Calmodulin-Binding Proteins
BMC Bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:Background The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins. Results We propose a new method for the prediction of CaM-binding Proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding Proteins and 193 mitochondrial Proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k -nearest neighbors ( k -NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF). Conclusions Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding Proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding Proteins.
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the predictive performance of short linear motif features in the prediction of calmodulin binding Proteins
BMC Bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:Background The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins.
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The predictive performance of short-linear motif features in the prediction of Calmodulin-Binding Proteins.
BMC bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins. We propose a new method for the prediction of CaM-binding Proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding Proteins and 193 mitochondrial Proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF). Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding Proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding Proteins.
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IWBBIO (2) - Prediction of Calmodulin-Binding Proteins Using Short-Linear Motifs
Bioinformatics and Biomedical Engineering, 2017Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Luis Rueda, Alioune NgomAbstract:Prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because Calmodulin binds and regulates a multitude of protein targets affecting different cellular processes. Short linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been used in the prediction of CaM-binding Proteins. In this study, we propose a new method for prediction of CaM-binding Proteins based on both the total and average scores of SLiMs in protein sequences using a new scoring method, which we call Sliding Window Scoring (SWS) as features for the prediction. A dataset of 194 manually curated human CaM-binding Proteins and 193 Mitochondrial Proteins have been obtained and used for testing the proposed model. Multiple EM for Motif Elucidation (MEME) has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with Random Forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbor (k-NN), support vector machine (SVM), and Random Forest (RF), on a 3-fold cross-validation setup. Our proposed method shows promising prediction results and demonstrates how information contained in SLiMs is highly relevant for prediction of CaM-binding Proteins.
Alioune Ngom - One of the best experts on this subject based on the ideXlab platform.
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The predictive performance of short-linear motif features in the prediction of Calmodulin-Binding Proteins
BMC Bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:Background The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins. Results We propose a new method for the prediction of CaM-binding Proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding Proteins and 193 mitochondrial Proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k -nearest neighbors ( k -NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF). Conclusions Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding Proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding Proteins.
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the predictive performance of short linear motif features in the prediction of calmodulin binding Proteins
BMC Bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:Background The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins.
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The predictive performance of short-linear motif features in the prediction of Calmodulin-Binding Proteins.
BMC bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins. We propose a new method for the prediction of CaM-binding Proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding Proteins and 193 mitochondrial Proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF). Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding Proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding Proteins.
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IWBBIO (2) - Prediction of Calmodulin-Binding Proteins Using Short-Linear Motifs
Bioinformatics and Biomedical Engineering, 2017Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Luis Rueda, Alioune NgomAbstract:Prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because Calmodulin binds and regulates a multitude of protein targets affecting different cellular processes. Short linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been used in the prediction of CaM-binding Proteins. In this study, we propose a new method for prediction of CaM-binding Proteins based on both the total and average scores of SLiMs in protein sequences using a new scoring method, which we call Sliding Window Scoring (SWS) as features for the prediction. A dataset of 194 manually curated human CaM-binding Proteins and 193 Mitochondrial Proteins have been obtained and used for testing the proposed model. Multiple EM for Motif Elucidation (MEME) has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with Random Forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbor (k-NN), support vector machine (SVM), and Random Forest (RF), on a 3-fold cross-validation setup. Our proposed method shows promising prediction results and demonstrates how information contained in SLiMs is highly relevant for prediction of CaM-binding Proteins.
Mina Maleki - One of the best experts on this subject based on the ideXlab platform.
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The predictive performance of short-linear motif features in the prediction of Calmodulin-Binding Proteins
BMC Bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:Background The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins. Results We propose a new method for the prediction of CaM-binding Proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding Proteins and 193 mitochondrial Proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k -nearest neighbors ( k -NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF). Conclusions Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding Proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding Proteins.
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the predictive performance of short linear motif features in the prediction of calmodulin binding Proteins
BMC Bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:Background The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins.
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The predictive performance of short-linear motif features in the prediction of Calmodulin-Binding Proteins.
BMC bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins. We propose a new method for the prediction of CaM-binding Proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding Proteins and 193 mitochondrial Proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF). Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding Proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding Proteins.
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IWBBIO (2) - Prediction of Calmodulin-Binding Proteins Using Short-Linear Motifs
Bioinformatics and Biomedical Engineering, 2017Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Luis Rueda, Alioune NgomAbstract:Prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because Calmodulin binds and regulates a multitude of protein targets affecting different cellular processes. Short linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been used in the prediction of CaM-binding Proteins. In this study, we propose a new method for prediction of CaM-binding Proteins based on both the total and average scores of SLiMs in protein sequences using a new scoring method, which we call Sliding Window Scoring (SWS) as features for the prediction. A dataset of 194 manually curated human CaM-binding Proteins and 193 Mitochondrial Proteins have been obtained and used for testing the proposed model. Multiple EM for Motif Elucidation (MEME) has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with Random Forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbor (k-NN), support vector machine (SVM), and Random Forest (RF), on a 3-fold cross-validation setup. Our proposed method shows promising prediction results and demonstrates how information contained in SLiMs is highly relevant for prediction of CaM-binding Proteins.
Nicholas J. Carruthers - One of the best experts on this subject based on the ideXlab platform.
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The predictive performance of short-linear motif features in the prediction of Calmodulin-Binding Proteins
BMC Bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:Background The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins. Results We propose a new method for the prediction of CaM-binding Proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding Proteins and 193 mitochondrial Proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k -nearest neighbors ( k -NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF). Conclusions Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding Proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding Proteins.
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the predictive performance of short linear motif features in the prediction of calmodulin binding Proteins
BMC Bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:Background The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins.
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The predictive performance of short-linear motif features in the prediction of Calmodulin-Binding Proteins.
BMC bioinformatics, 2018Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Alioune Ngom, Luis RuedaAbstract:The prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding Proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding Proteins. We propose a new method for the prediction of CaM-binding Proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding Proteins and 193 mitochondrial Proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF). Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding Proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding Proteins.
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IWBBIO (2) - Prediction of Calmodulin-Binding Proteins Using Short-Linear Motifs
Bioinformatics and Biomedical Engineering, 2017Co-Authors: Yixun Li, Mina Maleki, Nicholas J. Carruthers, Paul M. Stemmer, Luis Rueda, Alioune NgomAbstract:Prediction of Calmodulin-Binding (CaM-binding) Proteins plays a very important role in the fields of biology and biochemistry, because Calmodulin binds and regulates a multitude of protein targets affecting different cellular processes. Short linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been used in the prediction of CaM-binding Proteins. In this study, we propose a new method for prediction of CaM-binding Proteins based on both the total and average scores of SLiMs in protein sequences using a new scoring method, which we call Sliding Window Scoring (SWS) as features for the prediction. A dataset of 194 manually curated human CaM-binding Proteins and 193 Mitochondrial Proteins have been obtained and used for testing the proposed model. Multiple EM for Motif Elucidation (MEME) has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with Random Forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbor (k-NN), support vector machine (SVM), and Random Forest (RF), on a 3-fold cross-validation setup. Our proposed method shows promising prediction results and demonstrates how information contained in SLiMs is highly relevant for prediction of CaM-binding Proteins.
