狠狠撸shows by User: goodb

狠狠撸shows by User: goodb / http://www.slideshare.net/images/logo.gif 狠狠撸shows by User: goodb / Wed, 13 Mar 2019 00:43:06 GMT 狠狠撸Share feed for 狠狠撸shows by User: goodb Representing and reasoning with biological knowledge /slideshow/representing-and-reasoning-with-biological-knowledge/135979348 representingandreasoningwithbiologicalknowledge-us2ts-2019-190313004306
Introduces the Gene Ontology, GO Causal Activity Models, and shows how OWL reasoning is used in the project. ]]>
Introduces the Gene Ontology, GO Causal Activity Models, and shows how OWL reasoning is used in the project. ]]> Wed, 13 Mar 2019 00:43:06 GMT /slideshow/representing-and-reasoning-with-biological-knowledge/135979348 goodb@slideshare.net(goodb) Representing and reasoning with biological knowledge goodb Introduces the Gene Ontology, GO Causal Activity Models, and shows how OWL reasoning is used in the project. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/representingandreasoningwithbiologicalknowledge-us2ts-2019-190313004306-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduces the Gene Ontology, GO Causal Activity Models, and shows how OWL reasoning is used in the project.

Representing and reasoning with biological knowledge from Benjamin Good

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0 Integrating Pathway Databases with Gene Ontology Causal Activity Models /goodb/integrating-pathway-databases-with-gene-ontology-causal-activity-models good-pathways2go-rockyaspen-2018-powerpoint-181208004827
The Gene Ontology (GO) Consortium (GOC) is developing a new knowledge representation approach called 鈥榗ausal activity models鈥� (GO-CAM). A GO-CAM describes how one or several gene products contribute to the execution of a biological process. In these models (implemented as OWL instance graphs anchored in Open Biological Ontology (OBO) classes and relations), gene products are linked to molecular activities via semantic relationships like 鈥榚nables鈥�, molecular activities are linked to each other via causal relationships such as 鈥榩ositively regulates鈥�, and sets of molecular activities are defined as 鈥榩arts鈥� of larger biological processes. This approach provides the GOC with a more complete and extensible structure for capturing knowledge of gene function. It also allows for the representation of knowledge typically seen in pathway databases. Here, we present details and results of a rule-based transformation of pathways represented using the BioPAX exchange format into GO-CAMs. We have automatically converted all Reactome pathways into GO-CAMs and are currently working on the conversion of additional resources available through Pathway Commons. By converting pathways into GO-CAMs, we can leverage OWL description logic reasoning over OBO ontologies to infer new biological relationships and detect logical inconsistencies. Further, the conversion helps to increase standardization for the representation of biological entities and processes. The products of this work can be used to improve source databases, for example by inferring new GO annotations for pathways and reactions and can help with the formation of meta-knowledge bases that integrate content from multiple sources.]]>
The Gene Ontology (GO) Consortium (GOC) is developing a new knowledge representation approach called 鈥榗ausal activity models鈥� (GO-CAM). A GO-CAM describes how one or several gene products contribute to the execution of a biological process. In these models (implemented as OWL instance graphs anchored in Open Biological Ontology (OBO) classes and relations), gene products are linked to molecular activities via semantic relationships like 鈥榚nables鈥�, molecular activities are linked to each other via causal relationships such as 鈥榩ositively regulates鈥�, and sets of molecular activities are defined as 鈥榩arts鈥� of larger biological processes. This approach provides the GOC with a more complete and extensible structure for capturing knowledge of gene function. It also allows for the representation of knowledge typically seen in pathway databases. Here, we present details and results of a rule-based transformation of pathways represented using the BioPAX exchange format into GO-CAMs. We have automatically converted all Reactome pathways into GO-CAMs and are currently working on the conversion of additional resources available through Pathway Commons. By converting pathways into GO-CAMs, we can leverage OWL description logic reasoning over OBO ontologies to infer new biological relationships and detect logical inconsistencies. Further, the conversion helps to increase standardization for the representation of biological entities and processes. The products of this work can be used to improve source databases, for example by inferring new GO annotations for pathways and reactions and can help with the formation of meta-knowledge bases that integrate content from multiple sources.]]> Sat, 08 Dec 2018 00:48:27 GMT /goodb/integrating-pathway-databases-with-gene-ontology-causal-activity-models goodb@slideshare.net(goodb) Integrating Pathway Databases with Gene Ontology Causal Activity Models goodb The Gene Ontology (GO) Consortium (GOC) is developing a new knowledge representation approach called 鈥榗ausal activity models鈥� (GO-CAM). A GO-CAM describes how one or several gene products contribute to the execution of a biological process. In these models (implemented as OWL instance graphs anchored in Open Biological Ontology (OBO) classes and relations), gene products are linked to molecular activities via semantic relationships like 鈥榚nables鈥�, molecular activities are linked to each other via causal relationships such as 鈥榩ositively regulates鈥�, and sets of molecular activities are defined as 鈥榩arts鈥� of larger biological processes. This approach provides the GOC with a more complete and extensible structure for capturing knowledge of gene function. It also allows for the representation of knowledge typically seen in pathway databases. Here, we present details and results of a rule-based transformation of pathways represented using the BioPAX exchange format into GO-CAMs. We have automatically converted all Reactome pathways into GO-CAMs and are currently working on the conversion of additional resources available through Pathway Commons. By converting pathways into GO-CAMs, we can leverage OWL description logic reasoning over OBO ontologies to infer new biological relationships and detect logical inconsistencies. Further, the conversion helps to increase standardization for the representation of biological entities and processes. The products of this work can be used to improve source databases, for example by inferring new GO annotations for pathways and reactions and can help with the formation of meta-knowledge bases that integrate content from multiple sources. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/good-pathways2go-rockyaspen-2018-powerpoint-181208004827-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Gene Ontology (GO) Consortium (GOC) is developing a new knowledge representation approach called 鈥榗ausal activity models鈥� (GO-CAM). A GO-CAM describes how one or several gene products contribute to the execution of a biological process. In these models (implemented as OWL instance graphs anchored in Open Biological Ontology (OBO) classes and relations), gene products are linked to molecular activities via semantic relationships like 鈥榚nables鈥�, molecular activities are linked to each other via causal relationships such as 鈥榩ositively regulates鈥�, and sets of molecular activities are defined as 鈥榩arts鈥� of larger biological processes. This approach provides the GOC with a more complete and extensible structure for capturing knowledge of gene function. It also allows for the representation of knowledge typically seen in pathway databases. Here, we present details and results of a rule-based transformation of pathways represented using the BioPAX exchange format into GO-CAMs. We have automatically converted all Reactome pathways into GO-CAMs and are currently working on the conversion of additional resources available through Pathway Commons. By converting pathways into GO-CAMs, we can leverage OWL description logic reasoning over OBO ontologies to infer new biological relationships and detect logical inconsistencies. Further, the conversion helps to increase standardization for the representation of biological entities and processes. The products of this work can be used to improve source databases, for example by inferring new GO annotations for pathways and reactions and can help with the formation of meta-knowledge bases that integrate content from multiple sources.

Integrating Pathway Databases with Gene Ontology Causal Activity Models from Benjamin Good

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0 Pathways2GO: Converting BioPax pathways to GO-CAMs /slideshow/pathways2go-converting-biopax-pathways-to-gocams/98000547 pathways2go-gocnyu2018-180522042203
Presentation at the Gene Ontology Consortium Annual Meeting. Describing the automatic conversion of biochemical pathways in the Reactome Knowledge Base into the Gene Ontology 'Causal Activity Model' representation. ]]>
Presentation at the Gene Ontology Consortium Annual Meeting. Describing the automatic conversion of biochemical pathways in the Reactome Knowledge Base into the Gene Ontology 'Causal Activity Model' representation. ]]> Tue, 22 May 2018 04:22:02 GMT /slideshow/pathways2go-converting-biopax-pathways-to-gocams/98000547 goodb@slideshare.net(goodb) Pathways2GO: Converting BioPax pathways to GO-CAMs goodb Presentation at the Gene Ontology Consortium Annual Meeting. Describing the automatic conversion of biochemical pathways in the Reactome Knowledge Base into the Gene Ontology 'Causal Activity Model' representation. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pathways2go-gocnyu2018-180522042203-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation at the Gene Ontology Consortium Annual Meeting. Describing the automatic conversion of biochemical pathways in the Reactome Knowledge Base into the Gene Ontology 'Causal Activity Model' representation.

Pathways2GO: Converting BioPax pathways to GO-CAMs from Benjamin Good

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0 Knowledge Beacons /slideshow/knowledge-beacons/75514950 isvbcjaqsaegitg4hu04-signature-1c315c52f069974d47c22cba0d522b094d21eb9bd366cf13e7cb89bdbd1fe3ac-poli-170428225601
Riffing on the GA4GH Beacon concept as a path towards semantic web services for biomedical knowledge. ]]>
Riffing on the GA4GH Beacon concept as a path towards semantic web services for biomedical knowledge. ]]> Fri, 28 Apr 2017 22:56:01 GMT /slideshow/knowledge-beacons/75514950 goodb@slideshare.net(goodb) Knowledge Beacons goodb Riffing on the GA4GH Beacon concept as a path towards semantic web services for biomedical knowledge. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/isvbcjaqsaegitg4hu04-signature-1c315c52f069974d47c22cba0d522b094d21eb9bd366cf13e7cb89bdbd1fe3ac-poli-170428225601-thumbnail.jpg?width=120&height=120&fit=bounds" /> Riffing on the GA4GH Beacon concept as a path towards semantic web services for biomedical knowledge.

Knowledge Beacons from Benjamin Good

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0 Building a Biomedical Knowledge Garden /slideshow/building-a-biomedical-knowledge-garden/69777856 8cy9x6i2svctkokdozxb-signature-0013ace2833a9a83b26035006a6549480cf284ce33e4faa17ee6e18991236298-poli-161203001106
Describes the tribulations of building a large biomedical knowledge graph. Provides a comparison between the UMLS and Wikidata in terms of content and structure. Concludes with the idea of anchoring the knowledge graph in Wikidata items and properties. ]]>
Describes the tribulations of building a large biomedical knowledge graph. Provides a comparison between the UMLS and Wikidata in terms of content and structure. Concludes with the idea of anchoring the knowledge graph in Wikidata items and properties. ]]> Sat, 03 Dec 2016 00:11:06 GMT /slideshow/building-a-biomedical-knowledge-garden/69777856 goodb@slideshare.net(goodb) Building a Biomedical Knowledge Garden goodb Describes the tribulations of building a large biomedical knowledge graph. Provides a comparison between the UMLS and Wikidata in terms of content and structure. Concludes with the idea of anchoring the knowledge graph in Wikidata items and properties. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/8cy9x6i2svctkokdozxb-signature-0013ace2833a9a83b26035006a6549480cf284ce33e4faa17ee6e18991236298-poli-161203001106-thumbnail.jpg?width=120&height=120&fit=bounds" /> Describes the tribulations of building a large biomedical knowledge graph. Provides a comparison between the UMLS and Wikidata in terms of content and structure. Concludes with the idea of anchoring the knowledge graph in Wikidata items and properties.

Building a Biomedical Knowledge Garden from Benjamin Good

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0 Science Game Lab /slideshow/science-game-lab/69262152 9fwcfomwslmdjpbntuji-signature-a933cc3b912d9ffbb3ab4467ebe60cc891ac569a0531368727c06070be83451d-poli-161118170718
When the Heart BD2K grant was originally written. We proposed to build something called 鈥淏ig Data World鈥� to help advance citizen science, scientific crowdsourcing and science education 鈥� especially in bioinformatics. This past year, this idea has become Science Game Lab ( https://sciencegamelab.org ) . A collaboration between the Su laboratory at Scripps Research, Playmatics LLC, and recently the creators of WikiPathways. ]]>
When the Heart BD2K grant was originally written. We proposed to build something called 鈥淏ig Data World鈥� to help advance citizen science, scientific crowdsourcing and science education 鈥� especially in bioinformatics. This past year, this idea has become Science Game Lab ( https://sciencegamelab.org ) . A collaboration between the Su laboratory at Scripps Research, Playmatics LLC, and recently the creators of WikiPathways. ]]> Fri, 18 Nov 2016 17:07:18 GMT /slideshow/science-game-lab/69262152 goodb@slideshare.net(goodb) Science Game Lab goodb When the Heart BD2K grant was originally written. We proposed to build something called 鈥淏ig Data World鈥� to help advance citizen science, scientific crowdsourcing and science education 鈥� especially in bioinformatics. This past year, this idea has become Science Game Lab ( https://sciencegamelab.org ) . A collaboration between the Su laboratory at Scripps Research, Playmatics LLC, and recently the creators of WikiPathways. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/9fwcfomwslmdjpbntuji-signature-a933cc3b912d9ffbb3ab4467ebe60cc891ac569a0531368727c06070be83451d-poli-161118170718-thumbnail.jpg?width=120&height=120&fit=bounds" /> When the Heart BD2K grant was originally written. We proposed to build something called 鈥淏ig Data World鈥� to help advance citizen science, scientific crowdsourcing and science education 鈥� especially in bioinformatics. This past year, this idea has become Science Game Lab ( https://sciencegamelab.org ) . A collaboration between the Su laboratory at Scripps Research, Playmatics LLC, and recently the creators of WikiPathways.

Science Game Lab from Benjamin Good

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0 Wikidata and the Semantic Web of Food /slideshow/wikidata-and-the-semantic-web-of-food/68512410 w5d9durltfa6m3zmfdqe-signature-280947269b16f63a5ea3324e26b991309cdd8354303d8a62de82ccb706bad45f-poli-161109190121
A 10 minute introduction to Wikidata, the Gene Wiki project, and the Semantic Web. Presented at IC-Foods inaugural conference at UC Davis]]>
A 10 minute introduction to Wikidata, the Gene Wiki project, and the Semantic Web. Presented at IC-Foods inaugural conference at UC Davis]]> Wed, 09 Nov 2016 19:01:20 GMT /slideshow/wikidata-and-the-semantic-web-of-food/68512410 goodb@slideshare.net(goodb) Wikidata and the Semantic Web of Food goodb A 10 minute introduction to Wikidata, the Gene Wiki project, and the Semantic Web. Presented at IC-Foods inaugural conference at UC Davis <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/w5d9durltfa6m3zmfdqe-signature-280947269b16f63a5ea3324e26b991309cdd8354303d8a62de82ccb706bad45f-poli-161109190121-thumbnail.jpg?width=120&height=120&fit=bounds" /> A 10 minute introduction to Wikidata, the Gene Wiki project, and the Semantic Web. Presented at IC-Foods inaugural conference at UC Davis

Wikidata and the Semantic Web of Food from Benjamin Good

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0 Gene Wiki and Wikimedia Foundation SPARQL workshop /slideshow/gene-wiki-and-mediawiki-foundation-sparql-workshop/65835856 0yclehbxr62b22rc8r5q-signature-10bc0845f2943dd7378bd9ba7c9ce33897f38d17dd63d44c36dd29b81e79303b-poli-160908192118
Introduction to gene wiki project, the centralized model organism project and the use of SPARQL. ]]>
Introduction to gene wiki project, the centralized model organism project and the use of SPARQL. ]]> Thu, 08 Sep 2016 19:21:17 GMT /slideshow/gene-wiki-and-mediawiki-foundation-sparql-workshop/65835856 goodb@slideshare.net(goodb) Gene Wiki and Wikimedia Foundation SPARQL workshop goodb Introduction to gene wiki project, the centralized model organism project and the use of SPARQL. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/0yclehbxr62b22rc8r5q-signature-10bc0845f2943dd7378bd9ba7c9ce33897f38d17dd63d44c36dd29b81e79303b-poli-160908192118-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduction to gene wiki project, the centralized model organism project and the use of SPARQL.

Gene Wiki and Wikimedia Foundation SPARQL workshop from Benjamin Good

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0 Opportunities and challenges presented by Wikidata in the context of biocuration /slideshow/opportunities-and-challenges-presented-by-wikidata-in-the-context-of-biocuration/63827184 cecm9029ttcgrdicbucv-signature-0c8bc173267998f8f1d83d327f8a6b6012ae0acbe2bc704f99731aa7ae49a3fc-poli-160707220703
Abstract鈥擶ikidata is a world readable and writable knowledge base maintained by the Wikimedia Foundation. It offers the opportunity to collaboratively construct a fully open access knowledge graph spanning biology, medicine, and all other domains of knowledge. To meet this potential, social and technical challenges must be overcome - many of which are familiar to the biocuration community. These include community ontology building, high precision information extraction, provenance, and license management. By working together with Wikidata now, we can help shape it into a trustworthy, unencumbered central node in the Semantic Web of biomedical data. ]]>
Abstract鈥擶ikidata is a world readable and writable knowledge base maintained by the Wikimedia Foundation. It offers the opportunity to collaboratively construct a fully open access knowledge graph spanning biology, medicine, and all other domains of knowledge. To meet this potential, social and technical challenges must be overcome - many of which are familiar to the biocuration community. These include community ontology building, high precision information extraction, provenance, and license management. By working together with Wikidata now, we can help shape it into a trustworthy, unencumbered central node in the Semantic Web of biomedical data. ]]> Thu, 07 Jul 2016 22:07:03 GMT /slideshow/opportunities-and-challenges-presented-by-wikidata-in-the-context-of-biocuration/63827184 goodb@slideshare.net(goodb) Opportunities and challenges presented by Wikidata in the context of biocuration goodb Abstract鈥擶ikidata is a world readable and writable knowledge base maintained by the Wikimedia Foundation. It offers the opportunity to collaboratively construct a fully open access knowledge graph spanning biology, medicine, and all other domains of knowledge. To meet this potential, social and technical challenges must be overcome - many of which are familiar to the biocuration community. These include community ontology building, high precision information extraction, provenance, and license management. By working together with Wikidata now, we can help shape it into a trustworthy, unencumbered central node in the Semantic Web of biomedical data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cecm9029ttcgrdicbucv-signature-0c8bc173267998f8f1d83d327f8a6b6012ae0acbe2bc704f99731aa7ae49a3fc-poli-160707220703-thumbnail.jpg?width=120&height=120&fit=bounds" /> Abstract鈥擶ikidata is a world readable and writable knowledge base maintained by the Wikimedia Foundation. It offers the opportunity to collaboratively construct a fully open access knowledge graph spanning biology, medicine, and all other domains of knowledge. To meet this potential, social and technical challenges must be overcome - many of which are familiar to the biocuration community. These include community ontology building, high precision information extraction, provenance, and license management. By working together with Wikidata now, we can help shape it into a trustworthy, unencumbered central node in the Semantic Web of biomedical data.

Opportunities and challenges presented by Wikidata in the context of biocuration from Benjamin Good

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0 Scripps bioinformatics seminar_day_2 /slideshow/scripps-bioinformatics-seminarday2/62702658 mhermy4lsookdqi6dhdq-signature-3fb35ed23d46d5ce32e8b8ba9cbfc96149fb5daa809e457c90df6f535c27891f-poli-160603172726
Part 2 of introduction to knowledge representation and applications for knowledge discovery in bioinformatics]]>
Part 2 of introduction to knowledge representation and applications for knowledge discovery in bioinformatics]]> Fri, 03 Jun 2016 17:27:26 GMT /slideshow/scripps-bioinformatics-seminarday2/62702658 goodb@slideshare.net(goodb) Scripps bioinformatics seminar_day_2 goodb Part 2 of introduction to knowledge representation and applications for knowledge discovery in bioinformatics <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mhermy4lsookdqi6dhdq-signature-3fb35ed23d46d5ce32e8b8ba9cbfc96149fb5daa809e457c90df6f535c27891f-poli-160603172726-thumbnail.jpg?width=120&height=120&fit=bounds" /> Part 2 of introduction to knowledge representation and applications for knowledge discovery in bioinformatics

Scripps bioinformatics seminar_day_2 from Benjamin Good

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0 Computing on the shoulders of giants /slideshow/computing-on-the-shoulders-of-giants/62583111 t4ssedhlqjey3gubzrcg-signature-1ff9e63e36e9d05e4581ee903475b496d2ebc097dd4158b0330d47700381c0d2-poli-160531163257
Computing on the shoulders of giants: how existing knowledge is represented and applied in bioinformatics. A seminar for the TSRI graduate program.]]>
Computing on the shoulders of giants: how existing knowledge is represented and applied in bioinformatics. A seminar for the TSRI graduate program.]]> Tue, 31 May 2016 16:32:57 GMT /slideshow/computing-on-the-shoulders-of-giants/62583111 goodb@slideshare.net(goodb) Computing on the shoulders of giants goodb Computing on the shoulders of giants: 锟絟ow existing knowledge is represented and applied in bioinformatics. A seminar for the TSRI graduate program. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/t4ssedhlqjey3gubzrcg-signature-1ff9e63e36e9d05e4581ee903475b496d2ebc097dd4158b0330d47700381c0d2-poli-160531163257-thumbnail.jpg?width=120&height=120&fit=bounds" /> Computing on the shoulders of giants: 锟絟ow existing knowledge is represented and applied in bioinformatics. A seminar for the TSRI graduate program.

Computing on the shoulders of giants from Benjamin Good

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0 Wikidata workshop for ISB Biocuration 2016 /slideshow/wikidata-workshop-for-isb-biocuration-2016/60855984 mkyyez0fqmuhcvbf0m1k-signature-7d782bf979f2efdb134f6c2d4e788a99259314a540eb0e187e17b9e796c6bf92-poli-160413094148
Intro to wikidata for life scientists]]>
Intro to wikidata for life scientists]]> Wed, 13 Apr 2016 09:41:47 GMT /slideshow/wikidata-workshop-for-isb-biocuration-2016/60855984 goodb@slideshare.net(goodb) Wikidata workshop for ISB Biocuration 2016 goodb Intro to wikidata for life scientists <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mkyyez0fqmuhcvbf0m1k-signature-7d782bf979f2efdb134f6c2d4e788a99259314a540eb0e187e17b9e796c6bf92-poli-160413094148-thumbnail.jpg?width=120&height=120&fit=bounds" /> Intro to wikidata for life scientists

Wikidata workshop for ISB Biocuration 2016 from Benjamin Good

]]> 627 6 https://cdn.slidesharecdn.com/ss_thumbnails/mkyyez0fqmuhcvbf0m1k-signature-7d782bf979f2efdb134f6c2d4e788a99259314a540eb0e187e17b9e796c6bf92-poli-160413094148-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Channeling Collaborative Spirit /slideshow/channeling-collaborative-spirit/58598065 ys3aez3pqq6rpt4qmbzw-signature-5a2d51d1d6e88a2a3d8d93188a21c558d41002fdd53105eadc19460d4591214e-poli-160223094936
The Stone Soups of Programmers (hackathons) and Data (WikiData). Presented at Heart BD2K PI meeting, EBI, Feb. 22, 2016]]>
The Stone Soups of Programmers (hackathons) and Data (WikiData). Presented at Heart BD2K PI meeting, EBI, Feb. 22, 2016]]> Tue, 23 Feb 2016 09:49:36 GMT /slideshow/channeling-collaborative-spirit/58598065 goodb@slideshare.net(goodb) Channeling Collaborative Spirit goodb The Stone Soups of Programmers (hackathons) and Data (WikiData). Presented at Heart BD2K PI meeting, EBI, Feb. 22, 2016 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ys3aez3pqq6rpt4qmbzw-signature-5a2d51d1d6e88a2a3d8d93188a21c558d41002fdd53105eadc19460d4591214e-poli-160223094936-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Stone Soups of Programmers (hackathons) and Data (WikiData). Presented at Heart BD2K PI meeting, EBI, Feb. 22, 2016

Channeling Collaborative Spirit from Benjamin Good

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0 2016 bd2k bgood_wikidata /slideshow/2016-bd2k-bgoodwikidata/57931798 0uqqgyqxraqdhkkpbgms-signature-6d9f2590bf388d094e5be7e1df62a1e4fe119df1493cc1d56220453e1c4f8ee2-poli-160205174036
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]]> Fri, 05 Feb 2016 17:40:36 GMT /slideshow/2016-bd2k-bgoodwikidata/57931798 goodb@slideshare.net(goodb) 2016 bd2k bgood_wikidata goodb <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/0uqqgyqxraqdhkkpbgms-signature-6d9f2590bf388d094e5be7e1df62a1e4fe119df1493cc1d56220453e1c4f8ee2-poli-160205174036-thumbnail.jpg?width=120&height=120&fit=bounds" />

2016 bd2k bgood_wikidata from Benjamin Good

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0 2016 mem good /slideshow/2016-mem-good/57311566 ugdt1cbsr0itlor73ogz-signature-6023dda07b276d9dc10d2a401dccf8cc70e2c62a38e7245381f652a2bbe669fe-poli-160121065728
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]]> Thu, 21 Jan 2016 06:57:28 GMT /slideshow/2016-mem-good/57311566 goodb@slideshare.net(goodb) 2016 mem good goodb <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ugdt1cbsr0itlor73ogz-signature-6023dda07b276d9dc10d2a401dccf8cc70e2c62a38e7245381f652a2bbe669fe-poli-160121065728-thumbnail.jpg?width=120&height=120&fit=bounds" />

2016 mem good from Benjamin Good

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0 (Poster) Knowledge.Bio: an Interactive Tool for Literature-based Discovery /slideshow/poster-knowledgebio-an-interactive-tool-for-literaturebased-discovery/54792003 knobio1bd2k112015v2-151105181433-lva1-app6892
PubMed now indexes roughly 25 million articles and is growing by more than a million per year. The scale of this 鈥淏ig Knowledge鈥� repository renders traditional, article-based modes of user interaction unsatisfactory, demanding new interfaces for integrating and summarizing widely distributed knowledge. Natural language processing (NLP) techniques coupled with rich user interfaces can help meet this demand, providing end-users with enhanced views into public knowledge, stimulating their ability to form new hypotheses. Knowledge.Bio provides a Web interface for exploring the results from text-mining PubMed. It works with subject, predicate, object assertions (triples) extracted from individual abstracts and with predicted statistical associations between pairs of concepts. While agnostic to the NLP technology employed, the current implementation is loaded with triples from the SemRep-generated SemmedDB database and putative gene-disease pairs obtained using Leiden University Medical Center鈥檚鈥業mplicitome鈥� technology. Users of Knowledge.Bio begin by identifying a concept of interest using text search. Once a concept is identified, associated triples and concept-pairs are displayed in tables. These tables have text-based and semantic filters to help refine the list of triples to relations of interest. The user then selects relations for insertion into a personal knowledge graph implemented using cytoscape.js. The graph is used as a note-taking or 鈥榤ind-mapping鈥� structure that can be saved offline and then later reloaded into the application. Clicking on edges within a graph or on the 鈥榚vidence鈥� element of a triple displays the abstracts where that relation was detected, thus allowing the user to judge the veracity of the statement and to read the underlying articles. Knowledge.Bio is a free, open-source application that can provide, deep, personal, concise, shareable views into the 鈥淏ig Knowledge鈥� scattered across the biomedical literature. Application: http://knowledge.bio Source code: https://bitbucket.org/sulab/kb1/ ]]>
PubMed now indexes roughly 25 million articles and is growing by more than a million per year. The scale of this 鈥淏ig Knowledge鈥� repository renders traditional, article-based modes of user interaction unsatisfactory, demanding new interfaces for integrating and summarizing widely distributed knowledge. Natural language processing (NLP) techniques coupled with rich user interfaces can help meet this demand, providing end-users with enhanced views into public knowledge, stimulating their ability to form new hypotheses. Knowledge.Bio provides a Web interface for exploring the results from text-mining PubMed. It works with subject, predicate, object assertions (triples) extracted from individual abstracts and with predicted statistical associations between pairs of concepts. While agnostic to the NLP technology employed, the current implementation is loaded with triples from the SemRep-generated SemmedDB database and putative gene-disease pairs obtained using Leiden University Medical Center鈥檚鈥業mplicitome鈥� technology. Users of Knowledge.Bio begin by identifying a concept of interest using text search. Once a concept is identified, associated triples and concept-pairs are displayed in tables. These tables have text-based and semantic filters to help refine the list of triples to relations of interest. The user then selects relations for insertion into a personal knowledge graph implemented using cytoscape.js. The graph is used as a note-taking or 鈥榤ind-mapping鈥� structure that can be saved offline and then later reloaded into the application. Clicking on edges within a graph or on the 鈥榚vidence鈥� element of a triple displays the abstracts where that relation was detected, thus allowing the user to judge the veracity of the statement and to read the underlying articles. Knowledge.Bio is a free, open-source application that can provide, deep, personal, concise, shareable views into the 鈥淏ig Knowledge鈥� scattered across the biomedical literature. Application: http://knowledge.bio Source code: https://bitbucket.org/sulab/kb1/ ]]> Thu, 05 Nov 2015 18:14:32 GMT /slideshow/poster-knowledgebio-an-interactive-tool-for-literaturebased-discovery/54792003 goodb@slideshare.net(goodb) (Poster) Knowledge.Bio: an Interactive Tool for Literature-based Discovery goodb PubMed now indexes roughly 25 million articles and is growing by more than a million per year. The scale of this 鈥淏ig Knowledge鈥� repository renders traditional, article-based modes of user interaction unsatisfactory, demanding new interfaces for integrating and summarizing widely distributed knowledge. Natural language processing (NLP) techniques coupled with rich user interfaces can help meet this demand, providing end-users with enhanced views into public knowledge, stimulating their ability to form new hypotheses. Knowledge.Bio provides a Web interface for exploring the results from text-mining PubMed. It works with subject, predicate, object assertions (triples) extracted from individual abstracts and with predicted statistical associations between pairs of concepts. While agnostic to the NLP technology employed, the current implementation is loaded with triples from the SemRep-generated SemmedDB database and putative gene-disease pairs obtained using Leiden University Medical Center鈥檚鈥業mplicitome鈥� technology. Users of Knowledge.Bio begin by identifying a concept of interest using text search. Once a concept is identified, associated triples and concept-pairs are displayed in tables. These tables have text-based and semantic filters to help refine the list of triples to relations of interest. The user then selects relations for insertion into a personal knowledge graph implemented using cytoscape.js. The graph is used as a note-taking or 鈥榤ind-mapping鈥� structure that can be saved offline and then later reloaded into the application. Clicking on edges within a graph or on the 鈥榚vidence鈥� element of a triple displays the abstracts where that relation was detected, thus allowing the user to judge the veracity of the statement and to read the underlying articles. Knowledge.Bio is a free, open-source application that can provide, deep, personal, concise, shareable views into the 鈥淏ig Knowledge鈥� scattered across the biomedical literature. Application: http://knowledge.bio Source code: https://bitbucket.org/sulab/kb1/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/knobio1bd2k112015v2-151105181433-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> PubMed now indexes roughly 25 million articles and is growing by more than a million per year. The scale of this 鈥淏ig Knowledge鈥� repository renders traditional, article-based modes of user interaction unsatisfactory, demanding new interfaces for integrating and summarizing widely distributed knowledge. Natural language processing (NLP) techniques coupled with rich user interfaces can help meet this demand, providing end-users with enhanced views into public knowledge, stimulating their ability to form new hypotheses. Knowledge.Bio provides a Web interface for exploring the results from text-mining PubMed. It works with subject, predicate, object assertions (triples) extracted from individual abstracts and with predicted statistical associations between pairs of concepts. While agnostic to the NLP technology employed, the current implementation is loaded with triples from the SemRep-generated SemmedDB database and putative gene-disease pairs obtained using Leiden University Medical Center鈥檚鈥業mplicitome鈥� technology. Users of Knowledge.Bio begin by identifying a concept of interest using text search. Once a concept is identified, associated triples and concept-pairs are displayed in tables. These tables have text-based and semantic filters to help refine the list of triples to relations of interest. The user then selects relations for insertion into a personal knowledge graph implemented using cytoscape.js. The graph is used as a note-taking or 鈥榤ind-mapping鈥� structure that can be saved offline and then later reloaded into the application. Clicking on edges within a graph or on the 鈥榚vidence鈥� element of a triple displays the abstracts where that relation was detected, thus allowing the user to judge the veracity of the statement and to read the underlying articles. Knowledge.Bio is a free, open-source application that can provide, deep, personal, concise, shareable views into the 鈥淏ig Knowledge鈥� scattered across the biomedical literature. Application: http://knowledge.bio Source code: https://bitbucket.org/sulab/kb1/

(Poster) Knowledge.Bio: an Interactive Tool for Literature-based Discovery from Benjamin Good

]]> 906 8 https://cdn.slidesharecdn.com/ss_thumbnails/knobio1bd2k112015v2-151105181433-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Gene Wiki and Mark2Cure update for BD2K /slideshow/gene-wiki-and-mark2cure-update-for-bd2k/51223887 20150417bd2kgenewikimark2cure-150803153252-lva1-app6892
An introduction to the Gene Wiki project with an emphasis on the use of the new WikiData project. Also describes mark2cure, a citizen science initiative oriented on biomedical text mining. ]]>
An introduction to the Gene Wiki project with an emphasis on the use of the new WikiData project. Also describes mark2cure, a citizen science initiative oriented on biomedical text mining. ]]> Mon, 03 Aug 2015 15:32:52 GMT /slideshow/gene-wiki-and-mark2cure-update-for-bd2k/51223887 goodb@slideshare.net(goodb) Gene Wiki and Mark2Cure update for BD2K goodb An introduction to the Gene Wiki project with an emphasis on the use of the new WikiData project. Also describes mark2cure, a citizen science initiative oriented on biomedical text mining. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20150417bd2kgenewikimark2cure-150803153252-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> An introduction to the Gene Wiki project with an emphasis on the use of the new WikiData project. Also describes mark2cure, a citizen science initiative oriented on biomedical text mining.

Gene Wiki and Mark2Cure update for BD2K from Benjamin Good

]]> 676 6 https://cdn.slidesharecdn.com/ss_thumbnails/20150417bd2kgenewikimark2cure-150803153252-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 2015 6 bd2k_biobranch_knowbio /slideshow/2015-6-bd2kbiobranchknowbio/51143798 20156bd2kbiobranchknowbio-150731143408-lva1-app6891
Update on the gene wiki project, introduction to knowledge.bio semantic search application, introduction to biobranch.org collaborative decision tree creator]]>
Update on the gene wiki project, introduction to knowledge.bio semantic search application, introduction to biobranch.org collaborative decision tree creator]]> Fri, 31 Jul 2015 14:34:08 GMT /slideshow/2015-6-bd2kbiobranchknowbio/51143798 goodb@slideshare.net(goodb) 2015 6 bd2k_biobranch_knowbio goodb Update on the gene wiki project, introduction to knowledge.bio semantic search application, introduction to biobranch.org collaborative decision tree creator <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/20156bd2kbiobranchknowbio-150731143408-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Update on the gene wiki project, introduction to knowledge.bio semantic search application, introduction to biobranch.org collaborative decision tree creator

2015 6 bd2k_biobranch_knowbio from Benjamin Good

]]> 547 7 https://cdn.slidesharecdn.com/ss_thumbnails/20156bd2kbiobranchknowbio-150731143408-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 (Bio)Hackathons /slideshow/hackathon-bd2k2015-pdf/44584005 hackathonbd2k2015pdf-150212021603-conversion-gate01
A what, why and how description of hackathons for bioinformatics]]>
A what, why and how description of hackathons for bioinformatics]]> Thu, 12 Feb 2015 02:16:03 GMT /slideshow/hackathon-bd2k2015-pdf/44584005 goodb@slideshare.net(goodb) (Bio)Hackathons goodb A what, why and how description of hackathons for bioinformatics <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hackathonbd2k2015pdf-150212021603-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> A what, why and how description of hackathons for bioinformatics

(Bio)Hackathons from Benjamin Good

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0 Citizen sciencepanel2015 pdf /slideshow/citizen-sciencepanel2015-pdf/44582430 citizensciencepanel2015pdf-150212011224-conversion-gate01
Experiences and opportunities for citizen science in biomedical science. Talk given at #CitSci2015 citizen science conference panel.]]>
Experiences and opportunities for citizen science in biomedical science. Talk given at #CitSci2015 citizen science conference panel.]]> Thu, 12 Feb 2015 01:12:24 GMT /slideshow/citizen-sciencepanel2015-pdf/44582430 goodb@slideshare.net(goodb) Citizen sciencepanel2015 pdf goodb Experiences and opportunities for citizen science in biomedical science. Talk given at #CitSci2015 citizen science conference panel. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/citizensciencepanel2015pdf-150212011224-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Experiences and opportunities for citizen science in biomedical science. Talk given at #CitSci2015 citizen science conference panel.

Citizen sciencepanel2015 pdf from Benjamin Good

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0 https://cdn.slidesharecdn.com/profile-photo-goodb-48x48.jpg?cb=1727470129 I study and build systems that use the World Wide Web to advance biomedical research. Specialties: semantic web, ontology, OWL, machine learning, crowdsourcing, wikis, citizen science, natural language processing, knowledge bases i9606.blogspot.com https://cdn.slidesharecdn.com/ss_thumbnails/representingandreasoningwithbiologicalknowledge-us2ts-2019-190313004306-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/representing-and-reasoning-with-biological-knowledge/135979348 Representing and reaso... https://cdn.slidesharecdn.com/ss_thumbnails/good-pathways2go-rockyaspen-2018-powerpoint-181208004827-thumbnail.jpg?width=320&height=320&fit=bounds goodb/integrating-pathway-databases-with-gene-ontology-causal-activity-models Integrating Pathway Da... https://cdn.slidesharecdn.com/ss_thumbnails/pathways2go-gocnyu2018-180522042203-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/pathways2go-converting-biopax-pathways-to-gocams/98000547 Pathways2GO: Convertin...