ºÝºÝߣshows by User: dgrapov / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: dgrapov / Sat, 29 Apr 2023 02:39:40 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: dgrapov R programming for Data Science - A Beginner’s Guide https://fr.slideshare.net/slideshow/r-programming-for-data-science-a-beginners-guide-257617883/257617883 course2-230429023940-9c84f25e
https://creativedatasolutions.github.io/R_programming_for_DS_beginner/]]>
https://creativedatasolutions.github.io/R_programming_for_DS_beginner/]]> Sat, 29 Apr 2023 02:39:40 GMT https://fr.slideshare.net/slideshow/r-programming-for-data-science-a-beginners-guide-257617883/257617883 dgrapov@slideshare.net(dgrapov) R programming for Data Science - A Beginner’s Guide dgrapov https://creativedatasolutions.github.io/R_programming_for_DS_beginner/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/course2-230429023940-9c84f25e-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> https://creativedatasolutions.github.io/R_programming_for_DS_beginner/
from Dmitry Grapov
]]> 4 0 https://cdn.slidesharecdn.com/ss_thumbnails/course2-230429023940-9c84f25e-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Network mapping 101 course /slideshow/network-mapping-101-course/254845454 tutorial-221210030911-aa356370
Full course: https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/ The course covered all of the steps required to go from `raw data` to a rich `mapped biochemical network` incorporating statistical, multivariate and machine learning results. This included [examples](https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/#topics) and tutorials for: * Preparing raw data for analysis * Multivariate data exploration * Supervised clustering * Machine learning – classification model validation and feature selection * Network analysis - biochemical, structural similarity and correlation networks * Network mapping – putting it all together to create a publication quality network url: https://github.com/CreativeDataSolutions/CDS.courses/blob/gh-pages/courses/network_mapping_101/materials/lectures/tutorial.pdf]]>
Full course: https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/ The course covered all of the steps required to go from `raw data` to a rich `mapped biochemical network` incorporating statistical, multivariate and machine learning results. This included [examples](https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/#topics) and tutorials for: * Preparing raw data for analysis * Multivariate data exploration * Supervised clustering * Machine learning – classification model validation and feature selection * Network analysis - biochemical, structural similarity and correlation networks * Network mapping – putting it all together to create a publication quality network url: https://github.com/CreativeDataSolutions/CDS.courses/blob/gh-pages/courses/network_mapping_101/materials/lectures/tutorial.pdf]]> Sat, 10 Dec 2022 03:09:11 GMT /slideshow/network-mapping-101-course/254845454 dgrapov@slideshare.net(dgrapov) Network mapping 101 course dgrapov Full course: https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/ The course covered all of the steps required to go from `raw data` to a rich `mapped biochemical network` incorporating statistical, multivariate and machine learning results. This included [examples](https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/#topics) and tutorials for: * Preparing raw data for analysis * Multivariate data exploration * Supervised clustering * Machine learning – classification model validation and feature selection * Network analysis - biochemical, structural similarity and correlation networks * Network mapping – putting it all together to create a publication quality network url: https://github.com/CreativeDataSolutions/CDS.courses/blob/gh-pages/courses/network_mapping_101/materials/lectures/tutorial.pdf <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tutorial-221210030911-aa356370-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Full course: https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/ The course covered all of the steps required to go from `raw data` to a rich `mapped biochemical network` incorporating statistical, multivariate and machine learning results. This included [examples](https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/#topics) and tutorials for: * Preparing raw data for analysis * Multivariate data exploration * Supervised clustering * Machine learning – classification model validation and feature selection * Network analysis - biochemical, structural similarity and correlation networks * Network mapping – putting it all together to create a publication quality network url: https://github.com/CreativeDataSolutions/CDS.courses/blob/gh-pages/courses/network_mapping_101/materials/lectures/tutorial.pdf
Network mapping 101 course from Dmitry Grapov
]]> 34 0 https://cdn.slidesharecdn.com/ss_thumbnails/tutorial-221210030911-aa356370-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine /slideshow/rise-of-deep-learning-for-genomic-proteomic-and-metabolomic-data-integration-in-precision-medicine/112644157 highlights-180902042328
Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.]]>
Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.]]> Sun, 02 Sep 2018 04:23:27 GMT /slideshow/rise-of-deep-learning-for-genomic-proteomic-and-metabolomic-data-integration-in-precision-medicine/112644157 dgrapov@slideshare.net(dgrapov) Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine dgrapov Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/highlights-180902042328-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.
Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine from Dmitry Grapov
]]> 763 3 https://cdn.slidesharecdn.com/ss_thumbnails/highlights-180902042328-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Dmitry Grapov Resume and CV /slideshow/dmitry-grapov-resume-and-cv/76617911 dmitrygrapovresume2017-170603162942
current: https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8 I am always looking for the next data science, machine learning and visualization challenge. Here is a link to my up to date resume: https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8 cv: https://drive.google.com/open?id=0B51AEMfo-fh9Z05aM2p6XzFIOFE]]>
current: https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8 I am always looking for the next data science, machine learning and visualization challenge. Here is a link to my up to date resume: https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8 cv: https://drive.google.com/open?id=0B51AEMfo-fh9Z05aM2p6XzFIOFE]]> Sat, 03 Jun 2017 16:29:42 GMT /slideshow/dmitry-grapov-resume-and-cv/76617911 dgrapov@slideshare.net(dgrapov) Dmitry Grapov Resume and CV dgrapov current: https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8 I am always looking for the next data science, machine learning and visualization challenge. Here is a link to my up to date resume: https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8 cv: https://drive.google.com/open?id=0B51AEMfo-fh9Z05aM2p6XzFIOFE <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dmitrygrapovresume2017-170603162942-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> current: https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8 I am always looking for the next data science, machine learning and visualization challenge. Here is a link to my up to date resume: https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8 cv: https://drive.google.com/open?id=0B51AEMfo-fh9Z05aM2p6XzFIOFE
Dmitry Grapov Resume and CV from Dmitry Grapov
]]> 2186 10 https://cdn.slidesharecdn.com/ss_thumbnails/dmitrygrapovresume2017-170603162942-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Machine Learning Powered Metabolomic Network Analysis /slideshow/machine-learning-powered-metabolomic-network-analysis/76617616 metxemn2017final-170603160652
https://www.youtube.com/watch?v=Y_-o-4rKxUk Machine learning powered metabolomic network analysis Dmitry Grapov PhD, Director of Data Science and Bioinformatics, CDS- Creative Data Solutions www.createdatasol.com Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks. Learn more at www.createdatasol.com ]]>
https://www.youtube.com/watch?v=Y_-o-4rKxUk Machine learning powered metabolomic network analysis Dmitry Grapov PhD, Director of Data Science and Bioinformatics, CDS- Creative Data Solutions www.createdatasol.com Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks. Learn more at www.createdatasol.com ]]> Sat, 03 Jun 2017 16:06:52 GMT /slideshow/machine-learning-powered-metabolomic-network-analysis/76617616 dgrapov@slideshare.net(dgrapov) Machine Learning Powered Metabolomic Network Analysis dgrapov https://www.youtube.com/watch?v=Y_-o-4rKxUk Machine learning powered metabolomic network analysis Dmitry Grapov PhD, Director of Data Science and Bioinformatics, CDS- Creative Data Solutions www.createdatasol.com Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks. Learn more at www.createdatasol.com <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/metxemn2017final-170603160652-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> https://www.youtube.com/watch?v=Y_-o-4rKxUk Machine learning powered metabolomic network analysis Dmitry Grapov PhD, Director of Data Science and Bioinformatics, CDS- Creative Data Solutions www.createdatasol.com Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks. Learn more at www.createdatasol.com
Machine Learning Powered Metabolomic Network Analysis from Dmitry Grapov
]]> 9165 5 https://cdn.slidesharecdn.com/ss_thumbnails/metxemn2017final-170603160652-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Complex Systems Biology Informed Data Analysis and Machine Learning /slideshow/complex-systems-biology-informed-data-analysis-and-machine-learning/69575746 teri2016-161127230243
Summary of biochemical data analysis methods enabling complex systems biology based inference.]]>
Summary of biochemical data analysis methods enabling complex systems biology based inference.]]> Sun, 27 Nov 2016 23:02:43 GMT /slideshow/complex-systems-biology-informed-data-analysis-and-machine-learning/69575746 dgrapov@slideshare.net(dgrapov) Complex Systems Biology Informed Data Analysis and Machine Learning dgrapov Summary of biochemical data analysis methods enabling complex systems biology based inference. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/teri2016-161127230243-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Summary of biochemical data analysis methods enabling complex systems biology based inference.
Complex Systems Biology Informed Data Analysis and Machine Learning from Dmitry Grapov
]]> 1796 9 https://cdn.slidesharecdn.com/ss_thumbnails/teri2016-161127230243-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data analysis workflows part 1 2015 /slideshow/data-analysis-workflows-part-1-2015/53759340 dataanalysisworkflowspart12015-151010014252-lva1-app6891
part 2 https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Workflow/report/report.md]]>
part 2 https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Workflow/report/report.md]]> Sat, 10 Oct 2015 01:42:52 GMT /slideshow/data-analysis-workflows-part-1-2015/53759340 dgrapov@slideshare.net(dgrapov) Data analysis workflows part 1 2015 dgrapov part 2 https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Workflow/report/report.md <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dataanalysisworkflowspart12015-151010014252-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> part 2 https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Workflow/report/report.md
Data analysis workflows part 1 2015 from Dmitry Grapov
]]> 1654 4 https://cdn.slidesharecdn.com/ss_thumbnails/dataanalysisworkflowspart12015-151010014252-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data analysis workflows part 2 2015 /slideshow/data-analysis-workflows-part-2-2015/53759178 dataanalysisworkflowspart22015-151010012953-lva1-app6891
previous: https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Workflow/report/report.md]]>
previous: https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Workflow/report/report.md]]> Sat, 10 Oct 2015 01:29:53 GMT /slideshow/data-analysis-workflows-part-2-2015/53759178 dgrapov@slideshare.net(dgrapov) Data analysis workflows part 2 2015 dgrapov previous: https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Workflow/report/report.md <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dataanalysisworkflowspart22015-151010012953-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> previous: https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Workflow/report/report.md
Data analysis workflows part 2 2015 from Dmitry Grapov
]]> 673 6 https://cdn.slidesharecdn.com/ss_thumbnails/dataanalysisworkflowspart22015-151010012953-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Metabolomics and Beyond Challenges and Strategies for Next-gen Omic Analyses /slideshow/metabolomics-and-beyond-challenges-and-strategies-for-nextgen-omic-analyses/52953204 metabolomicssociety2015-150919014258-lva1-app6891
https://www.youtube.com/watch?v=4AhBN5Q1oMs]]>
https://www.youtube.com/watch?v=4AhBN5Q1oMs]]> Sat, 19 Sep 2015 01:42:58 GMT /slideshow/metabolomics-and-beyond-challenges-and-strategies-for-nextgen-omic-analyses/52953204 dgrapov@slideshare.net(dgrapov) Metabolomics and Beyond Challenges and Strategies for Next-gen Omic Analyses dgrapov https://www.youtube.com/watch?v=4AhBN5Q1oMs <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/metabolomicssociety2015-150919014258-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> https://www.youtube.com/watch?v=4AhBN5Q1oMs
Metabolomics and Beyond Challenges and Strategies for Next-gen Omic Analyses from Dmitry Grapov
]]> 6521 6 https://cdn.slidesharecdn.com/ss_thumbnails/metabolomicssociety2015-150919014258-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Case Study: Overview of Metabolomic Data Normalization Strategies /slideshow/case-study-metabolomic-data-normalization-example/51171155 normalizationexample-150801143533-lva1-app6892
Five normalization methods were compared, of which the combination of qc-LOESS and cubic splines showed the best performance based on within-batch and between-batch variable relative standard deviations for QCs. This approach was used to normalize sample measurements the results of which were analyzed using principal components analysis.]]>
Five normalization methods were compared, of which the combination of qc-LOESS and cubic splines showed the best performance based on within-batch and between-batch variable relative standard deviations for QCs. This approach was used to normalize sample measurements the results of which were analyzed using principal components analysis.]]> Sat, 01 Aug 2015 14:35:33 GMT /slideshow/case-study-metabolomic-data-normalization-example/51171155 dgrapov@slideshare.net(dgrapov) Case Study: Overview of Metabolomic Data Normalization Strategies dgrapov Five normalization methods were compared, of which the combination of qc-LOESS and cubic splines showed the best performance based on within-batch and between-batch variable relative standard deviations for QCs. This approach was used to normalize sample measurements the results of which were analyzed using principal components analysis. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/normalizationexample-150801143533-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Five normalization methods were compared, of which the combination of qc-LOESS and cubic splines showed the best performance based on within-batch and between-batch variable relative standard deviations for QCs. This approach was used to normalize sample measurements the results of which were analyzed using principal components analysis.
Case Study: Overview of Metabolomic Data Normalization Strategies from Dmitry Grapov
]]> 943 8 https://cdn.slidesharecdn.com/ss_thumbnails/normalizationexample-150801143533-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Modeling poster /slideshow/modeling-poster/47932173 modelingposter-150509013042-lva1-app6891
manuscript: http://ajpendo.physiology.org/content/early/2015/04/01/ajpendo.00019.2015]]>
manuscript: http://ajpendo.physiology.org/content/early/2015/04/01/ajpendo.00019.2015]]> Sat, 09 May 2015 01:30:42 GMT /slideshow/modeling-poster/47932173 dgrapov@slideshare.net(dgrapov) Modeling poster dgrapov manuscript: http://ajpendo.physiology.org/content/early/2015/04/01/ajpendo.00019.2015 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/modelingposter-150509013042-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> manuscript: http://ajpendo.physiology.org/content/early/2015/04/01/ajpendo.00019.2015
Modeling poster from Dmitry Grapov
]]> 559 1 https://cdn.slidesharecdn.com/ss_thumbnails/modelingposter-150509013042-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Mapping to the Metabolomic Manifold /slideshow/mapping-to-the-metabolomic-manifold/41005046 omicsmanifold-141101190719-conversion-gate02
Description of network mapping and data visualization approaches for metabolomic and genomic data integration.]]>
Description of network mapping and data visualization approaches for metabolomic and genomic data integration.]]> Sat, 01 Nov 2014 19:07:19 GMT /slideshow/mapping-to-the-metabolomic-manifold/41005046 dgrapov@slideshare.net(dgrapov) Mapping to the Metabolomic Manifold dgrapov Description of network mapping and data visualization approaches for metabolomic and genomic data integration. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/omicsmanifold-141101190719-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Description of network mapping and data visualization approaches for metabolomic and genomic data integration.
Mapping to the Metabolomic Manifold from Dmitry Grapov
]]> 4288 2 https://cdn.slidesharecdn.com/ss_thumbnails/omicsmanifold-141101190719-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 3 data normalization (2014 lab tutorial) /slideshow/3-data-normalization-2014-lab-tutorial/40148427 3-datanormalization-141011122615-conversion-gate02
Get more information: http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/ Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center. ]]>
Get more information: http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/ Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center. ]]> Sat, 11 Oct 2014 12:26:15 GMT /slideshow/3-data-normalization-2014-lab-tutorial/40148427 dgrapov@slideshare.net(dgrapov) 3 data normalization (2014 lab tutorial) dgrapov Get more information: http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/ Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/3-datanormalization-141011122615-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Get more information: http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/ Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center.
3 data normalization (2014 lab tutorial) from Dmitry Grapov
]]> 1023 7 https://cdn.slidesharecdn.com/ss_thumbnails/3-datanormalization-141011122615-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Metabolomic Data Analysis Workshop and Tutorials (2014) /slideshow/0-introduction-40148424/40148424 0-introduction-141011122613-conversion-gate02
Get more information: http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/ Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center. Similar to last year, I’ve posted all the content (lectures, labs and software) for any one to follow along with at their own pace. I also plan to release videos for all the lectures and labs. ]]>
Get more information: http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/ Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center. Similar to last year, I’ve posted all the content (lectures, labs and software) for any one to follow along with at their own pace. I also plan to release videos for all the lectures and labs. ]]> Sat, 11 Oct 2014 12:26:13 GMT /slideshow/0-introduction-40148424/40148424 dgrapov@slideshare.net(dgrapov) Metabolomic Data Analysis Workshop and Tutorials (2014) dgrapov Get more information: http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/ Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center. Similar to last year, I’ve posted all the content (lectures, labs and software) for any one to follow along with at their own pace. I also plan to release videos for all the lectures and labs. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/0-introduction-141011122613-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Get more information: http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/ Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center. Similar to last year, I’ve posted all the content (lectures, labs and software) for any one to follow along with at their own pace. I also plan to release videos for all the lectures and labs.
Metabolomic Data Analysis Workshop and Tutorials (2014) from Dmitry Grapov
]]> 17413 7 https://cdn.slidesharecdn.com/ss_thumbnails/0-introduction-141011122613-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Normalization of Large-Scale Metabolomic Studies 2014 /dgrapov/normalization-of-largescale-metabolomic-studies visualizationgroupdatanormalization081114-140812124657-phpapp01
Overview of common data normalization approaches with applications to 2 large scale metabolomic studies.]]>
Overview of common data normalization approaches with applications to 2 large scale metabolomic studies.]]> Tue, 12 Aug 2014 12:46:57 GMT /dgrapov/normalization-of-largescale-metabolomic-studies dgrapov@slideshare.net(dgrapov) Normalization of Large-Scale Metabolomic Studies 2014 dgrapov Overview of common data normalization approaches with applications to 2 large scale metabolomic studies. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/visualizationgroupdatanormalization081114-140812124657-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Overview of common data normalization approaches with applications to 2 large scale metabolomic studies.
Normalization of Large-Scale Metabolomic Studies 2014 from Dmitry Grapov
]]> 2084 4 https://cdn.slidesharecdn.com/ss_thumbnails/visualizationgroupdatanormalization081114-140812124657-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Gene Ontology Enrichment Network Analysis -Tutorial /slideshow/proteomics-workshop-2014-lab-dmitry-grapov/37828362 proteomicsworkshop2014lab-dmitrygrapov-140809101238-phpapp01
Step by step tutorial for conducting GO enrichment analysis and then creating a network from the results. Material from the UC Davis 2014 Proteomics Workshop. See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/ ]]>
Step by step tutorial for conducting GO enrichment analysis and then creating a network from the results. Material from the UC Davis 2014 Proteomics Workshop. See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/ ]]> Sat, 09 Aug 2014 10:12:38 GMT /slideshow/proteomics-workshop-2014-lab-dmitry-grapov/37828362 dgrapov@slideshare.net(dgrapov) Gene Ontology Enrichment Network Analysis -Tutorial dgrapov Step by step tutorial for conducting GO enrichment analysis and then creating a network from the results. Material from the UC Davis 2014 Proteomics Workshop. See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/proteomicsworkshop2014lab-dmitrygrapov-140809101238-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Step by step tutorial for conducting GO enrichment analysis and then creating a network from the results. Material from the UC Davis 2014 Proteomics Workshop. See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
Gene Ontology Enrichment Network Analysis -Tutorial from Dmitry Grapov
]]> 11895 8 https://cdn.slidesharecdn.com/ss_thumbnails/proteomicsworkshop2014lab-dmitrygrapov-140809101238-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Prote-OMIC Data Analysis and Visualization /slideshow/proteomics-workshop-2014-dmitry-grapov/37828361 proteomicsworkshop2014-dmitrygrapov-140809101234-phpapp01
Introductory lecture to multivariate analysis of proteomic data. Material from the UC Davis 2014 Proteomics Workshop. See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/]]>
Introductory lecture to multivariate analysis of proteomic data. Material from the UC Davis 2014 Proteomics Workshop. See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/]]> Sat, 09 Aug 2014 10:12:34 GMT /slideshow/proteomics-workshop-2014-dmitry-grapov/37828361 dgrapov@slideshare.net(dgrapov) Prote-OMIC Data Analysis and Visualization dgrapov Introductory lecture to multivariate analysis of proteomic data. Material from the UC Davis 2014 Proteomics Workshop. See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/proteomicsworkshop2014-dmitrygrapov-140809101234-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Introductory lecture to multivariate analysis of proteomic data. Material from the UC Davis 2014 Proteomics Workshop. See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
Prote-OMIC Data Analysis and Visualization from Dmitry Grapov
]]> 5723 4 https://cdn.slidesharecdn.com/ss_thumbnails/proteomicsworkshop2014-dmitrygrapov-140809101234-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 American Society of Mass Spectrommetry Conference 2014 /slideshow/american-society-of-mass-spectrommetry-conference-2014/36355041 posterversion2bdg-140626171504-phpapp01
see higher resolution image: https://imdevsoftware.files.wordpress.com/2014/06/asms-2014-j-fahrman.png]]>
see higher resolution image: https://imdevsoftware.files.wordpress.com/2014/06/asms-2014-j-fahrman.png]]> Thu, 26 Jun 2014 17:15:04 GMT /slideshow/american-society-of-mass-spectrommetry-conference-2014/36355041 dgrapov@slideshare.net(dgrapov) American Society of Mass Spectrommetry Conference 2014 dgrapov see higher resolution image: https://imdevsoftware.files.wordpress.com/2014/06/asms-2014-j-fahrman.png <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/posterversion2bdg-140626171504-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> see higher resolution image: https://imdevsoftware.files.wordpress.com/2014/06/asms-2014-j-fahrman.png
American Society of Mass Spectrommetry Conference 2014 from Dmitry Grapov
]]> 1322 3 https://cdn.slidesharecdn.com/ss_thumbnails/posterversion2bdg-140626171504-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Multivarite and Network Tools for Biological Data Analysis /slideshow/multivarite-and-network-tools-for-biological-data-analysis/36354141 asms2014v2dmitrygrapov-140626164212-phpapp01
See video: http://imdevsoftware.wordpress.com/2014/06/27/multivariate-data-analysis-and-visualization-through-network-mapping/]]>
See video: http://imdevsoftware.wordpress.com/2014/06/27/multivariate-data-analysis-and-visualization-through-network-mapping/]]> Thu, 26 Jun 2014 16:42:12 GMT /slideshow/multivarite-and-network-tools-for-biological-data-analysis/36354141 dgrapov@slideshare.net(dgrapov) Multivarite and network tools for biological data analysis dgrapov See video: http://imdevsoftware.wordpress.com/2014/06/27/multivariate-data-analysis-and-visualization-through-network-mapping/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/asms2014v2dmitrygrapov-140626164212-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> See video: http://imdevsoftware.wordpress.com/2014/06/27/multivariate-data-analysis-and-visualization-through-network-mapping/
Multivarite and network tools for biological data analysis from Dmitry Grapov
]]> 28108 9 https://cdn.slidesharecdn.com/ss_thumbnails/asms2014v2dmitrygrapov-140626164212-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data Normalization Approaches for Large-scale Biological Studies /slideshow/data-normalization-approaches-for-largescale-biological-studies/35499177 datanormalizationfiehnlabseminar050314-140604170356-phpapp02
Overview of how to estimate data quality and validate normalization approaches to remove analytical variance. See here for animations used in the presentation: http://imdevsoftware.wordpress.com/2014/06/04/using-repeated-measures-to-remove-artifacts-from-longitudinal-data/]]>
Overview of how to estimate data quality and validate normalization approaches to remove analytical variance. See here for animations used in the presentation: http://imdevsoftware.wordpress.com/2014/06/04/using-repeated-measures-to-remove-artifacts-from-longitudinal-data/]]> Wed, 04 Jun 2014 17:03:56 GMT /slideshow/data-normalization-approaches-for-largescale-biological-studies/35499177 dgrapov@slideshare.net(dgrapov) Data Normalization Approaches for Large-scale Biological Studies dgrapov Overview of how to estimate data quality and validate normalization approaches to remove analytical variance. See here for animations used in the presentation: http://imdevsoftware.wordpress.com/2014/06/04/using-repeated-measures-to-remove-artifacts-from-longitudinal-data/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datanormalizationfiehnlabseminar050314-140604170356-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Overview of how to estimate data quality and validate normalization approaches to remove analytical variance. See here for animations used in the presentation: http://imdevsoftware.wordpress.com/2014/06/04/using-repeated-measures-to-remove-artifacts-from-longitudinal-data/
Data Normalization Approaches for Large-scale Biological Studies from Dmitry Grapov
]]> 13283 7 https://cdn.slidesharecdn.com/ss_thumbnails/datanormalizationfiehnlabseminar050314-140604170356-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-dgrapov-48x48.jpg?cb=1683943769 Expert in bioinformatics, data science and engineering, with experience in developing and leading teams, to design and implement massively scalable cloud computing strategies for big data workflows, data visualization, analytics, and machine learning creative-data.science/ https://cdn.slidesharecdn.com/ss_thumbnails/course2-230429023940-9c84f25e-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/r-programming-for-data-science-a-beginners-guide-257617883/257617883 R programming for Data... https://cdn.slidesharecdn.com/ss_thumbnails/tutorial-221210030911-aa356370-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/network-mapping-101-course/254845454 Network mapping 101 co... https://cdn.slidesharecdn.com/ss_thumbnails/highlights-180902042328-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/rise-of-deep-learning-for-genomic-proteomic-and-metabolomic-data-integration-in-precision-medicine/112644157 Rise of Deep Learning ...