The Experts below are selected from a list of 564297 Experts worldwide ranked by ideXlab platform
Hans Binder - One of the best experts on this subject based on the ideXlab platform.
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Mining SOM expression portraits: feature selection and integrating concepts of Molecular Function
BioData mining, 2012Co-Authors: Henry Wirth, Martin Von Bergen, Hans BinderAbstract:Background Self organizing maps (SOM) enable the straightforward portraying of high-dimensional data of large sample collections in terms of sample-specific images. The analysis of their texture provides so-called spot-clusters of co-expressed genes which require subsequent significance filtering and Functional interpretation. We address feature selection in terms of the gene ranking problem and the interpretation of the obtained spot-related lists using concepts of Molecular Function.
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Mining SOM expression portraits: Feature selection and integrating concepts of Molecular Function
Nature Precedings, 2011Co-Authors: Henry Wirth, Martin Von Bergen, Hans BinderAbstract:Background: Self organizing maps (SOM) enable the straightforward portraying of high-dimensional data of large sample collections in terms of sample-specific images. The analysis of their texture provides so-called spot-clusters of co-expressed genes which require subsequent significance filtering and Functional interpretation. We address feature selection in terms of the gene ranking problem and the interpretation of the obtained spot-related lists using concepts of Molecular Function. Results: Different expression scores based either on simple fold change-measures or on regularized Students t-statistics are applied to spot-related gene lists and compared with special emphasis on the error characteristics of microarray expression data. The spot-clusters are analyzed using different methods of gene set enrichment analysis with the focus on overexpression and/or overrepresentation of predefined sets of genes. Metagene-related overrepresentation of selected gene sets was mapped into the SOM images to assign gene Function to different regions. Alternatively we estimated set-related overexpression profiles over all samples studied using a gene set enrichment score. It was also applied to the spot-clusters to generate lists of enriched gene sets. We used the tissue body index data set, a collection of expression data of human tissues, as an illustrative example. We found that tissue related spots typically contain enriched populations of gene sets well corresponding to Molecular processes in the respective tissues. In addition, we display special sets of housekeeping and of consistently weak and highly expressed genes using SOM data filtering. Conclusions: The presented methods allow the comprehensive downstream analysis of SOM-transformed expression data in terms of cluster-related gene lists and enriched gene sets for Functional interpretation. SOM clustering implies the ability to define either new gene sets using selected SOM spots or to verify and/or to amend existing ones.
Henry Wirth - One of the best experts on this subject based on the ideXlab platform.
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Mining SOM expression portraits: feature selection and integrating concepts of Molecular Function
BioData mining, 2012Co-Authors: Henry Wirth, Martin Von Bergen, Hans BinderAbstract:Background Self organizing maps (SOM) enable the straightforward portraying of high-dimensional data of large sample collections in terms of sample-specific images. The analysis of their texture provides so-called spot-clusters of co-expressed genes which require subsequent significance filtering and Functional interpretation. We address feature selection in terms of the gene ranking problem and the interpretation of the obtained spot-related lists using concepts of Molecular Function.
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Mining SOM expression portraits: Feature selection and integrating concepts of Molecular Function
Nature Precedings, 2011Co-Authors: Henry Wirth, Martin Von Bergen, Hans BinderAbstract:Background: Self organizing maps (SOM) enable the straightforward portraying of high-dimensional data of large sample collections in terms of sample-specific images. The analysis of their texture provides so-called spot-clusters of co-expressed genes which require subsequent significance filtering and Functional interpretation. We address feature selection in terms of the gene ranking problem and the interpretation of the obtained spot-related lists using concepts of Molecular Function. Results: Different expression scores based either on simple fold change-measures or on regularized Students t-statistics are applied to spot-related gene lists and compared with special emphasis on the error characteristics of microarray expression data. The spot-clusters are analyzed using different methods of gene set enrichment analysis with the focus on overexpression and/or overrepresentation of predefined sets of genes. Metagene-related overrepresentation of selected gene sets was mapped into the SOM images to assign gene Function to different regions. Alternatively we estimated set-related overexpression profiles over all samples studied using a gene set enrichment score. It was also applied to the spot-clusters to generate lists of enriched gene sets. We used the tissue body index data set, a collection of expression data of human tissues, as an illustrative example. We found that tissue related spots typically contain enriched populations of gene sets well corresponding to Molecular processes in the respective tissues. In addition, we display special sets of housekeeping and of consistently weak and highly expressed genes using SOM data filtering. Conclusions: The presented methods allow the comprehensive downstream analysis of SOM-transformed expression data in terms of cluster-related gene lists and enriched gene sets for Functional interpretation. SOM clustering implies the ability to define either new gene sets using selected SOM spots or to verify and/or to amend existing ones.
Zen-ichi Yoshida Prof. - One of the best experts on this subject based on the ideXlab platform.
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Shape-persistency and Molecular Function in Heteromacrocycles: Creation of Heteroarenecyclynes and Arene–Azaarenecyclynes
Chemistry - A European Journal, 2004Co-Authors: Yoshihiro Yamaguchi Prof., Zen-ichi Yoshida Prof.Abstract:On the basis of our concept that the introduction of heteroatoms and shape-persistency into the π-macrocycles should bring forth striking Functions or properties, heteroarenecyclynes (such as oxaarenecyclynes and thiaarenecyclynes) with semi-shape-persistent structure, and arene–azaarenecyclynes with shape-persistent structure have been prepared. Their novel Functions and characteristic properties are disclosed. Noteworthy is that heteroarenecyclynes include C60 to provide a Saturn-type complex with a N2 binding Function. A simple member of the oxaarenecyclyne compounds undergoes the AgI-induced cyclization leading to the quantitative formation of strongly luminescent perylene derivative. Arene–azaarenecyclynes are versatile compounds. For example, they exhibit intense luminescence in spite of the meta-bonding structure, providing the circular luminophore. Also they serve as receptors for special metal, organic, and inorganic substrates. The observed Molecular Functions are valuable for scientific and practical application.
Angelo Azzi - One of the best experts on this subject based on the ideXlab platform.
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Molecular mechanism of α tocopherol action
Free Radical Biology and Medicine, 2007Co-Authors: Angelo AzziAbstract:The inability of other antioxidants to substitute for α-tocopherol in a number of cellular reactions, the lack of a compensatory antioxidant response in the gene expression under conditions of α-tocopherol deficiency, the unique uptake of α-tocopherol relative to the other tocopherols and its slower catabolism, and the striking differences in the Molecular Function of the different tocopherols and tocotrienols, observed in vitro, unrelated to their antioxidant properties, are all data in support of a nonantioxidant Molecular Function of α-tocopherol. Furthermore, in vivo studies have also shown that α-tocopherol is not able, at physiological concentrations, to protect against oxidant-induced damage or prevent disease allegedly caused by oxidative damage. α-Tocopherol appears to act as a ligand of not yet identified specific proteins (receptors, transcription factors) capable of regulating signal transduction and gene expression.
Steven E. Brenner - One of the best experts on this subject based on the ideXlab platform.
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Phylogenetic Molecular Function annotation
Journal of physics. Conference series, 2009Co-Authors: Barbara E. Engelhardt, Michael I. Jordan, Susanna Repo, Steven E. BrennerAbstract:It is now easier to discover thousands of protein sequences in a new microbial genome than it is to biochemically characterize the specific activity of a single protein of unknown Function. The Molecular Functions of protein sequences have typically been predicted using homology-based computational methods, which rely on the principle that homologous proteins share a similar Function. However, some protein families include groups of proteins with different Molecular Functions. A phylogenetic approach for predicting Molecular Function (sometimes called "phylogenomics") is an effective means to predict protein Molecular Function. These methods incorporate Functional evidence from all members of a family that have Functional characterizations using the evolutionary history of the protein family to make robust predictions for the uncharacterized proteins. However, they are often difficult to apply on a genome-wide scale because of the time-consuming step of reconstructing the phylogenies of each protein to be annotated. Our automated approach for Function annotation using phylogeny, the SIFTER (Statistical Inference of Function Through Evolutionary Relationships) methodology, uses a statistical graphical model to compute the probabilities of Molecular Functions for unannotated proteins. Our benchmark tests showed that SIFTER provides accurate Functional predictions on various protein families, outperforming other available methods.
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ICML - A graphical model for predicting protein Molecular Function
Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006Co-Authors: Barbara E. Engelhardt, Michael I. Jordan, Steven E. BrennerAbstract:We present a simple statistical model of Molecular Function evolution to predict protein Function. The model description encodes general knowledge of how Molecular Function evolves within a phylogenetic tree based on the proteins' sequence. Inputs are a phylogeny for a set of evolutionarily related protein sequences and any available Function characterizations for those proteins. Posterior probabilities for each protein are used to predict the Molecular Function of that protein. We present results from applying our model to three protein families, and compare our prediction results on the extant proteins to other available protein Function prediction methods. For the deaminase family, our method achieves 93.9% where related methods BLAST achieves 72.7%, GOtcha achieves 87.9%, and Orthostrapper achieves 72.7% in prediction accuracy.
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protein Molecular Function prediction by bayesian phylogenomics
PLOS Computational Biology, 2005Co-Authors: Barbara E. Engelhardt, Michael I. Jordan, Kathryn E. Muratore, Steven E. BrennerAbstract:We present a statistical graphical model to infer specific Molecular Function for unannotated protein sequences using homology. Based on phylogenomic principles, SIFTER (Statistical Inference of Function Through Evolutionary Relationships) accurately predicts Molecular Function for members of a protein family given a reconciled phylogeny and available Function annotations, even when the data are sparse or noisy. Our method produced specific and consistent Molecular Function predictions across 100 Pfam families in comparison to the Gene Ontology annotation database, BLAST, GOtcha, and Orthostrapper. We performed a more detailed exploration of Functional predictions on the adenosine-5'-monophosphate/adenosine deaminase family and the lactate/malate dehydrogenase family, in the former case comparing the predictions against a gold standard set of published Functional characterizations. Given Function annotations for 3% of the proteins in the deaminase family, SIFTER achieves 96% accuracy in predicting Molecular Function for experimentally characterized proteins as reported in the literature. The accuracy of SIFTER on this dataset is a significant improvement over other currently available methods such as BLAST (75%), GeneQuiz (64%), GOtcha (89%), and Orthostrapper (11%). We also experimentally characterized the adenosine deaminase from Plasmodium falciparum, confirming SIFTER's prediction. The results illustrate the predictive power of exploiting a statistical model of Function evolution in phylogenomic problems. A software implementation of SIFTER is available from the authors.
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Protein Molecular Function Prediction
2005Co-Authors: Bayesian Phylogenomics, Michael I. Jordan, Barbara E. Engelhardt, Kathryn E. Muratore, Steven E. BrennerAbstract:We present a statistical graphical model to infer specific Molecular Function for unannotated protein sequences using homology. Based on phylogenomic principles, SIFTER (Statistical Inference of Function Through Evolutionary Relationships) accurately predicts Molecular Function for members of a protein family given a reconciled phylogeny and available Function annotations, even when the data are sparse or noisy. Our method produced specific and consistent Molecular Function predictions across 100 Pfam families in comparison to the Gene Ontology annotation database, BLAST, GOtcha, and Orthostrapper. We performed a more detailed exploration of Functional predictions on the adenosine-59-monophosphate/adenosine deaminase family and the lactate/malate dehydrogenase family, in the former case comparing the predictions against a gold standard set of published Functional characterizations. Given Function annotations for 3% of the proteins in the deaminase family, SIFTER achieves 96% accuracy in predicting Molecular Function for experimentally characterized proteins as reported in the literature. The accuracy of SIFTER on this dataset is a significant improvement over other currently available methods such as BLAST (75%), GeneQuiz (64%), GOtcha (89%), and Orthostrapper (11%). We also experimentally characterized the adenosine deaminase from Plasmodium falciparum, confirming SIFTER’s prediction. The results illustrate the predictive power of exploiting a statistical model of Function evolution in phylogenomic problems. A software implementation of SIFTER is available from the authors.