The Experts below are selected from a list of 152076 Experts worldwide ranked by ideXlab platform
Daoqiang Zhang - One of the best experts on this subject based on the ideXlab platform.
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Supervised HyperAlignment for multi-subject fMRI Data Alignment
IEEE Transactions on Cognitive and Developmental Systems, 2020Co-Authors: Muhammad Yousefnezhad, Alessandro Selvitella, Liangxiu Han, Daoqiang ZhangAbstract:HyperAlignment has been widely employed in Multivariate Pattern (MVP) analysis to discover the cognitive states in the human brains based on multi-subject functional Magnetic Resonance Imaging (fMRI) Datasets. Most of the existing HA methods utilized unsupervised approaches, where they only maximized the correlation between the voxels with the same position in the time series. However, these unsupervised solutions may not be optimum for handling the functional Alignment in the supervised MVP problems. This paper proposes a Supervised HyperAlignment (SHA) method to ensure better functional Alignment for MVP analysis, where the proposed method provides a supervised shared space that can maximize the correlation among the stimuli belonging to the same category and minimize the correlation between distinct categories of stimuli. Further, SHA employs a generalized optimization solution, which generates the shared space and calculates the mapped features in a single iteration, hence with optimum time and space complexities for large Datasets. Experiments on multi-subject Datasets demonstrate that SHA method achieves up to 19% better performance for multi-class problems over the state-of-the-art HA algorithms.
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PRICAI (1) - Gradient HyperAlignment for Multi-subject fMRI Data Alignment
Lecture Notes in Computer Science, 2018Co-Authors: Muhammad Yousefnezhad, Daoqiang ZhangAbstract:Multi-subject fMRI Data analysis is an interesting and challenging problem in human brain decoding studies. The inherent anatomical and functional variability across subjects make it necessary to do both anatomical and functional Alignment before classification analysis. Besides, when it comes to big Data, time complexity becomes a problem that cannot be ignored. This paper proposes Gradient HyperAlignment (Gradient-HA) as a gradient-based functional Alignment method that is suitable for multi-subject fMRI Datasets with large amounts of samples and voxels. The advantage of Gradient-HA is that it can solve independence and high dimension problems by using Independent Component Analysis (ICA) and Stochastic Gradient Ascent (SGA). Validation using multi-classification tasks on big Data demonstrates that Gradient-HA method has less time complexity and better or comparable performance compared with other state-of-the-art functional Alignment methods.
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Local Discriminant HyperAlignment for multi-subject fMRI Data Alignment
2016Co-Authors: Muhammad Yousefnezhad, Daoqiang ZhangAbstract:Multivariate Pattern (MVP) classification can map different cognitive states to the brain tasks. One of the main challenges in MVP analysis is validating the generated results across subjects. However, analyzing multi-subject fMRI Data requires accurate functional Alignments between neuronal activities of different subjects, which can rapidly increase the performance and robustness of the final results. HyperAlignment (HA) is one of the most effective functional Alignment methods, which can be mathematically formulated by the Canonical Correlation Analysis (CCA) methods. Since HA mostly uses the unsupervised CCA techniques, its solution may not be optimized for MVP analysis. By incorporating the idea of Local Discriminant Analysis (LDA) into CCA, this paper proposes Local Discriminant HyperAlignment (LDHA) as a novel supervised HA method, which can provide better functional Alignment for MVP analysis. Indeed, the locality is defined based on the stimuli categories in the train-set, where the correlation between all stimuli in the same category will be maximized and the correlation between distinct categories of stimuli approaches to near zero. Experimental studies on multi-subject MVP analysis confirm that the LDHA method achieves superior performance to other state-of-the-art HA algorithms.
Melissa Hanna-brown - One of the best experts on this subject based on the ideXlab platform.
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Micellar electrokinetic capillary chromatography and Data Alignment analysis: a new tool in urine profiling.
Journal of chromatography. A, 2004Co-Authors: Christelle Guillo, David Barlow, David Perrett, Melissa Hanna-brownAbstract:The complex nature of biofluids demands efficient, sensitive and high-resolution analytical methodologies to examine how the 'metabolic fingerprint' changes during disease. This paper describes how sulphated beta-cyclodextrin-modified micellar electrokinetic capillary chromatography (SbetaCD-MECC) has been combined with Data Alignment analysis and may prove a useful new tool in urine profiling, allowing for separation of over 80 urinary analytes in under 25 min. The optimised and validated SbetaCD-MECC methodology combined with Data Alignment analysis provides rapid identification of 'mismatches' between urine profiles which are not easily detected with the naked eye as well as a 'similarity score' which indicates the total sum of differences between one profile and another. The combination of SbetaCD-MECC with Data Alignment software should prove a useful alternative tool in metabonomic studies for rapid comparison of urine profiles.
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Micellar electrokinetic capillary chromatography and Data Alignment analysis: a new tool in urine profiling.
Journal of Chromatography A, 2003Co-Authors: Christelle Guillo, David Perrett, David J. Barlow, Melissa Hanna-brownAbstract:Abstract The complex nature of biofluids demands efficient, sensitive and high-resolution analytical methodologies to examine how the ‘metabolic fingerprint’ changes during disease. This paper describes how sulphated β-cyclodextrin-modified micellar electrokinetic capillary chromatography (SβCD-MECC) has been combined with Data Alignment analysis and may prove a useful new tool in urine profiling, allowing for separation of over 80 urinary analytes in under 25 min. The optimised and validated SβCD-MECC methodology combined with Data Alignment analysis provides rapid identification of ‘mismatches’ between urine profiles which are not easily detected with the naked eye as well as a ‘similarity score’ which indicates the total sum of differences between one profile and another. The combination of SβCD-MECC with Data Alignment software should prove a useful alternative tool in metabonomic studies for rapid comparison of urine profiles.
Muhammad Yousefnezhad - One of the best experts on this subject based on the ideXlab platform.
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Supervised HyperAlignment for multi-subject fMRI Data Alignment
IEEE Transactions on Cognitive and Developmental Systems, 2020Co-Authors: Muhammad Yousefnezhad, Alessandro Selvitella, Liangxiu Han, Daoqiang ZhangAbstract:HyperAlignment has been widely employed in Multivariate Pattern (MVP) analysis to discover the cognitive states in the human brains based on multi-subject functional Magnetic Resonance Imaging (fMRI) Datasets. Most of the existing HA methods utilized unsupervised approaches, where they only maximized the correlation between the voxels with the same position in the time series. However, these unsupervised solutions may not be optimum for handling the functional Alignment in the supervised MVP problems. This paper proposes a Supervised HyperAlignment (SHA) method to ensure better functional Alignment for MVP analysis, where the proposed method provides a supervised shared space that can maximize the correlation among the stimuli belonging to the same category and minimize the correlation between distinct categories of stimuli. Further, SHA employs a generalized optimization solution, which generates the shared space and calculates the mapped features in a single iteration, hence with optimum time and space complexities for large Datasets. Experiments on multi-subject Datasets demonstrate that SHA method achieves up to 19% better performance for multi-class problems over the state-of-the-art HA algorithms.
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PRICAI (1) - Gradient HyperAlignment for Multi-subject fMRI Data Alignment
Lecture Notes in Computer Science, 2018Co-Authors: Muhammad Yousefnezhad, Daoqiang ZhangAbstract:Multi-subject fMRI Data analysis is an interesting and challenging problem in human brain decoding studies. The inherent anatomical and functional variability across subjects make it necessary to do both anatomical and functional Alignment before classification analysis. Besides, when it comes to big Data, time complexity becomes a problem that cannot be ignored. This paper proposes Gradient HyperAlignment (Gradient-HA) as a gradient-based functional Alignment method that is suitable for multi-subject fMRI Datasets with large amounts of samples and voxels. The advantage of Gradient-HA is that it can solve independence and high dimension problems by using Independent Component Analysis (ICA) and Stochastic Gradient Ascent (SGA). Validation using multi-classification tasks on big Data demonstrates that Gradient-HA method has less time complexity and better or comparable performance compared with other state-of-the-art functional Alignment methods.
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Local Discriminant HyperAlignment for multi-subject fMRI Data Alignment
2016Co-Authors: Muhammad Yousefnezhad, Daoqiang ZhangAbstract:Multivariate Pattern (MVP) classification can map different cognitive states to the brain tasks. One of the main challenges in MVP analysis is validating the generated results across subjects. However, analyzing multi-subject fMRI Data requires accurate functional Alignments between neuronal activities of different subjects, which can rapidly increase the performance and robustness of the final results. HyperAlignment (HA) is one of the most effective functional Alignment methods, which can be mathematically formulated by the Canonical Correlation Analysis (CCA) methods. Since HA mostly uses the unsupervised CCA techniques, its solution may not be optimized for MVP analysis. By incorporating the idea of Local Discriminant Analysis (LDA) into CCA, this paper proposes Local Discriminant HyperAlignment (LDHA) as a novel supervised HA method, which can provide better functional Alignment for MVP analysis. Indeed, the locality is defined based on the stimuli categories in the train-set, where the correlation between all stimuli in the same category will be maximized and the correlation between distinct categories of stimuli approaches to near zero. Experimental studies on multi-subject MVP analysis confirm that the LDHA method achieves superior performance to other state-of-the-art HA algorithms.
Christelle Guillo - One of the best experts on this subject based on the ideXlab platform.
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Micellar electrokinetic capillary chromatography and Data Alignment analysis: a new tool in urine profiling.
Journal of chromatography. A, 2004Co-Authors: Christelle Guillo, David Barlow, David Perrett, Melissa Hanna-brownAbstract:The complex nature of biofluids demands efficient, sensitive and high-resolution analytical methodologies to examine how the 'metabolic fingerprint' changes during disease. This paper describes how sulphated beta-cyclodextrin-modified micellar electrokinetic capillary chromatography (SbetaCD-MECC) has been combined with Data Alignment analysis and may prove a useful new tool in urine profiling, allowing for separation of over 80 urinary analytes in under 25 min. The optimised and validated SbetaCD-MECC methodology combined with Data Alignment analysis provides rapid identification of 'mismatches' between urine profiles which are not easily detected with the naked eye as well as a 'similarity score' which indicates the total sum of differences between one profile and another. The combination of SbetaCD-MECC with Data Alignment software should prove a useful alternative tool in metabonomic studies for rapid comparison of urine profiles.
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Micellar electrokinetic capillary chromatography and Data Alignment analysis: a new tool in urine profiling.
Journal of Chromatography A, 2003Co-Authors: Christelle Guillo, David Perrett, David J. Barlow, Melissa Hanna-brownAbstract:Abstract The complex nature of biofluids demands efficient, sensitive and high-resolution analytical methodologies to examine how the ‘metabolic fingerprint’ changes during disease. This paper describes how sulphated β-cyclodextrin-modified micellar electrokinetic capillary chromatography (SβCD-MECC) has been combined with Data Alignment analysis and may prove a useful new tool in urine profiling, allowing for separation of over 80 urinary analytes in under 25 min. The optimised and validated SβCD-MECC methodology combined with Data Alignment analysis provides rapid identification of ‘mismatches’ between urine profiles which are not easily detected with the naked eye as well as a ‘similarity score’ which indicates the total sum of differences between one profile and another. The combination of SβCD-MECC with Data Alignment software should prove a useful alternative tool in metabonomic studies for rapid comparison of urine profiles.
Jun Tanida - One of the best experts on this subject based on the ideXlab platform.
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STRING Data Alignment BY A SPATIAL CODING AND MOIRE TECHNIQUE
Optics letters, 1999Co-Authors: Jun TanidaAbstract:A method for string Alignment is presented in which a moire technique is applied to one-dimensional spatial encoding patterns. String Alignment, an essential operation in genome analysis, evaluates local similarity between sequences of bases or amino acids. The method uses a simple procedure to provide matching results for not only the same locus but also neighboring loci. Experimental verification shows the effectiveness of the proposed method.
