The Experts below are selected from a list of 690243 Experts worldwide ranked by ideXlab platform
Giovanni Felici - One of the best experts on this subject based on the ideXlab platform.
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Supervised DNA Barcodes species Classification: Analysis, comparisons and results
BioData Mining, 2014Co-Authors: Emanuel Weitschek, Giulia Fiscon, Giovanni FeliciAbstract:Background Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Species Classification with DNA Barcode sequences has been proven effective on different organisms. Indeed, specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The Classification problem assigns an unknown specimen to a known species by analyzing its Barcode. This task has to be supported with reliable methods and algorithms. Methods In this work the efficacy of supervised machine learning methods to classify species with DNA Barcode sequences is shown. The Weka software suite, which includes a collection of supervised Classification methods, is adopted to address the task of DNA Barcode Analysis. Classifier families are tested on synthetic and empirical datasets belonging to the animal, fungus, and plant kingdoms. In particular, the function-based method Support Vector Machines (SVM), the rule-based RIPPER, the decision tree C4.5, and the Naïve Bayes method are considered. Additionally, the Classification results are compared with respect to ad-hoc and well-established DNA Barcode Classification methods. Results A software that converts the DNA Barcode FASTA sequences to the Weka format is released, to adapt different input formats and to allow the execution of the Classification procedure. The Analysis of results on synthetic and real datasets shows that SVM and Naïve Bayes outperform on average the other considered classifiers, although they do not provide a human interpretable Classification model. Rule-based methods have slightly inferior Classification performances, but deliver the species specific positions and nucleotide assignments. On synthetic data the supervised machine learning methods obtain superior Classification performances with respect to the traditional DNA Barcode Classification methods. On empirical data their Classification performances are at a comparable level to the other methods. Conclusions The Classification Analysis shows that supervised machine learning methods are promising candidates for handling with success the DNA Barcoding species Classification problem, obtaining excellent performances. To conclude, a powerful tool to perform species identification is now available to the DNA Barcoding community.
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Supervised DNA barcodes species Classification: Analysis, comparisons and results.
BioData mining, 2014Co-Authors: Emanuel Weitschek, Giulia Fiscon, Giovanni FeliciAbstract:Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Species Classification with DNA Barcode sequences has been proven effective on different organisms. Indeed, specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The Classification problem assigns an unknown specimen to a known species by analyzing its Barcode. This task has to be supported with reliable methods and algorithms. In this work the efficacy of supervised machine learning methods to classify species with DNA Barcode sequences is shown. The Weka software suite, which includes a collection of supervised Classification methods, is adopted to address the task of DNA Barcode Analysis. Classifier families are tested on synthetic and empirical datasets belonging to the animal, fungus, and plant kingdoms. In particular, the function-based method Support Vector Machines (SVM), the rule-based RIPPER, the decision tree C4.5, and the Naive Bayes method are considered. Additionally, the Classification results are compared with respect to ad-hoc and well-established DNA Barcode Classification methods. A software that converts the DNA Barcode FASTA sequences to the Weka format is released, to adapt different input formats and to allow the execution of the Classification procedure. The Analysis of results on synthetic and real datasets shows that SVM and Naive Bayes outperform on average the other considered classifiers, although they do not provide a human interpretable Classification model. Rule-based methods have slightly inferior Classification performances, but deliver the species specific positions and nucleotide assignments. On synthetic data the supervised machine learning methods obtain superior Classification performances with respect to the traditional DNA Barcode Classification methods. On empirical data their Classification performances are at a comparable level to the other methods. The Classification Analysis shows that supervised machine learning methods are promising candidates for handling with success the DNA Barcoding species Classification problem, obtaining excellent performances. To conclude, a powerful tool to perform species identification is now available to the DNA Barcoding community.
Emanuel Weitschek - One of the best experts on this subject based on the ideXlab platform.
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Supervised DNA Barcodes species Classification: Analysis, comparisons and results
BioData Mining, 2014Co-Authors: Emanuel Weitschek, Giulia Fiscon, Giovanni FeliciAbstract:Background Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Species Classification with DNA Barcode sequences has been proven effective on different organisms. Indeed, specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The Classification problem assigns an unknown specimen to a known species by analyzing its Barcode. This task has to be supported with reliable methods and algorithms. Methods In this work the efficacy of supervised machine learning methods to classify species with DNA Barcode sequences is shown. The Weka software suite, which includes a collection of supervised Classification methods, is adopted to address the task of DNA Barcode Analysis. Classifier families are tested on synthetic and empirical datasets belonging to the animal, fungus, and plant kingdoms. In particular, the function-based method Support Vector Machines (SVM), the rule-based RIPPER, the decision tree C4.5, and the Naïve Bayes method are considered. Additionally, the Classification results are compared with respect to ad-hoc and well-established DNA Barcode Classification methods. Results A software that converts the DNA Barcode FASTA sequences to the Weka format is released, to adapt different input formats and to allow the execution of the Classification procedure. The Analysis of results on synthetic and real datasets shows that SVM and Naïve Bayes outperform on average the other considered classifiers, although they do not provide a human interpretable Classification model. Rule-based methods have slightly inferior Classification performances, but deliver the species specific positions and nucleotide assignments. On synthetic data the supervised machine learning methods obtain superior Classification performances with respect to the traditional DNA Barcode Classification methods. On empirical data their Classification performances are at a comparable level to the other methods. Conclusions The Classification Analysis shows that supervised machine learning methods are promising candidates for handling with success the DNA Barcoding species Classification problem, obtaining excellent performances. To conclude, a powerful tool to perform species identification is now available to the DNA Barcoding community.
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Supervised DNA barcodes species Classification: Analysis, comparisons and results.
BioData mining, 2014Co-Authors: Emanuel Weitschek, Giulia Fiscon, Giovanni FeliciAbstract:Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Species Classification with DNA Barcode sequences has been proven effective on different organisms. Indeed, specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The Classification problem assigns an unknown specimen to a known species by analyzing its Barcode. This task has to be supported with reliable methods and algorithms. In this work the efficacy of supervised machine learning methods to classify species with DNA Barcode sequences is shown. The Weka software suite, which includes a collection of supervised Classification methods, is adopted to address the task of DNA Barcode Analysis. Classifier families are tested on synthetic and empirical datasets belonging to the animal, fungus, and plant kingdoms. In particular, the function-based method Support Vector Machines (SVM), the rule-based RIPPER, the decision tree C4.5, and the Naive Bayes method are considered. Additionally, the Classification results are compared with respect to ad-hoc and well-established DNA Barcode Classification methods. A software that converts the DNA Barcode FASTA sequences to the Weka format is released, to adapt different input formats and to allow the execution of the Classification procedure. The Analysis of results on synthetic and real datasets shows that SVM and Naive Bayes outperform on average the other considered classifiers, although they do not provide a human interpretable Classification model. Rule-based methods have slightly inferior Classification performances, but deliver the species specific positions and nucleotide assignments. On synthetic data the supervised machine learning methods obtain superior Classification performances with respect to the traditional DNA Barcode Classification methods. On empirical data their Classification performances are at a comparable level to the other methods. The Classification Analysis shows that supervised machine learning methods are promising candidates for handling with success the DNA Barcoding species Classification problem, obtaining excellent performances. To conclude, a powerful tool to perform species identification is now available to the DNA Barcoding community.
Giulia Fiscon - One of the best experts on this subject based on the ideXlab platform.
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Supervised DNA Barcodes species Classification: Analysis, comparisons and results
BioData Mining, 2014Co-Authors: Emanuel Weitschek, Giulia Fiscon, Giovanni FeliciAbstract:Background Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Species Classification with DNA Barcode sequences has been proven effective on different organisms. Indeed, specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The Classification problem assigns an unknown specimen to a known species by analyzing its Barcode. This task has to be supported with reliable methods and algorithms. Methods In this work the efficacy of supervised machine learning methods to classify species with DNA Barcode sequences is shown. The Weka software suite, which includes a collection of supervised Classification methods, is adopted to address the task of DNA Barcode Analysis. Classifier families are tested on synthetic and empirical datasets belonging to the animal, fungus, and plant kingdoms. In particular, the function-based method Support Vector Machines (SVM), the rule-based RIPPER, the decision tree C4.5, and the Naïve Bayes method are considered. Additionally, the Classification results are compared with respect to ad-hoc and well-established DNA Barcode Classification methods. Results A software that converts the DNA Barcode FASTA sequences to the Weka format is released, to adapt different input formats and to allow the execution of the Classification procedure. The Analysis of results on synthetic and real datasets shows that SVM and Naïve Bayes outperform on average the other considered classifiers, although they do not provide a human interpretable Classification model. Rule-based methods have slightly inferior Classification performances, but deliver the species specific positions and nucleotide assignments. On synthetic data the supervised machine learning methods obtain superior Classification performances with respect to the traditional DNA Barcode Classification methods. On empirical data their Classification performances are at a comparable level to the other methods. Conclusions The Classification Analysis shows that supervised machine learning methods are promising candidates for handling with success the DNA Barcoding species Classification problem, obtaining excellent performances. To conclude, a powerful tool to perform species identification is now available to the DNA Barcoding community.
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Supervised DNA barcodes species Classification: Analysis, comparisons and results.
BioData mining, 2014Co-Authors: Emanuel Weitschek, Giulia Fiscon, Giovanni FeliciAbstract:Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Species Classification with DNA Barcode sequences has been proven effective on different organisms. Indeed, specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The Classification problem assigns an unknown specimen to a known species by analyzing its Barcode. This task has to be supported with reliable methods and algorithms. In this work the efficacy of supervised machine learning methods to classify species with DNA Barcode sequences is shown. The Weka software suite, which includes a collection of supervised Classification methods, is adopted to address the task of DNA Barcode Analysis. Classifier families are tested on synthetic and empirical datasets belonging to the animal, fungus, and plant kingdoms. In particular, the function-based method Support Vector Machines (SVM), the rule-based RIPPER, the decision tree C4.5, and the Naive Bayes method are considered. Additionally, the Classification results are compared with respect to ad-hoc and well-established DNA Barcode Classification methods. A software that converts the DNA Barcode FASTA sequences to the Weka format is released, to adapt different input formats and to allow the execution of the Classification procedure. The Analysis of results on synthetic and real datasets shows that SVM and Naive Bayes outperform on average the other considered classifiers, although they do not provide a human interpretable Classification model. Rule-based methods have slightly inferior Classification performances, but deliver the species specific positions and nucleotide assignments. On synthetic data the supervised machine learning methods obtain superior Classification performances with respect to the traditional DNA Barcode Classification methods. On empirical data their Classification performances are at a comparable level to the other methods. The Classification Analysis shows that supervised machine learning methods are promising candidates for handling with success the DNA Barcoding species Classification problem, obtaining excellent performances. To conclude, a powerful tool to perform species identification is now available to the DNA Barcoding community.
Hua Li - One of the best experts on this subject based on the ideXlab platform.
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quantitative and Classification Analysis of slag samples by laser induced breakdown spectroscopy libs coupled with support vector machine svm and partial least square pls methods
Journal of Analytical Atomic Spectrometry, 2015Co-Authors: Tianlong Zhang, Shan Wu, Juan Dong, Kang Wang, Hongsheng Tang, Xiaofeng Yang, Hua LiAbstract:The laser induced breakdown spectroscopy (LIBS) technique coupled with a support vector machine (SVM) and partial least square (PLS) methods was proposed to perform quantitative and Classification Analysis of 20 slag samples. The characteristic lines (Ca, Si, Al, Mg and Ti) of LIBS spectra for slag samples can be identified based on the NIST database. At first, quantitative Analysis of the major components (Fe2O3, CaO, SiO2, Al2O3, MgO and TiO2) in slag samples was completed by SVM with the full spectra as the input variable, and two parameters (kernel parameter of RBF-γ and σ2) of SVM were optimized by a grid search (GS) approach based on 5-fold cross-validation (CV). The performance of the SVM calibration model was investigated by 5-fold CV, and the prediction accuracy and root mean square error (RMSE) of SVM and PLS were employed to validate the predictive ability of the multivariate SVM calibration model in slag. The SVM model can eliminate the influence of nonlinear factors due to self-absorption in the plasma and provide a better predictive result. And then, two type of slag samples (open-hearth furnace slag and high titanium slag) were identified and classified by a partial least squares-discrimination Analysis (PLS-DA) method with different input variables. Sensitivity, specificity and accuracy were calculated to evaluate the Classification performance of the PLS-DA model for slag samples. It has been confirmed that the LIBS technique coupled with SVM and PLS methods is a promising approach to achieve the online Analysis and process control of slag and even in the metallurgy field.
Tianlong Zhang - One of the best experts on this subject based on the ideXlab platform.
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quantitative and Classification Analysis of slag samples by laser induced breakdown spectroscopy libs coupled with support vector machine svm and partial least square pls methods
Journal of Analytical Atomic Spectrometry, 2015Co-Authors: Tianlong Zhang, Shan Wu, Juan Dong, Kang Wang, Hongsheng Tang, Xiaofeng Yang, Hua LiAbstract:The laser induced breakdown spectroscopy (LIBS) technique coupled with a support vector machine (SVM) and partial least square (PLS) methods was proposed to perform quantitative and Classification Analysis of 20 slag samples. The characteristic lines (Ca, Si, Al, Mg and Ti) of LIBS spectra for slag samples can be identified based on the NIST database. At first, quantitative Analysis of the major components (Fe2O3, CaO, SiO2, Al2O3, MgO and TiO2) in slag samples was completed by SVM with the full spectra as the input variable, and two parameters (kernel parameter of RBF-γ and σ2) of SVM were optimized by a grid search (GS) approach based on 5-fold cross-validation (CV). The performance of the SVM calibration model was investigated by 5-fold CV, and the prediction accuracy and root mean square error (RMSE) of SVM and PLS were employed to validate the predictive ability of the multivariate SVM calibration model in slag. The SVM model can eliminate the influence of nonlinear factors due to self-absorption in the plasma and provide a better predictive result. And then, two type of slag samples (open-hearth furnace slag and high titanium slag) were identified and classified by a partial least squares-discrimination Analysis (PLS-DA) method with different input variables. Sensitivity, specificity and accuracy were calculated to evaluate the Classification performance of the PLS-DA model for slag samples. It has been confirmed that the LIBS technique coupled with SVM and PLS methods is a promising approach to achieve the online Analysis and process control of slag and even in the metallurgy field.