The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform
Aryeh Finkelberg - One of the best experts on this subject based on the ideXlab platform.
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Heraclitus and Thales' Conceptual Scheme: A Historical Study
2017Co-Authors: Aryeh FinkelbergAbstract:In Heraclitus and Thales’ Conceptual Scheme: A Historical Study Aryeh Finkelberg rejects the teleological interpretation of early Greek thought as targeted at later results, viz. philosophy, and seeks to determine its intended meaning by restoring it to its historical context.
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heraclitus and thales Conceptual Scheme a historical study
2017Co-Authors: Aryeh FinkelbergAbstract:Aryeh Finkelberg offers an alternative to the traditional teleological interpretation of early Greek thought. Instead of explaining it as targeted at later results, viz. philosophy, as this thought was first Conceptualized by Aristotle and has been regarded ever since, the author seeks to determine its intended meaning by restoring it to its historical context as evinced, inter alia, by epigraphic and papyrological evidence, in particular, the Gold Leaves, the Olbian bone plates, and the Derveni papyrus. This approach, together with a considerable amount of hitherto unidentified or largely disregarded evidence, yields a picture of early Greek thought significantly different from the traditional history of 'Presocratic philosophy'.
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Heraclitus and Thales’ Conceptual Scheme: A Historical Study - The Fugitive δαίμονες
Heraclitus and Thales’ Conceptual Scheme: A Historical Study, 2017Co-Authors: Aryeh FinkelbergAbstract:In Heraclitus and Thales’ Conceptual Scheme: A Historical Study Aryeh Finkelberg rejects the teleological interpretation of early Greek thought as targeted at later results, viz. philosophy, and seeks to determine its intended meaning by restoring it to its historical context.
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Heraclitus and Thales’ Conceptual Scheme: A Historical Study - Questions of Method
Heraclitus and Thales’ Conceptual Scheme: A Historical Study, 2017Co-Authors: Aryeh FinkelbergAbstract:In Heraclitus and Thales’ Conceptual Scheme: A Historical Study Aryeh Finkelberg rejects the teleological interpretation of early Greek thought as targeted at later results, viz. philosophy, and seeks to determine its intended meaning by restoring it to its historical context.
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Heraclitus and Thales’ Conceptual Scheme: A Historical Study - The Solstices of Fire
Heraclitus and Thales’ Conceptual Scheme: A Historical Study, 2017Co-Authors: Aryeh FinkelbergAbstract:In Heraclitus and Thales’ Conceptual Scheme: A Historical Study Aryeh Finkelberg rejects the teleological interpretation of early Greek thought as targeted at later results, viz. philosophy, and seeks to determine its intended meaning by restoring it to its historical context.
Maja Jagodic - One of the best experts on this subject based on the ideXlab platform.
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stategra multi omics data integration a Conceptual Scheme with a bioinformatics pipeline
Frontiers in Genetics, 2021Co-Authors: Nuria Planell, Vincenzo Lagani, Frans M. Van Der Kloet, Ewoud Ewing, Nestoras Karathanasis, Arantxa Urdangarin, Imanol Arozarena, Patricia Sebastianleon, Maja JagodicAbstract:Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to Conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a Conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlysis tools (machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.
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STATegra: Multi-omics data integration - A Conceptual Scheme with a bioinformatics pipeline
2020Co-Authors: Nuria Planell, Vincenzo Lagani, Patricia Sebastián-león, Frans M. Van Der Kloet, Ewoud Ewing, Nestoras Karathanasis, Arantxa Urdangarin, Imanol Arozarena, Maja Jagodic, Ioannis TsamardinosAbstract:Abstract Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. It is therefore an unmet need to Conceptualize how to integrate such data and to implement and validate pipelines in different cases. We have designed a Conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis in a step-wise manner. While in several studies we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity to identify features in comparison to single-omics analysis. Such an integrative multi-omics analysis framework for the identification of features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.
Nestoras Karathanasis - One of the best experts on this subject based on the ideXlab platform.
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stategra multi omics data integration a Conceptual Scheme with a bioinformatics pipeline
Frontiers in Genetics, 2021Co-Authors: Nuria Planell, Vincenzo Lagani, Frans M. Van Der Kloet, Ewoud Ewing, Nestoras Karathanasis, Arantxa Urdangarin, Imanol Arozarena, Patricia Sebastianleon, Maja JagodicAbstract:Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to Conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a Conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlysis tools (machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.
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STATegra: Multi-omics data integration - A Conceptual Scheme with a bioinformatics pipeline
2020Co-Authors: Nuria Planell, Vincenzo Lagani, Patricia Sebastián-león, Frans M. Van Der Kloet, Ewoud Ewing, Nestoras Karathanasis, Arantxa Urdangarin, Imanol Arozarena, Maja Jagodic, Ioannis TsamardinosAbstract:Abstract Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. It is therefore an unmet need to Conceptualize how to integrate such data and to implement and validate pipelines in different cases. We have designed a Conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis in a step-wise manner. While in several studies we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity to identify features in comparison to single-omics analysis. Such an integrative multi-omics analysis framework for the identification of features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.
Nuria Planell - One of the best experts on this subject based on the ideXlab platform.
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stategra multi omics data integration a Conceptual Scheme with a bioinformatics pipeline
Frontiers in Genetics, 2021Co-Authors: Nuria Planell, Vincenzo Lagani, Frans M. Van Der Kloet, Ewoud Ewing, Nestoras Karathanasis, Arantxa Urdangarin, Imanol Arozarena, Patricia Sebastianleon, Maja JagodicAbstract:Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to Conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a Conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlysis tools (machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.
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STATegra: Multi-omics data integration - A Conceptual Scheme with a bioinformatics pipeline
2020Co-Authors: Nuria Planell, Vincenzo Lagani, Patricia Sebastián-león, Frans M. Van Der Kloet, Ewoud Ewing, Nestoras Karathanasis, Arantxa Urdangarin, Imanol Arozarena, Maja Jagodic, Ioannis TsamardinosAbstract:Abstract Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. It is therefore an unmet need to Conceptualize how to integrate such data and to implement and validate pipelines in different cases. We have designed a Conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis in a step-wise manner. While in several studies we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity to identify features in comparison to single-omics analysis. Such an integrative multi-omics analysis framework for the identification of features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.
Vincenzo Lagani - One of the best experts on this subject based on the ideXlab platform.
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stategra multi omics data integration a Conceptual Scheme with a bioinformatics pipeline
Frontiers in Genetics, 2021Co-Authors: Nuria Planell, Vincenzo Lagani, Frans M. Van Der Kloet, Ewoud Ewing, Nestoras Karathanasis, Arantxa Urdangarin, Imanol Arozarena, Patricia Sebastianleon, Maja JagodicAbstract:Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to Conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a Conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlysis tools (machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.
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STATegra: Multi-omics data integration - A Conceptual Scheme with a bioinformatics pipeline
2020Co-Authors: Nuria Planell, Vincenzo Lagani, Patricia Sebastián-león, Frans M. Van Der Kloet, Ewoud Ewing, Nestoras Karathanasis, Arantxa Urdangarin, Imanol Arozarena, Maja Jagodic, Ioannis TsamardinosAbstract:Abstract Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. It is therefore an unmet need to Conceptualize how to integrate such data and to implement and validate pipelines in different cases. We have designed a Conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis in a step-wise manner. While in several studies we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity to identify features in comparison to single-omics analysis. Such an integrative multi-omics analysis framework for the identification of features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.