際際滷shows by User: bukharig8 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: bukharig8 / Wed, 09 May 2018 21:24:18 GMT 際際滷Share feed for 際際滷shows by User: bukharig8 CEDAR: Easing Authoring of Metadata to Make Biomedical Data Sets More Findable and Reusable /slideshow/cedar-easing-authoring-of-metadata-to-make-biomedical-data-sets-more-findable-and-reusable/96564085 bd2k2016cedaroverview-180509212418
CEDAR: Easing Authoring of Metadata to Make Biomedical Data Sets More Findable and Reusable ]]>
CEDAR: Easing Authoring of Metadata to Make Biomedical Data Sets More Findable and Reusable ]]> Wed, 09 May 2018 21:24:18 GMT /slideshow/cedar-easing-authoring-of-metadata-to-make-biomedical-data-sets-more-findable-and-reusable/96564085 bukharig8@slideshare.net(bukharig8) CEDAR: Easing Authoring of Metadata to Make Biomedical Data Sets More Findable and Reusable bukharig8 CEDAR: Easing Authoring of Metadata to Make Biomedical Data Sets More Findable and Reusable <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bd2k2016cedaroverview-180509212418-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> CEDAR: Easing Authoring of Metadata to Make Biomedical Data Sets More Findable and Reusable
CEDAR: Easing Authoring of Metadata to Make Biomedical Data Sets More Findable and Reusable from Syed Ahmad Chan Bukhari, PhD
]]> 450 1 https://cdn.slidesharecdn.com/ss_thumbnails/bd2k2016cedaroverview-180509212418-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 Finding and Reusing Biomedical Datasets using CEDAR Metadata Repository and Tools /slideshow/finding-and-reusing-biomedical-datasets-using-cedar-metadata-repository-and-tools/96563932 cedaroverviewbigdata2017-180509212232
Finding and Reusing Biomedical Datasets using CEDAR Metadata Repository and Tools ]]>
Finding and Reusing Biomedical Datasets using CEDAR Metadata Repository and Tools ]]> Wed, 09 May 2018 21:22:32 GMT /slideshow/finding-and-reusing-biomedical-datasets-using-cedar-metadata-repository-and-tools/96563932 bukharig8@slideshare.net(bukharig8) Finding and Reusing Biomedical Datasets using CEDAR Metadata Repository and Tools bukharig8 Finding and Reusing Biomedical Datasets using CEDAR Metadata Repository and Tools <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cedaroverviewbigdata2017-180509212232-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Finding and Reusing Biomedical Datasets using CEDAR Metadata Repository and Tools
Finding and Reusing Biomedical Datasets using CEDAR Metadata Repository and Tools from Syed Ahmad Chan Bukhari, PhD
]]> 56 1 https://cdn.slidesharecdn.com/ss_thumbnails/cedaroverviewbigdata2017-180509212232-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 CEDAR Technologies for AIRR Submissions /slideshow/cedar-technologies-for-airr-submissions/96563820 cedartechnologiesforairrsubmissionsposter-180509212114
CEDAR Technologies for AIRR Submissions]]>
CEDAR Technologies for AIRR Submissions]]> Wed, 09 May 2018 21:21:14 GMT /slideshow/cedar-technologies-for-airr-submissions/96563820 bukharig8@slideshare.net(bukharig8) CEDAR Technologies for AIRR Submissions bukharig8 CEDAR Technologies for AIRR Submissions <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cedartechnologiesforairrsubmissionsposter-180509212114-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> CEDAR Technologies for AIRR Submissions
CEDAR Technologies for AIRR Submissions from Syed Ahmad Chan Bukhari, PhD
]]> 47 2 https://cdn.slidesharecdn.com/ss_thumbnails/cedartechnologiesforairrsubmissionsposter-180509212114-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 CEDAR: Web-Based Tools for Accelerating the Creation of Standardized Metadata /slideshow/cedar-webbased-tools-for-accelerating-the-creation-of-standardized-metadata-96563670/96563670 rdabmircedarposter-180509211933
CEDAR: Web-Based Tools for Accelerating the Creation of Standardized Metadata ]]>
CEDAR: Web-Based Tools for Accelerating the Creation of Standardized Metadata ]]> Wed, 09 May 2018 21:19:33 GMT /slideshow/cedar-webbased-tools-for-accelerating-the-creation-of-standardized-metadata-96563670/96563670 bukharig8@slideshare.net(bukharig8) CEDAR: Web-Based Tools for Accelerating the Creation of Standardized Metadata bukharig8 CEDAR: Web-Based Tools for Accelerating the Creation of Standardized Metadata <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rdabmircedarposter-180509211933-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> CEDAR: Web-Based Tools for Accelerating the Creation of Standardized Metadata
CEDAR: Web-Based Tools for Accelerating the Creation of Standardized Metadata from Syed Ahmad Chan Bukhari, PhD
]]> 46 2 https://cdn.slidesharecdn.com/ss_thumbnails/rdabmircedarposter-180509211933-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 Leveraging CEDAR workbench for ontology-linked submission of adaptive immune receptor repertoire data to the Sequence Read Achieve (SRA) /slideshow/leveraging-cedar-workbench-for-ontologylinked-submission-of-adaptive-immune-receptor-repertoire-data-to-the-sequence-read-achieve-sra/96563502 1ac97527-e195-4d8a-b60e-9eff591944a3-161124013716-180509211727
Leveraging CEDAR workbench for ontology-linked submission of adaptive immune receptor repertoire data to the Sequence Read Achieve (SRA)]]>
Leveraging CEDAR workbench for ontology-linked submission of adaptive immune receptor repertoire data to the Sequence Read Achieve (SRA)]]> Wed, 09 May 2018 21:17:27 GMT /slideshow/leveraging-cedar-workbench-for-ontologylinked-submission-of-adaptive-immune-receptor-repertoire-data-to-the-sequence-read-achieve-sra/96563502 bukharig8@slideshare.net(bukharig8) Leveraging CEDAR workbench for ontology-linked submission of adaptive immune receptor repertoire data to the Sequence Read Achieve (SRA) bukharig8 Leveraging CEDAR workbench for ontology-linked submission of adaptive immune receptor repertoire data to the Sequence Read Achieve (SRA) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/1ac97527-e195-4d8a-b60e-9eff591944a3-161124013716-180509211727-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Leveraging CEDAR workbench for ontology-linked submission of adaptive immune receptor repertoire data to the Sequence Read Achieve (SRA)
Leveraging CEDAR workbench for ontology-linked submission of adaptive immune receptor repertoire data to the Sequence Read Achieve (SRA) from Syed Ahmad Chan Bukhari, PhD
]]> 82 4 https://cdn.slidesharecdn.com/ss_thumbnails/1ac97527-e195-4d8a-b60e-9eff591944a3-161124013716-180509211727-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 Standardization of the HIPC Data Templates /slideshow/standardization-of-the-hipc-data-templates/96563315 immportstandardahmad1-170505225812-180509211515
Human Immunology Project Consortium]]>
Human Immunology Project Consortium]]> Wed, 09 May 2018 21:15:15 GMT /slideshow/standardization-of-the-hipc-data-templates/96563315 bukharig8@slideshare.net(bukharig8) Standardization of the HIPC Data Templates bukharig8 Human Immunology Project Consortium <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/immportstandardahmad1-170505225812-180509211515-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Human Immunology Project Consortium
Standardization of the HIPC Data Templates from Syed Ahmad Chan Bukhari, PhD
]]> 68 2 https://cdn.slidesharecdn.com/ss_thumbnails/immportstandardahmad1-170505225812-180509211515-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 A semantic framework for biomedical image discovery /slideshow/a-semantic-framework-for-biomedical-image-discovery/85225921 icyrusswat4lsslidesfinal-171228214553
. Images have an irrefutably central role in scientific discovery and discourse. However, the issues associated with knowledge management and utility operations unique to image data are only recently gaining recognition. In our previous work, we have developed Yale Image finder (YIF), which is a novel Biomedical image search engine that indexes around two million biomedical image data, along with associated metadata. While YIF is considered to be a veritable source of easily accessible biomedical images, there are still a number of usability and interoperability challenges that have yet to be addressed. To overcome these issues and to accelerate the adoption of the YIF for next generation biomedical applications, we have developed a publically accessible semantic API for biomedical images with multiple modalities. The core API called iCyrus is powered by a dedicated semantic architecture that exposes the YIF content as linked data, permitting integration with related information resources and consumption by linked data-aware data services. To facilitate the adhoc integration of image data with other online data resources, we also built semantic web services for iCyrus, such that it is compatible with the SADI semantic web service framework. The utility of the combined infrastructure is illustrated with a number of compelling use cases and further extended through the incorporation of Domeo, a well known tool for open annotation. Domeo facilitates enhanced search over the images using annotations provided through crowdsourcing. The iCyrus triplestore currently holds more than thirty-five million triples and can be accessed and operated through syntactic or semantic query interfaces. Core features of the iCyrus API, namely: data reusability, system interoperability, semantic image search, automatic update and dedicated semantic infrastructure make iCyrus a state of the art resource for image data discovery and retrieval]]>
. Images have an irrefutably central role in scientific discovery and discourse. However, the issues associated with knowledge management and utility operations unique to image data are only recently gaining recognition. In our previous work, we have developed Yale Image finder (YIF), which is a novel Biomedical image search engine that indexes around two million biomedical image data, along with associated metadata. While YIF is considered to be a veritable source of easily accessible biomedical images, there are still a number of usability and interoperability challenges that have yet to be addressed. To overcome these issues and to accelerate the adoption of the YIF for next generation biomedical applications, we have developed a publically accessible semantic API for biomedical images with multiple modalities. The core API called iCyrus is powered by a dedicated semantic architecture that exposes the YIF content as linked data, permitting integration with related information resources and consumption by linked data-aware data services. To facilitate the adhoc integration of image data with other online data resources, we also built semantic web services for iCyrus, such that it is compatible with the SADI semantic web service framework. The utility of the combined infrastructure is illustrated with a number of compelling use cases and further extended through the incorporation of Domeo, a well known tool for open annotation. Domeo facilitates enhanced search over the images using annotations provided through crowdsourcing. The iCyrus triplestore currently holds more than thirty-five million triples and can be accessed and operated through syntactic or semantic query interfaces. Core features of the iCyrus API, namely: data reusability, system interoperability, semantic image search, automatic update and dedicated semantic infrastructure make iCyrus a state of the art resource for image data discovery and retrieval]]> Thu, 28 Dec 2017 21:45:53 GMT /slideshow/a-semantic-framework-for-biomedical-image-discovery/85225921 bukharig8@slideshare.net(bukharig8) A semantic framework for biomedical image discovery bukharig8 . Images have an irrefutably central role in scientific discovery and discourse. However, the issues associated with knowledge management and utility operations unique to image data are only recently gaining recognition. In our previous work, we have developed Yale Image finder (YIF), which is a novel Biomedical image search engine that indexes around two million biomedical image data, along with associated metadata. While YIF is considered to be a veritable source of easily accessible biomedical images, there are still a number of usability and interoperability challenges that have yet to be addressed. To overcome these issues and to accelerate the adoption of the YIF for next generation biomedical applications, we have developed a publically accessible semantic API for biomedical images with multiple modalities. The core API called iCyrus is powered by a dedicated semantic architecture that exposes the YIF content as linked data, permitting integration with related information resources and consumption by linked data-aware data services. To facilitate the adhoc integration of image data with other online data resources, we also built semantic web services for iCyrus, such that it is compatible with the SADI semantic web service framework. The utility of the combined infrastructure is illustrated with a number of compelling use cases and further extended through the incorporation of Domeo, a well known tool for open annotation. Domeo facilitates enhanced search over the images using annotations provided through crowdsourcing. The iCyrus triplestore currently holds more than thirty-five million triples and can be accessed and operated through syntactic or semantic query interfaces. Core features of the iCyrus API, namely: data reusability, system interoperability, semantic image search, automatic update and dedicated semantic infrastructure make iCyrus a state of the art resource for image data discovery and retrieval <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/icyrusswat4lsslidesfinal-171228214553-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> . Images have an irrefutably central role in scientific discovery and discourse. However, the issues associated with knowledge management and utility operations unique to image data are only recently gaining recognition. In our previous work, we have developed Yale Image finder (YIF), which is a novel Biomedical image search engine that indexes around two million biomedical image data, along with associated metadata. While YIF is considered to be a veritable source of easily accessible biomedical images, there are still a number of usability and interoperability challenges that have yet to be addressed. To overcome these issues and to accelerate the adoption of the YIF for next generation biomedical applications, we have developed a publically accessible semantic API for biomedical images with multiple modalities. The core API called iCyrus is powered by a dedicated semantic architecture that exposes the YIF content as linked data, permitting integration with related information resources and consumption by linked data-aware data services. To facilitate the adhoc integration of image data with other online data resources, we also built semantic web services for iCyrus, such that it is compatible with the SADI semantic web service framework. The utility of the combined infrastructure is illustrated with a number of compelling use cases and further extended through the incorporation of Domeo, a well known tool for open annotation. Domeo facilitates enhanced search over the images using annotations provided through crowdsourcing. The iCyrus triplestore currently holds more than thirty-five million triples and can be accessed and operated through syntactic or semantic query interfaces. Core features of the iCyrus API, namely: data reusability, system interoperability, semantic image search, automatic update and dedicated semantic infrastructure make iCyrus a state of the art resource for image data discovery and retrieval
A semantic framework for biomedical image discovery from Syed Ahmad Chan Bukhari, PhD
]]> 595 6 https://cdn.slidesharecdn.com/ss_thumbnails/icyrusswat4lsslidesfinal-171228214553-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 Semantic enrichment and similarity approximation for biomedical sequence images /bukharig8/semantic-enrichment-and-similarity-approximation-for-biomedical-sequence-images semanticenrichmentandsimilarityapproximationforbiomedicalsequenceimages-171228213039
ABSTRACT Scientific publications are considered as the most up-to-date resource of ongoing research activities and scientific knowledge. Efficient practices for accessing biomedical publications are key to allowing a timely transfer of information from the scientific research community to peer investigators and other healthcare practitioners. Biomedical sequence images published within the literature play a central role in life science discoveries. Whereas advanced text-mining pipelines for information retrieval and knowledge extraction are now commonplace methodologies for processing documents, the ongoing challenges associated with knowledge management and utility operations unique to biomedical image data are only recently gaining recognition. Sequence images depicting key findings of research papers contain rich information derived from a wide range of biomedical experiments. Searching for relevant sequence images is however error prone as images are still opaque to information retrieval and knowledge extraction engines. Specifically, there is no explicit description or annotation of the sequence image content. Moreover, traditional biomedical search engines, which search image captions for relevant keywords only, offer syntactic search mechanisms without regard for the exact meaning of the query. As proposed in this thesis, semantic enrichment of biomedical sequence images is a solution which adopts a combination of technologies to harness the comprehensive information associated with, and contained in, biomedical sequence images. Extracted information from sequence images is used as seed data to aggregate and iii harvest new annotations from heterogeneous online biomedical resources. Comprehensive semantic enrichment of biomedical images incorporates a variety of knowledge infrastructure components and services including image feature extraction, semantic web data services, linked open data and crowd annotation. Together, these resources make it possible to automatically and/or semi-automatically discover and semantically interlink new information in a way that supports semantic search for sequence images. The resulting enriched sequence images are readily reusable based on their semantic annotations and can be made available for use in ad-hoc data integration activities. Furthermore, to support image reuse this thesis introduces a mechanism for identifying similar sequence images based on fuzzy inference and cosine similarity techniques that can retrieve and classify the related sequence images based on their semantic annotations. The outcomes of this research work will be relevant to a variety of user groups ranging from clinicians and researchers searching with sequence image data.]]>
ABSTRACT Scientific publications are considered as the most up-to-date resource of ongoing research activities and scientific knowledge. Efficient practices for accessing biomedical publications are key to allowing a timely transfer of information from the scientific research community to peer investigators and other healthcare practitioners. Biomedical sequence images published within the literature play a central role in life science discoveries. Whereas advanced text-mining pipelines for information retrieval and knowledge extraction are now commonplace methodologies for processing documents, the ongoing challenges associated with knowledge management and utility operations unique to biomedical image data are only recently gaining recognition. Sequence images depicting key findings of research papers contain rich information derived from a wide range of biomedical experiments. Searching for relevant sequence images is however error prone as images are still opaque to information retrieval and knowledge extraction engines. Specifically, there is no explicit description or annotation of the sequence image content. Moreover, traditional biomedical search engines, which search image captions for relevant keywords only, offer syntactic search mechanisms without regard for the exact meaning of the query. As proposed in this thesis, semantic enrichment of biomedical sequence images is a solution which adopts a combination of technologies to harness the comprehensive information associated with, and contained in, biomedical sequence images. Extracted information from sequence images is used as seed data to aggregate and iii harvest new annotations from heterogeneous online biomedical resources. Comprehensive semantic enrichment of biomedical images incorporates a variety of knowledge infrastructure components and services including image feature extraction, semantic web data services, linked open data and crowd annotation. Together, these resources make it possible to automatically and/or semi-automatically discover and semantically interlink new information in a way that supports semantic search for sequence images. The resulting enriched sequence images are readily reusable based on their semantic annotations and can be made available for use in ad-hoc data integration activities. Furthermore, to support image reuse this thesis introduces a mechanism for identifying similar sequence images based on fuzzy inference and cosine similarity techniques that can retrieve and classify the related sequence images based on their semantic annotations. The outcomes of this research work will be relevant to a variety of user groups ranging from clinicians and researchers searching with sequence image data.]]> Thu, 28 Dec 2017 21:30:39 GMT /bukharig8/semantic-enrichment-and-similarity-approximation-for-biomedical-sequence-images bukharig8@slideshare.net(bukharig8) Semantic enrichment and similarity approximation for biomedical sequence images bukharig8 ABSTRACT Scientific publications are considered as the most up-to-date resource of ongoing research activities and scientific knowledge. Efficient practices for accessing biomedical publications are key to allowing a timely transfer of information from the scientific research community to peer investigators and other healthcare practitioners. Biomedical sequence images published within the literature play a central role in life science discoveries. Whereas advanced text-mining pipelines for information retrieval and knowledge extraction are now commonplace methodologies for processing documents, the ongoing challenges associated with knowledge management and utility operations unique to biomedical image data are only recently gaining recognition. Sequence images depicting key findings of research papers contain rich information derived from a wide range of biomedical experiments. Searching for relevant sequence images is however error prone as images are still opaque to information retrieval and knowledge extraction engines. Specifically, there is no explicit description or annotation of the sequence image content. Moreover, traditional biomedical search engines, which search image captions for relevant keywords only, offer syntactic search mechanisms without regard for the exact meaning of the query. As proposed in this thesis, semantic enrichment of biomedical sequence images is a solution which adopts a combination of technologies to harness the comprehensive information associated with, and contained in, biomedical sequence images. Extracted information from sequence images is used as seed data to aggregate and iii harvest new annotations from heterogeneous online biomedical resources. Comprehensive semantic enrichment of biomedical images incorporates a variety of knowledge infrastructure components and services including image feature extraction, semantic web data services, linked open data and crowd annotation. Together, these resources make it possible to automatically and/or semi-automatically discover and semantically interlink new information in a way that supports semantic search for sequence images. The resulting enriched sequence images are readily reusable based on their semantic annotations and can be made available for use in ad-hoc data integration activities. Furthermore, to support image reuse this thesis introduces a mechanism for identifying similar sequence images based on fuzzy inference and cosine similarity techniques that can retrieve and classify the related sequence images based on their semantic annotations. The outcomes of this research work will be relevant to a variety of user groups ranging from clinicians and researchers searching with sequence image data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/semanticenrichmentandsimilarityapproximationforbiomedicalsequenceimages-171228213039-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> ABSTRACT Scientific publications are considered as the most up-to-date resource of ongoing research activities and scientific knowledge. Efficient practices for accessing biomedical publications are key to allowing a timely transfer of information from the scientific research community to peer investigators and other healthcare practitioners. Biomedical sequence images published within the literature play a central role in life science discoveries. Whereas advanced text-mining pipelines for information retrieval and knowledge extraction are now commonplace methodologies for processing documents, the ongoing challenges associated with knowledge management and utility operations unique to biomedical image data are only recently gaining recognition. Sequence images depicting key findings of research papers contain rich information derived from a wide range of biomedical experiments. Searching for relevant sequence images is however error prone as images are still opaque to information retrieval and knowledge extraction engines. Specifically, there is no explicit description or annotation of the sequence image content. Moreover, traditional biomedical search engines, which search image captions for relevant keywords only, offer syntactic search mechanisms without regard for the exact meaning of the query. As proposed in this thesis, semantic enrichment of biomedical sequence images is a solution which adopts a combination of technologies to harness the comprehensive information associated with, and contained in, biomedical sequence images. Extracted information from sequence images is used as seed data to aggregate and iii harvest new annotations from heterogeneous online biomedical resources. Comprehensive semantic enrichment of biomedical images incorporates a variety of knowledge infrastructure components and services including image feature extraction, semantic web data services, linked open data and crowd annotation. Together, these resources make it possible to automatically and/or semi-automatically discover and semantically interlink new information in a way that supports semantic search for sequence images. The resulting enriched sequence images are readily reusable based on their semantic annotations and can be made available for use in ad-hoc data integration activities. Furthermore, to support image reuse this thesis introduces a mechanism for identifying similar sequence images based on fuzzy inference and cosine similarity techniques that can retrieve and classify the related sequence images based on their semantic annotations. The outcomes of this research work will be relevant to a variety of user groups ranging from clinicians and researchers searching with sequence image data.
Semantic enrichment and similarity approximation for biomedical sequence images from Syed Ahmad Chan Bukhari, PhD
]]> 591 6 https://cdn.slidesharecdn.com/ss_thumbnails/semanticenrichmentandsimilarityapproximationforbiomedicalsequenceimages-171228213039-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 MiAIRR:Minimum information about an Adaptive Immune Receptor Repertoire Sequencing Experiment /slideshow/miairrminimum-information-about-an-adaptive-immune-receptor-repertoire-sequencing-experiment/83804222 miairrslides-171210201921
MiAIRR:Minimum information about an Adaptive Immune Receptor Repertoire Sequencing Experiment]]>
MiAIRR:Minimum information about an Adaptive Immune Receptor Repertoire Sequencing Experiment]]> Sun, 10 Dec 2017 20:19:21 GMT /slideshow/miairrminimum-information-about-an-adaptive-immune-receptor-repertoire-sequencing-experiment/83804222 bukharig8@slideshare.net(bukharig8) MiAIRR:Minimum information about an Adaptive Immune Receptor Repertoire Sequencing Experiment bukharig8 MiAIRR:Minimum information about an Adaptive Immune Receptor Repertoire Sequencing Experiment <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/miairrslides-171210201921-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> MiAIRR:Minimum information about an Adaptive Immune Receptor Repertoire Sequencing Experiment
MiAIRR:Minimum information about an Adaptive Immune Receptor Repertoire Sequencing Experiment from Syed Ahmad Chan Bukhari, PhD
]]> 546 2 https://cdn.slidesharecdn.com/ss_thumbnails/miairrslides-171210201921-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 Cedar OnDemand: An intelligent browser extension to generate ontology-based metadata. /bukharig8/cedar-ondemand-an-intelligent-browser-extension-to-generate-ontologybased-metadata cedarondemand-171210201513
Cedar OnDemand: An intelligent browser extension to generate ontology-based metadata.]]>
Cedar OnDemand: An intelligent browser extension to generate ontology-based metadata.]]> Sun, 10 Dec 2017 20:15:13 GMT /bukharig8/cedar-ondemand-an-intelligent-browser-extension-to-generate-ontologybased-metadata bukharig8@slideshare.net(bukharig8) Cedar OnDemand: An intelligent browser extension to generate ontology-based metadata. bukharig8 Cedar OnDemand: An intelligent browser extension to generate ontology-based metadata. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cedarondemand-171210201513-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Cedar OnDemand: An intelligent browser extension to generate ontology-based metadata.
Cedar OnDemand: An intelligent browser extension to generate ontology-based metadata. from Syed Ahmad Chan Bukhari, PhD
]]> 420 4 https://cdn.slidesharecdn.com/ss_thumbnails/cedarondemand-171210201513-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 Use of CEDAR Technology for Ontology-based Submission of Biomedical Data to the NCBI /bukharig8/use-of-cedar-technology-for-ontologybased-submission-of-biomedical-data-to-the-ncbi 130pmsteveahmadcedar-airr2ncbi-171210201301
Use of CEDAR Technology for Ontology-based Submission of Biomedical Data to the NCBI ]]>
Use of CEDAR Technology for Ontology-based Submission of Biomedical Data to the NCBI ]]> Sun, 10 Dec 2017 20:13:00 GMT /bukharig8/use-of-cedar-technology-for-ontologybased-submission-of-biomedical-data-to-the-ncbi bukharig8@slideshare.net(bukharig8) Use of CEDAR Technology for Ontology-based Submission of Biomedical Data to the NCBI bukharig8 Use of CEDAR Technology for Ontology-based Submission of Biomedical Data to the NCBI <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/130pmsteveahmadcedar-airr2ncbi-171210201301-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Use of CEDAR Technology for Ontology-based Submission of Biomedical Data to the NCBI
Use of CEDAR Technology for Ontology-based Submission of Biomedical Data to the NCBI from Syed Ahmad Chan Bukhari, PhD
]]> 457 2 https://cdn.slidesharecdn.com/ss_thumbnails/130pmsteveahmadcedar-airr2ncbi-171210201301-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 CAIRR: A pipeline to submit AIRR data to the NCBI through the CEDAR Workbench /slideshow/cairr-a-pipeline-to-submit-airr-data-to-the-ncbi-through-the-cedar-workbench/83803901 airrmeetngdemo1-171210200943
CAIRR: A pipeline to submit AIRR data to the NCBI through the CEDAR Workbench]]>
CAIRR: A pipeline to submit AIRR data to the NCBI through the CEDAR Workbench]]> Sun, 10 Dec 2017 20:09:43 GMT /slideshow/cairr-a-pipeline-to-submit-airr-data-to-the-ncbi-through-the-cedar-workbench/83803901 bukharig8@slideshare.net(bukharig8) CAIRR: A pipeline to submit AIRR data to the NCBI through the CEDAR Workbench bukharig8 CAIRR: A pipeline to submit AIRR data to the NCBI through the CEDAR Workbench <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/airrmeetngdemo1-171210200943-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> CAIRR: A pipeline to submit AIRR data to the NCBI through the CEDAR Workbench
CAIRR: A pipeline to submit AIRR data to the NCBI through the CEDAR Workbench from Syed Ahmad Chan Bukhari, PhD
]]> 140 1 https://cdn.slidesharecdn.com/ss_thumbnails/airrmeetngdemo1-171210200943-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 BioNLP-SADI: A Suite of interoperable BioNLP Semantic Web Services based on SADI Framework /bukharig8/bionlpsadi-a-suite-of-interoperable-bionlp-semantic-web-services-based-on-sadi-framework cshals2013poster8-130502155733-phpapp01
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]]> Thu, 02 May 2013 15:57:33 GMT /bukharig8/bionlpsadi-a-suite-of-interoperable-bionlp-semantic-web-services-based-on-sadi-framework bukharig8@slideshare.net(bukharig8) BioNLP-SADI: A Suite of interoperable BioNLP Semantic Web Services based on SADI Framework bukharig8 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cshals2013poster8-130502155733-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
BioNLP-SADI: A Suite of interoperable BioNLP Semantic Web Services based on SADI Framework from Syed Ahmad Chan Bukhari, PhD
]]> 752 2 https://cdn.slidesharecdn.com/ss_thumbnails/cshals2013poster8-130502155733-phpapp01-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 Type 2 fuzzy ontology ahmadchan /slideshow/type-2-fuzzy-ontology-ahmadchan/18872893 type-2fuzzyontologyahmadchan-130415153413-phpapp01
A smart coupling of type-2 fuzzy ontology (T2FO) with a multi-agent system: A novel mechanism to automate the personalized itinerary ]]>
A smart coupling of type-2 fuzzy ontology (T2FO) with a multi-agent system: A novel mechanism to automate the personalized itinerary ]]> Mon, 15 Apr 2013 15:34:13 GMT /slideshow/type-2-fuzzy-ontology-ahmadchan/18872893 bukharig8@slideshare.net(bukharig8) Type 2 fuzzy ontology ahmadchan bukharig8 A smart coupling of type-2 fuzzy ontology (T2FO) with a multi-agent system: A novel mechanism to automate the personalized itinerary <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/type-2fuzzyontologyahmadchan-130415153413-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A smart coupling of type-2 fuzzy ontology (T2FO) with a multi-agent system: A novel mechanism to automate the personalized itinerary
Type 2 fuzzy ontology ahmadchan from Syed Ahmad Chan Bukhari, PhD
]]> 1468 5 https://cdn.slidesharecdn.com/ss_thumbnails/type-2fuzzyontologyahmadchan-130415153413-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 AN Intelligent Realtime multiple vessel collision risk assessment system /slideshow/final-presentataion/18872073 finalpresentataion-130415151717-phpapp02
AN Intelligent Realtime multiple vessel collision risk assessment system from VTS point based on Fuzzy Inference System]]>
AN Intelligent Realtime multiple vessel collision risk assessment system from VTS point based on Fuzzy Inference System]]> Mon, 15 Apr 2013 15:17:17 GMT /slideshow/final-presentataion/18872073 bukharig8@slideshare.net(bukharig8) AN Intelligent Realtime multiple vessel collision risk assessment system bukharig8 AN Intelligent Realtime multiple vessel collision risk assessment system from VTS point based on Fuzzy Inference System <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/finalpresentataion-130415151717-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> AN Intelligent Realtime multiple vessel collision risk assessment system from VTS point based on Fuzzy Inference System
AN Intelligent Realtime multiple vessel collision risk assessment system from Syed Ahmad Chan Bukhari, PhD
]]> 1139 5 https://cdn.slidesharecdn.com/ss_thumbnails/finalpresentataion-130415151717-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 Canadian health census to lod /slideshow/canadian-health-census-to-lod/18804872 canadianhealthcensustolod-130414154835-phpapp02
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]]> Sun, 14 Apr 2013 15:48:35 GMT /slideshow/canadian-health-census-to-lod/18804872 bukharig8@slideshare.net(bukharig8) Canadian health census to lod bukharig8 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/canadianhealthcensustolod-130414154835-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Canadian health census to lod from Syed Ahmad Chan Bukhari, PhD
]]> 843 5 https://cdn.slidesharecdn.com/ss_thumbnails/canadianhealthcensustolod-130414154835-phpapp02-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 Type-2 Fuzzy Ontology /slideshow/final-thesis-presentaation/18305474 finalthesispresentaation-130406104523-phpapp02
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]]> Sat, 06 Apr 2013 10:45:23 GMT /slideshow/final-thesis-presentaation/18305474 bukharig8@slideshare.net(bukharig8) Type-2 Fuzzy Ontology bukharig8 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/finalthesispresentaation-130406104523-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Type-2 Fuzzy Ontology from Syed Ahmad Chan Bukhari, PhD
]]> 715 8 https://cdn.slidesharecdn.com/ss_thumbnails/finalthesispresentaation-130406104523-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 BioNLPSADI /slideshow/bio-nlpsadi/18304368 bionlpsadi-130406102332-phpapp01
BioNLP-SADI:A Suite of interoperable BioNLP Semantic Web Services based on SADI Framework]]>
BioNLP-SADI:A Suite of interoperable BioNLP Semantic Web Services based on SADI Framework]]> Sat, 06 Apr 2013 10:23:31 GMT /slideshow/bio-nlpsadi/18304368 bukharig8@slideshare.net(bukharig8) BioNLPSADI bukharig8 BioNLP-SADI:A Suite of interoperable BioNLP Semantic Web Services based on SADI Framework <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bionlpsadi-130406102332-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> BioNLP-SADI:A Suite of interoperable BioNLP Semantic Web Services based on SADI Framework
BioNLPSADI from Syed Ahmad Chan Bukhari, PhD
]]> 704 2 https://cdn.slidesharecdn.com/ss_thumbnails/bionlpsadi-130406102332-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 https://cdn.slidesharecdn.com/profile-photo-bukharig8-48x48.jpg?cb=1570997920 Researcher in #DataScience #SemanticWeb, #KnowledgeManagement, #BiomedicalInformatics, #LinkedBigData and #ArtificialIntelligence at #Yale ahmadchan.com https://cdn.slidesharecdn.com/ss_thumbnails/bd2k2016cedaroverview-180509212418-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/cedar-easing-authoring-of-metadata-to-make-biomedical-data-sets-more-findable-and-reusable/96564085 CEDAR: Easing Authorin... https://cdn.slidesharecdn.com/ss_thumbnails/cedaroverviewbigdata2017-180509212232-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/finding-and-reusing-biomedical-datasets-using-cedar-metadata-repository-and-tools/96563932 Finding and Reusing Bi... https://cdn.slidesharecdn.com/ss_thumbnails/cedartechnologiesforairrsubmissionsposter-180509212114-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/cedar-technologies-for-airr-submissions/96563820 CEDAR Technologies for...