�ݺ�ߣshows by User: thesilverhelix

�ݺ�ߣshows by User: thesilverhelix / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: thesilverhelix / Thu, 06 May 2021 03:02:11 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: thesilverhelix Elsevier's Healthcare Knowledge Graph: An Actionable Medical Knowledge Platform to Power Diverse Applications /thesilverhelix/elseviers-healthcare-knowledge-graph-an-actionable-medical-knowledge-platform-to-power-diverse-applications kgc-deck-main-210506030212
Invited Talk at Knowledge Graph Conference 2021. More information: https://www.knowledgegraph.tech/speakers/maulik-kamdar/]]>
Invited Talk at Knowledge Graph Conference 2021. More information: https://www.knowledgegraph.tech/speakers/maulik-kamdar/]]> Thu, 06 May 2021 03:02:11 GMT /thesilverhelix/elseviers-healthcare-knowledge-graph-an-actionable-medical-knowledge-platform-to-power-diverse-applications thesilverhelix@slideshare.net(thesilverhelix) Elsevier's Healthcare Knowledge Graph: An Actionable Medical Knowledge Platform to Power Diverse Applications thesilverhelix Invited Talk at Knowledge Graph Conference 2021. More information: https://www.knowledgegraph.tech/speakers/maulik-kamdar/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/kgc-deck-main-210506030212-thumbnail.jpg?width=120&height=120&fit=bounds" /> Invited Talk at Knowledge Graph Conference 2021. More information: https://www.knowledgegraph.tech/speakers/maulik-kamdar/

Elsevier's Healthcare Knowledge Graph: An Actionable Medical Knowledge Platform to Power Diverse Applications from Maulik Kamdar

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0 Text Snippets to Corroborate Medical Relations: An Unsupervised Approach using a Knowledge Graph and Embeddings /slideshow/text-snippets-to-corroborate-medical-relations-an-unsupervised-approach-using-a-knowledge-graph-and-embeddings/235058643 amia-maulik-200605131849
Knowledge graphs have been shown to significantly improve search results. Usually populated by subject matter experts, relations therein need to keep up to date with medical literature in order for search to remain relevant. Dynamically identifying text snippets in literature that confirm or deny knowledge graph triples is increasingly becoming the differentiator between trusted and untrusted medical decision support systems. This work describes our approach to mapping triples to medical text. A medical knowledge graph is used as a source of triples that are used to find matching sentences in reference text. Our unsupervised approach uses phrase embeddings and cosine similarity measures, and boosts candidate text snippets when certain key concepts exist. Using this approach, we can accurately map semantic relations within the medical knowledge graph to text snippets with a precision of 61.4% and recall of 86.3%. This method will be used to develop a novel application in the future to retrieve medical relations and corroborating snippets from medical text given a user query.]]>
Knowledge graphs have been shown to significantly improve search results. Usually populated by subject matter experts, relations therein need to keep up to date with medical literature in order for search to remain relevant. Dynamically identifying text snippets in literature that confirm or deny knowledge graph triples is increasingly becoming the differentiator between trusted and untrusted medical decision support systems. This work describes our approach to mapping triples to medical text. A medical knowledge graph is used as a source of triples that are used to find matching sentences in reference text. Our unsupervised approach uses phrase embeddings and cosine similarity measures, and boosts candidate text snippets when certain key concepts exist. Using this approach, we can accurately map semantic relations within the medical knowledge graph to text snippets with a precision of 61.4% and recall of 86.3%. This method will be used to develop a novel application in the future to retrieve medical relations and corroborating snippets from medical text given a user query.]]> Fri, 05 Jun 2020 13:18:49 GMT /slideshow/text-snippets-to-corroborate-medical-relations-an-unsupervised-approach-using-a-knowledge-graph-and-embeddings/235058643 thesilverhelix@slideshare.net(thesilverhelix) Text Snippets to Corroborate Medical Relations: An Unsupervised Approach using a Knowledge Graph and Embeddings thesilverhelix Knowledge graphs have been shown to significantly improve search results. Usually populated by subject matter experts, relations therein need to keep up to date with medical literature in order for search to remain relevant. Dynamically identifying text snippets in literature that confirm or deny knowledge graph triples is increasingly becoming the differentiator between trusted and untrusted medical decision support systems. This work describes our approach to mapping triples to medical text. A medical knowledge graph is used as a source of triples that are used to find matching sentences in reference text. Our unsupervised approach uses phrase embeddings and cosine similarity measures, and boosts candidate text snippets when certain key concepts exist. Using this approach, we can accurately map semantic relations within the medical knowledge graph to text snippets with a precision of 61.4% and recall of 86.3%. This method will be used to develop a novel application in the future to retrieve medical relations and corroborating snippets from medical text given a user query. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/amia-maulik-200605131849-thumbnail.jpg?width=120&height=120&fit=bounds" /> Knowledge graphs have been shown to significantly improve search results. Usually populated by subject matter experts, relations therein need to keep up to date with medical literature in order for search to remain relevant. Dynamically identifying text snippets in literature that confirm or deny knowledge graph triples is increasingly becoming the differentiator between trusted and untrusted medical decision support systems. This work describes our approach to mapping triples to medical text. A medical knowledge graph is used as a source of triples that are used to find matching sentences in reference text. Our unsupervised approach uses phrase embeddings and cosine similarity measures, and boosts candidate text snippets when certain key concepts exist. Using this approach, we can accurately map semantic relations within the medical knowledge graph to text snippets with a precision of 61.4% and recall of 86.3%. This method will be used to develop a novel application in the future to retrieve medical relations and corroborating snippets from medical text given a user query.

Text Snippets to Corroborate Medical Relations: An Unsupervised Approach using a Knowledge Graph and Embeddings from Maulik Kamdar

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0 Invited Talk at NASA Ames Research Center /slideshow/invited-talk-at-nasa-ames-research-center/110334867 nasatalkfinal-180817214729
This was an Invited Talk on my PhD research at the NASA Ames Research Center Intelligent Systems Division]]>
This was an Invited Talk on my PhD research at the NASA Ames Research Center Intelligent Systems Division]]> Fri, 17 Aug 2018 21:47:28 GMT /slideshow/invited-talk-at-nasa-ames-research-center/110334867 thesilverhelix@slideshare.net(thesilverhelix) Invited Talk at NASA Ames Research Center thesilverhelix This was an Invited Talk on my PhD research at the NASA Ames Research Center Intelligent Systems Division <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nasatalkfinal-180817214729-thumbnail.jpg?width=120&height=120&fit=bounds" /> This was an Invited Talk on my PhD research at the NASA Ames Research Center Intelligent Systems Division

Invited Talk at NASA Ames Research Center from Maulik Kamdar

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0 Mechanism-Based Pharmacovigilance Over the Life-Sciences Linked-Open-Data Cloud /slideshow/mechanismbased-pharmacovigilance-over-the-lifesciences-linkedopendata-cloud-82077360/82077360 amiamaulikfinaldrm-171114221134
Adverse drug reactions (ADR) result in significant morbidity and mortality in patients, and a substantial proportion of these ADRs are caused by drug--drug interactions (DDIs). Pharmacovigilance methods are used to detect unanticipated DDIs and ADRs by mining Spontaneous Reporting Systems, such as the US FDA Adverse Event Reporting System (FAERS). However, these methods do not provide mechanistic explanations for the discovered drug--ADR associations in a systematic manner. In this paper, we present a systems pharmacology-based approach to perform mechanism-based pharmacovigilance. We integrate data and knowledge from four different sources using Semantic Web Technologies and Linked Data principles to generate a systems network. We present a network-based Apriori algorithm for association mining in FAERS reports. We evaluate our method against existing pharmacovigilance methods for three different validation sets. Our method has AUROC statistics of 0.7--0.8, similar to current methods, and event-specific thresholds generate AUROC statistics greater than 0.75 for certain ADRs. Finally, we discuss the benefits of using Semantic Web technologies to attain the objectives for mechanism-based pharmacovigilance.]]>
Adverse drug reactions (ADR) result in significant morbidity and mortality in patients, and a substantial proportion of these ADRs are caused by drug--drug interactions (DDIs). Pharmacovigilance methods are used to detect unanticipated DDIs and ADRs by mining Spontaneous Reporting Systems, such as the US FDA Adverse Event Reporting System (FAERS). However, these methods do not provide mechanistic explanations for the discovered drug--ADR associations in a systematic manner. In this paper, we present a systems pharmacology-based approach to perform mechanism-based pharmacovigilance. We integrate data and knowledge from four different sources using Semantic Web Technologies and Linked Data principles to generate a systems network. We present a network-based Apriori algorithm for association mining in FAERS reports. We evaluate our method against existing pharmacovigilance methods for three different validation sets. Our method has AUROC statistics of 0.7--0.8, similar to current methods, and event-specific thresholds generate AUROC statistics greater than 0.75 for certain ADRs. Finally, we discuss the benefits of using Semantic Web technologies to attain the objectives for mechanism-based pharmacovigilance.]]> Tue, 14 Nov 2017 22:11:34 GMT /slideshow/mechanismbased-pharmacovigilance-over-the-lifesciences-linkedopendata-cloud-82077360/82077360 thesilverhelix@slideshare.net(thesilverhelix) Mechanism-Based Pharmacovigilance Over the Life-Sciences Linked-Open-Data Cloud thesilverhelix Adverse drug reactions (ADR) result in significant morbidity and mortality in patients, and a substantial proportion of these ADRs are caused by drug--drug interactions (DDIs). Pharmacovigilance methods are used to detect unanticipated DDIs and ADRs by mining Spontaneous Reporting Systems, such as the US FDA Adverse Event Reporting System (FAERS). However, these methods do not provide mechanistic explanations for the discovered drug--ADR associations in a systematic manner. In this paper, we present a systems pharmacology-based approach to perform mechanism-based pharmacovigilance. We integrate data and knowledge from four different sources using Semantic Web Technologies and Linked Data principles to generate a systems network. We present a network-based Apriori algorithm for association mining in FAERS reports. We evaluate our method against existing pharmacovigilance methods for three different validation sets. Our method has AUROC statistics of 0.7--0.8, similar to current methods, and event-specific thresholds generate AUROC statistics greater than 0.75 for certain ADRs. Finally, we discuss the benefits of using Semantic Web technologies to attain the objectives for mechanism-based pharmacovigilance. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/amiamaulikfinaldrm-171114221134-thumbnail.jpg?width=120&height=120&fit=bounds" /> Adverse drug reactions (ADR) result in significant morbidity and mortality in patients, and a substantial proportion of these ADRs are caused by drug--drug interactions (DDIs). Pharmacovigilance methods are used to detect unanticipated DDIs and ADRs by mining Spontaneous Reporting Systems, such as the US FDA Adverse Event Reporting System (FAERS). However, these methods do not provide mechanistic explanations for the discovered drug--ADR associations in a systematic manner. In this paper, we present a systems pharmacology-based approach to perform mechanism-based pharmacovigilance. We integrate data and knowledge from four different sources using Semantic Web Technologies and Linked Data principles to generate a systems network. We present a network-based Apriori algorithm for association mining in FAERS reports. We evaluate our method against existing pharmacovigilance methods for three different validation sets. Our method has AUROC statistics of 0.7--0.8, similar to current methods, and event-specific thresholds generate AUROC statistics greater than 0.75 for certain ADRs. Finally, we discuss the benefits of using Semantic Web technologies to attain the objectives for mechanism-based pharmacovigilance.

Mechanism-Based Pharmacovigilance Over the Life-Sciences Linked-Open-Data Cloud from Maulik Kamdar

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0 Analyzing User Interactions with Biomedical Ontologies: A Visual Perspective /slideshow/analyzing-user-interactions-with-biomedical-ontologies-a-visual-perspective/82077154 tuesdaytalkontoviz-171114220623
Biomedical ontologies are large: Several ontologies in the BioPortal repository contain thousands or even hundreds of thousands of entities. The development and maintenance of such large ontologies is difficult. To support ontology authors and repository developers in their work, it is crucial to improve our understanding of how these ontologies are explored, queried, reused, and used in downstream applications by biomedical researchers. We present an exploratory empirical analysis of user activities in the BioPortal ontology repository by analyzing BioPortal interaction logs across different access modes over several years. We investigate how users of BioPortal query and search for ontologies and their classes, how they explore the ontologies, and how they reuse classes from different ontologies. Additionally, through three real-world scenarios, we not only analyze the usage of ontologies for annotation tasks but also compare it to the browsing and querying behaviors of BioPortal users. For our investigation, we use several different visualization techniques. To inspect large amounts of interaction, reuse, and real-world usage data at a glance, we make use of and extend PolygOnto, a visualization method that has been successfully used to analyze reuse of ontologies in previous work. Our results show that exploration, query, reuse, and actual usage behaviors rarely align, suggesting that different users tend to explore, query and use different parts of an ontology. Finally, we highlight and discuss differences and commonalities among users of BioPortal. ]]>
Biomedical ontologies are large: Several ontologies in the BioPortal repository contain thousands or even hundreds of thousands of entities. The development and maintenance of such large ontologies is difficult. To support ontology authors and repository developers in their work, it is crucial to improve our understanding of how these ontologies are explored, queried, reused, and used in downstream applications by biomedical researchers. We present an exploratory empirical analysis of user activities in the BioPortal ontology repository by analyzing BioPortal interaction logs across different access modes over several years. We investigate how users of BioPortal query and search for ontologies and their classes, how they explore the ontologies, and how they reuse classes from different ontologies. Additionally, through three real-world scenarios, we not only analyze the usage of ontologies for annotation tasks but also compare it to the browsing and querying behaviors of BioPortal users. For our investigation, we use several different visualization techniques. To inspect large amounts of interaction, reuse, and real-world usage data at a glance, we make use of and extend PolygOnto, a visualization method that has been successfully used to analyze reuse of ontologies in previous work. Our results show that exploration, query, reuse, and actual usage behaviors rarely align, suggesting that different users tend to explore, query and use different parts of an ontology. Finally, we highlight and discuss differences and commonalities among users of BioPortal. ]]> Tue, 14 Nov 2017 22:06:23 GMT /slideshow/analyzing-user-interactions-with-biomedical-ontologies-a-visual-perspective/82077154 thesilverhelix@slideshare.net(thesilverhelix) Analyzing User Interactions with Biomedical Ontologies: A Visual Perspective thesilverhelix Biomedical ontologies are large: Several ontologies in the BioPortal repository contain thousands or even hundreds of thousands of entities. The development and maintenance of such large ontologies is difficult. To support ontology authors and repository developers in their work, it is crucial to improve our understanding of how these ontologies are explored, queried, reused, and used in downstream applications by biomedical researchers. We present an exploratory empirical analysis of user activities in the BioPortal ontology repository by analyzing BioPortal interaction logs across different access modes over several years. We investigate how users of BioPortal query and search for ontologies and their classes, how they explore the ontologies, and how they reuse classes from different ontologies. Additionally, through three real-world scenarios, we not only analyze the usage of ontologies for annotation tasks but also compare it to the browsing and querying behaviors of BioPortal users. For our investigation, we use several different visualization techniques. To inspect large amounts of interaction, reuse, and real-world usage data at a glance, we make use of and extend PolygOnto, a visualization method that has been successfully used to analyze reuse of ontologies in previous work. Our results show that exploration, query, reuse, and actual usage behaviors rarely align, suggesting that different users tend to explore, query and use different parts of an ontology. Finally, we highlight and discuss differences and commonalities among users of BioPortal. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tuesdaytalkontoviz-171114220623-thumbnail.jpg?width=120&height=120&fit=bounds" /> Biomedical ontologies are large: Several ontologies in the BioPortal repository contain thousands or even hundreds of thousands of entities. The development and maintenance of such large ontologies is difficult. To support ontology authors and repository developers in their work, it is crucial to improve our understanding of how these ontologies are explored, queried, reused, and used in downstream applications by biomedical researchers. We present an exploratory empirical analysis of user activities in the BioPortal ontology repository by analyzing BioPortal interaction logs across different access modes over several years. We investigate how users of BioPortal query and search for ontologies and their classes, how they explore the ontologies, and how they reuse classes from different ontologies. Additionally, through three real-world scenarios, we not only analyze the usage of ontologies for annotation tasks but also compare it to the browsing and querying behaviors of BioPortal users. For our investigation, we use several different visualization techniques. To inspect large amounts of interaction, reuse, and real-world usage data at a glance, we make use of and extend PolygOnto, a visualization method that has been successfully used to analyze reuse of ontologies in previous work. Our results show that exploration, query, reuse, and actual usage behaviors rarely align, suggesting that different users tend to explore, query and use different parts of an ontology. Finally, we highlight and discuss differences and commonalities among users of BioPortal.

Analyzing User Interactions with Biomedical Ontologies: A Visual Perspective from Maulik Kamdar

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0 BiOnIC: A Catalog of User Interactions with Biomedical Ontologies /slideshow/bionic-a-catalog-of-user-interactions-with-biomedical-ontologies/81432701 iswcrev24-171031180311
BiOnIC is a catalog of aggregated statistics of user clicks, queries, and reuse counts for access to over 200 biomedical ontologies. BiOnIC also provides anonymized sequences of classes accessed by users over a period of four years. To generate the statistics, we processed the access logs of BioPortal, a large open biomedical ontology repository. We publish the BiOnIC data using DCAT and SKOS metadata standards. The BiOnIC catalog has a wide range of applicability, which we demonstrate through its use in three different types of applications. To our knowledge, this type of interaction data stemming from a real-world, large-scale application has not been published before. We expect that the catalog will become an important resource for researchers and developers in the Semantic Web community by providing novel insights into how ontologies are explored, queried and reused. The BiOnIC catalog may ultimately assist in the more informed development of intelligent user interfaces for semantic resources through interface customization, prediction of user browsing and querying behavior, and ontology summarization. The BiOnIC catalog is available at: http://onto-apps.stanford.edu/bionic.]]>
BiOnIC is a catalog of aggregated statistics of user clicks, queries, and reuse counts for access to over 200 biomedical ontologies. BiOnIC also provides anonymized sequences of classes accessed by users over a period of four years. To generate the statistics, we processed the access logs of BioPortal, a large open biomedical ontology repository. We publish the BiOnIC data using DCAT and SKOS metadata standards. The BiOnIC catalog has a wide range of applicability, which we demonstrate through its use in three different types of applications. To our knowledge, this type of interaction data stemming from a real-world, large-scale application has not been published before. We expect that the catalog will become an important resource for researchers and developers in the Semantic Web community by providing novel insights into how ontologies are explored, queried and reused. The BiOnIC catalog may ultimately assist in the more informed development of intelligent user interfaces for semantic resources through interface customization, prediction of user browsing and querying behavior, and ontology summarization. The BiOnIC catalog is available at: http://onto-apps.stanford.edu/bionic.]]> Tue, 31 Oct 2017 18:03:11 GMT /slideshow/bionic-a-catalog-of-user-interactions-with-biomedical-ontologies/81432701 thesilverhelix@slideshare.net(thesilverhelix) BiOnIC: A Catalog of User Interactions with Biomedical Ontologies thesilverhelix BiOnIC is a catalog of aggregated statistics of user clicks, queries, and reuse counts for access to over 200 biomedical ontologies. BiOnIC also provides anonymized sequences of classes accessed by users over a period of four years. To generate the statistics, we processed the access logs of BioPortal, a large open biomedical ontology repository. We publish the BiOnIC data using DCAT and SKOS metadata standards. The BiOnIC catalog has a wide range of applicability, which we demonstrate through its use in three different types of applications. To our knowledge, this type of interaction data stemming from a real-world, large-scale application has not been published before. We expect that the catalog will become an important resource for researchers and developers in the Semantic Web community by providing novel insights into how ontologies are explored, queried and reused. The BiOnIC catalog may ultimately assist in the more informed development of intelligent user interfaces for semantic resources through interface customization, prediction of user browsing and querying behavior, and ontology summarization. The BiOnIC catalog is available at: http://onto-apps.stanford.edu/bionic. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iswcrev24-171031180311-thumbnail.jpg?width=120&height=120&fit=bounds" /> BiOnIC is a catalog of aggregated statistics of user clicks, queries, and reuse counts for access to over 200 biomedical ontologies. BiOnIC also provides anonymized sequences of classes accessed by users over a period of four years. To generate the statistics, we processed the access logs of BioPortal, a large open biomedical ontology repository. We publish the BiOnIC data using DCAT and SKOS metadata standards. The BiOnIC catalog has a wide range of applicability, which we demonstrate through its use in three different types of applications. To our knowledge, this type of interaction data stemming from a real-world, large-scale application has not been published before. We expect that the catalog will become an important resource for researchers and developers in the Semantic Web community by providing novel insights into how ontologies are explored, queried and reused. The BiOnIC catalog may ultimately assist in the more informed development of intelligent user interfaces for semantic resources through interface customization, prediction of user browsing and querying behavior, and ontology summarization. The BiOnIC catalog is available at: http://onto-apps.stanford.edu/bionic.

BiOnIC: A Catalog of User Interactions with Biomedical Ontologies from Maulik Kamdar

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0 Preproposal Talk /slideshow/preproposal-talk/79426225 preproposalfinal-170904213908
Query Federation over Biomedical Linked Open Data]]>
Query Federation over Biomedical Linked Open Data]]> Mon, 04 Sep 2017 21:39:08 GMT /slideshow/preproposal-talk/79426225 thesilverhelix@slideshare.net(thesilverhelix) Preproposal Talk thesilverhelix Query Federation over Biomedical Linked Open Data <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/preproposalfinal-170904213908-thumbnail.jpg?width=120&height=120&fit=bounds" /> Query Federation over Biomedical Linked Open Data

Preproposal Talk from Maulik Kamdar

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0 Graph Analytics in Pharmacology over the Web of Life Sciences Linked Open Data /slideshow/graph-analytics-in-pharmacology-over-the-web-of-life-sciences-linked-open-data/75654192 wwwfinaltouse-170503213143
�ݺ�ߣs from my talk presented at the 26th World Wide Web Conference (WWW 2017), held at Perth from April 3-8, 2017. ]]>
�ݺ�ߣs from my talk presented at the 26th World Wide Web Conference (WWW 2017), held at Perth from April 3-8, 2017. ]]> Wed, 03 May 2017 21:31:43 GMT /slideshow/graph-analytics-in-pharmacology-over-the-web-of-life-sciences-linked-open-data/75654192 thesilverhelix@slideshare.net(thesilverhelix) Graph Analytics in Pharmacology over the Web of Life Sciences Linked Open Data thesilverhelix �ݺ�ߣs from my talk presented at the 26th World Wide Web Conference (WWW 2017), held at Perth from April 3-8, 2017. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/wwwfinaltouse-170503213143-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs from my talk presented at the 26th World Wide Web Conference (WWW 2017), held at Perth from April 3-8, 2017.

Graph Analytics in Pharmacology over the Web of Life Sciences Linked Open Data from Maulik Kamdar

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0 BMI Research in Progress - Thursday talk /thesilverhelix/bmi-research-in-progress-thursday-talk thursday-talk-161121210826
My slides on "Query Federation over the Life Sciences Linked Open Data Cloud: Methods, Challenges and Applications"]]>
My slides on "Query Federation over the Life Sciences Linked Open Data Cloud: Methods, Challenges and Applications"]]> Mon, 21 Nov 2016 21:08:26 GMT /thesilverhelix/bmi-research-in-progress-thursday-talk thesilverhelix@slideshare.net(thesilverhelix) BMI Research in Progress - Thursday talk thesilverhelix My slides on "Query Federation over the Life Sciences Linked Open Data Cloud: Methods, Challenges and Applications" <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thursday-talk-161121210826-thumbnail.jpg?width=120&height=120&fit=bounds" /> My slides on "Query Federation over the Life Sciences Linked Open Data Cloud: Methods, Challenges and Applications"

BMI Research in Progress - Thursday talk from Maulik Kamdar

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0 PRISM: A data-driven platform for monitoring mental health /slideshow/prism-a-datadriven-platform-for-monitoring-mental-health/56759765 psb2016-160106221223
Talk presented at the 21st Pacific Symposium on Biocomputing Conference, Big Island of Hawaii, January 2016. Video Screencast: https://www.youtube.com/watch?v=IPVmu0bCmMU]]>
Talk presented at the 21st Pacific Symposium on Biocomputing Conference, Big Island of Hawaii, January 2016. Video Screencast: https://www.youtube.com/watch?v=IPVmu0bCmMU]]> Wed, 06 Jan 2016 22:12:23 GMT /slideshow/prism-a-datadriven-platform-for-monitoring-mental-health/56759765 thesilverhelix@slideshare.net(thesilverhelix) PRISM: A data-driven platform for monitoring mental health thesilverhelix Talk presented at the 21st Pacific Symposium on Biocomputing Conference, Big Island of Hawaii, January 2016. Video Screencast: https://www.youtube.com/watch?v=IPVmu0bCmMU <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/psb2016-160106221223-thumbnail.jpg?width=120&height=120&fit=bounds" /> Talk presented at the 21st Pacific Symposium on Biocomputing Conference, Big Island of Hawaii, January 2016. Video Screencast: https://www.youtube.com/watch?v=IPVmu0bCmMU

PRISM: A data-driven platform for monitoring mental health from Maulik Kamdar

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0 Investigating Term Reuse and Overlap in Biomedical Ontologies /slideshow/investigating-term-reuse-and-overlap-in-biomedical-ontologies/51555035 icbo2015presentationweb-150812174940-lva1-app6892
Our conference presentation at the 6th International Conference on Biomedical Ontology (ICBO), held at Lisbon, Portugal, during 27th-30th July 2015. Conference Proceedings: http://icbo2015.fc.ul.pt/ICBO2015Proceedings.pdf]]>
Our conference presentation at the 6th International Conference on Biomedical Ontology (ICBO), held at Lisbon, Portugal, during 27th-30th July 2015. Conference Proceedings: http://icbo2015.fc.ul.pt/ICBO2015Proceedings.pdf]]> Wed, 12 Aug 2015 17:49:40 GMT /slideshow/investigating-term-reuse-and-overlap-in-biomedical-ontologies/51555035 thesilverhelix@slideshare.net(thesilverhelix) Investigating Term Reuse and Overlap in Biomedical Ontologies thesilverhelix Our conference presentation at the 6th International Conference on Biomedical Ontology (ICBO), held at Lisbon, Portugal, during 27th-30th July 2015. Conference Proceedings: http://icbo2015.fc.ul.pt/ICBO2015Proceedings.pdf <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/icbo2015presentationweb-150812174940-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Our conference presentation at the 6th International Conference on Biomedical Ontology (ICBO), held at Lisbon, Portugal, during 27th-30th July 2015. Conference Proceedings: http://icbo2015.fc.ul.pt/ICBO2015Proceedings.pdf

Investigating Term Reuse and Overlap in Biomedical Ontologies from Maulik Kamdar

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0 Integrating Wearables and User Interaction Patterns to Monitor Mental Health /slideshow/integrating-wearables-and-user-interaction-patterns-to/48959056 finalpresentation-150603193647-lva1-app6892
Neuropsychiatric disorders are the leading cause of disability worldwide and there is no gold standard currently available for the measurement of mental health. This issue is exacerbated by the fact that the information physicians use to diagnose these disorders is episodic and often subjective. Current methods to monitor mental health involve the use of subject DSM-5 guidelines, invasive EEG measurements or qualitative video monitoring. Wearable technologies have surfaced as a ubiquitous and unobtrusive method for providing continuous, quantitative data about a patient. We have developed a framework for integrating motion, light and heart rate data from a smart watch application with user interactions and patient insights from a web application. We have demonstrated a proof of concept by collecting preliminary data through a pilot study of X subjects. We have engineered appropriate features and applied both unsupervised and supervised learning to show that the data has the potential to be useful for evaluating mental health. This framework will allow us to leverage continuous streams of passive data for early and accurate diagnosis as well as constant monitoring of patients suffering from mental disorders.]]>
Neuropsychiatric disorders are the leading cause of disability worldwide and there is no gold standard currently available for the measurement of mental health. This issue is exacerbated by the fact that the information physicians use to diagnose these disorders is episodic and often subjective. Current methods to monitor mental health involve the use of subject DSM-5 guidelines, invasive EEG measurements or qualitative video monitoring. Wearable technologies have surfaced as a ubiquitous and unobtrusive method for providing continuous, quantitative data about a patient. We have developed a framework for integrating motion, light and heart rate data from a smart watch application with user interactions and patient insights from a web application. We have demonstrated a proof of concept by collecting preliminary data through a pilot study of X subjects. We have engineered appropriate features and applied both unsupervised and supervised learning to show that the data has the potential to be useful for evaluating mental health. This framework will allow us to leverage continuous streams of passive data for early and accurate diagnosis as well as constant monitoring of patients suffering from mental disorders.]]> Wed, 03 Jun 2015 19:36:47 GMT /slideshow/integrating-wearables-and-user-interaction-patterns-to/48959056 thesilverhelix@slideshare.net(thesilverhelix) Integrating Wearables and User Interaction Patterns to Monitor Mental Health thesilverhelix Neuropsychiatric disorders are the leading cause of disability worldwide and there is no gold standard currently available for the measurement of mental health. This issue is exacerbated by the fact that the information physicians use to diagnose these disorders is episodic and often subjective. Current methods to monitor mental health involve the use of subject DSM-5 guidelines, invasive EEG measurements or qualitative video monitoring. Wearable technologies have surfaced as a ubiquitous and unobtrusive method for providing continuous, quantitative data about a patient. We have developed a framework for integrating motion, light and heart rate data from a smart watch application with user interactions and patient insights from a web application. We have demonstrated a proof of concept by collecting preliminary data through a pilot study of X subjects. We have engineered appropriate features and applied both unsupervised and supervised learning to show that the data has the potential to be useful for evaluating mental health. This framework will allow us to leverage continuous streams of passive data for early and accurate diagnosis as well as constant monitoring of patients suffering from mental disorders. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/finalpresentation-150603193647-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Neuropsychiatric disorders are the leading cause of disability worldwide and there is no gold standard currently available for the measurement of mental health. This issue is exacerbated by the fact that the information physicians use to diagnose these disorders is episodic and often subjective. Current methods to monitor mental health involve the use of subject DSM-5 guidelines, invasive EEG measurements or qualitative video monitoring. Wearable technologies have surfaced as a ubiquitous and unobtrusive method for providing continuous, quantitative data about a patient. We have developed a framework for integrating motion, light and heart rate data from a smart watch application with user interactions and patient insights from a web application. We have demonstrated a proof of concept by collecting preliminary data through a pilot study of X subjects. We have engineered appropriate features and applied both unsupervised and supervised learning to show that the data has the potential to be useful for evaluating mental health. This framework will allow us to leverage continuous streams of passive data for early and accurate diagnosis as well as constant monitoring of patients suffering from mental disorders.

Integrating Wearables and User Interaction Patterns to Monitor Mental Health from Maulik Kamdar

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0 Current advances to bridge the usability-expressivity gap in biomedical semantic search (and visualizing linked data) /slideshow/no-anim-protege-meeting-presentation-2015/46873160 noanimprotegemeetingpresentation2015-150410191304-conversion-gate01
I presented a talk at the Protege research meeting on the 'Current advances to bridge the usability-expressivity gap in biomedical semantic search (and visualizing linked data)' https://sites.google.com/site/protegeresearchmeeting/meeting-materials/current-advances-to-bridge-the-usability-expressivity-gap-in-semantic-search]]>
I presented a talk at the Protege research meeting on the 'Current advances to bridge the usability-expressivity gap in biomedical semantic search (and visualizing linked data)' https://sites.google.com/site/protegeresearchmeeting/meeting-materials/current-advances-to-bridge-the-usability-expressivity-gap-in-semantic-search]]> Fri, 10 Apr 2015 19:13:04 GMT /slideshow/no-anim-protege-meeting-presentation-2015/46873160 thesilverhelix@slideshare.net(thesilverhelix) Current advances to bridge the usability-expressivity gap in biomedical semantic search (and visualizing linked data) thesilverhelix I presented a talk at the Protege research meeting on the 'Current advances to bridge the usability-expressivity gap in biomedical semantic search (and visualizing linked data)' https://sites.google.com/site/protegeresearchmeeting/meeting-materials/current-advances-to-bridge-the-usability-expressivity-gap-in-semantic-search <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/noanimprotegemeetingpresentation2015-150410191304-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> I presented a talk at the Protege research meeting on the 'Current advances to bridge the usability-expressivity gap in biomedical semantic search (and visualizing linked data)' https://sites.google.com/site/protegeresearchmeeting/meeting-materials/current-advances-to-bridge-the-usability-expressivity-gap-in-semantic-search

Current advances to bridge the usability-expressivity gap in biomedical semantic search (and visualizing linked data) from Maulik Kamdar

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0 BMI 201 - Investigating Term Reuse and Overlap in Biomedical Ontologies /thesilverhelix/bmi-201-investigating-term-reuse-and-overlap-in-biomedical-ontologies noanimbmi201presentation2015-150408123821-conversion-gate01
My talk on 'Investigating Term Reuse and Overlap in Biomedical Ontologies' at the BMI 201 Tuesday Seminar.]]>
My talk on 'Investigating Term Reuse and Overlap in Biomedical Ontologies' at the BMI 201 Tuesday Seminar.]]> Wed, 08 Apr 2015 12:38:21 GMT /thesilverhelix/bmi-201-investigating-term-reuse-and-overlap-in-biomedical-ontologies thesilverhelix@slideshare.net(thesilverhelix) BMI 201 - Investigating Term Reuse and Overlap in Biomedical Ontologies thesilverhelix My talk on 'Investigating Term Reuse and Overlap in Biomedical Ontologies' at the BMI 201 Tuesday Seminar. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/noanimbmi201presentation2015-150408123821-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> My talk on 'Investigating Term Reuse and Overlap in Biomedical Ontologies' at the BMI 201 Tuesday Seminar.

BMI 201 - Investigating Term Reuse and Overlap in Biomedical Ontologies from Maulik Kamdar

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0 GenomeSnip: Fragmenting the Genomic Wheel to augment discovery in cancer research /slideshow/genomesnip-fragmenting-the-genomic-wheel-to-augment-discovery-in-cancer-research/31498168 cshalsupdated-140221174316-phpapp01
Presentation in the Conference on Semantics in Healthcare and Life Sciences (CSHALS) 2014, Boston. Abstract: Cancer genomics researchers have greatly benefited from high-throughput technologies for the characterization of genomic alterations in patients. These voluminous genomics datasets when supplemented with the appropriate computational tools have led towards the identification of 'oncogenes' and cancer pathways. However, if a researcher wishes to exploit the datasets in conjunction with this extracted knowledge his cognitive abilities need to be augmented through advanced visualizations. In this paper, we present GenomeSnip, a visual analytics platform, which facilitates the intuitive exploration of the human genome and displays the relationships between different genomic features. Knowledge, pertaining to the hierarchical categorization of the human genome, oncogenes and abstract, co-occurring relations, has been retrieved from multiple data sources and transformed a priori. We demonstrate how cancer experts could use this platform to interactively isolate genes or relations of interest and perform a comparative analysis on the 20.4 billion triples Linked Cancer Genome Atlas (TCGA) datasets.]]>
Presentation in the Conference on Semantics in Healthcare and Life Sciences (CSHALS) 2014, Boston. Abstract: Cancer genomics researchers have greatly benefited from high-throughput technologies for the characterization of genomic alterations in patients. These voluminous genomics datasets when supplemented with the appropriate computational tools have led towards the identification of 'oncogenes' and cancer pathways. However, if a researcher wishes to exploit the datasets in conjunction with this extracted knowledge his cognitive abilities need to be augmented through advanced visualizations. In this paper, we present GenomeSnip, a visual analytics platform, which facilitates the intuitive exploration of the human genome and displays the relationships between different genomic features. Knowledge, pertaining to the hierarchical categorization of the human genome, oncogenes and abstract, co-occurring relations, has been retrieved from multiple data sources and transformed a priori. We demonstrate how cancer experts could use this platform to interactively isolate genes or relations of interest and perform a comparative analysis on the 20.4 billion triples Linked Cancer Genome Atlas (TCGA) datasets.]]> Fri, 21 Feb 2014 17:43:16 GMT /slideshow/genomesnip-fragmenting-the-genomic-wheel-to-augment-discovery-in-cancer-research/31498168 thesilverhelix@slideshare.net(thesilverhelix) GenomeSnip: Fragmenting the Genomic Wheel to augment discovery in cancer research thesilverhelix Presentation in the Conference on Semantics in Healthcare and Life Sciences (CSHALS) 2014, Boston. Abstract: Cancer genomics researchers have greatly benefited from high-throughput technologies for the characterization of genomic alterations in patients. These voluminous genomics datasets when supplemented with the appropriate computational tools have led towards the identification of 'oncogenes' and cancer pathways. However, if a researcher wishes to exploit the datasets in conjunction with this extracted knowledge his cognitive abilities need to be augmented through advanced visualizations. In this paper, we present GenomeSnip, a visual analytics platform, which facilitates the intuitive exploration of the human genome and displays the relationships between different genomic features. Knowledge, pertaining to the hierarchical categorization of the human genome, oncogenes and abstract, co-occurring relations, has been retrieved from multiple data sources and transformed a priori. We demonstrate how cancer experts could use this platform to interactively isolate genes or relations of interest and perform a comparative analysis on the 20.4 billion triples Linked Cancer Genome Atlas (TCGA) datasets. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cshalsupdated-140221174316-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation in the Conference on Semantics in Healthcare and Life Sciences (CSHALS) 2014, Boston. Abstract: Cancer genomics researchers have greatly benefited from high-throughput technologies for the characterization of genomic alterations in patients. These voluminous genomics datasets when supplemented with the appropriate computational tools have led towards the identification of 'oncogenes' and cancer pathways. However, if a researcher wishes to exploit the datasets in conjunction with this extracted knowledge his cognitive abilities need to be augmented through advanced visualizations. In this paper, we present GenomeSnip, a visual analytics platform, which facilitates the intuitive exploration of the human genome and displays the relationships between different genomic features. Knowledge, pertaining to the hierarchical categorization of the human genome, oncogenes and abstract, co-occurring relations, has been retrieved from multiple data sources and transformed a priori. We demonstrate how cancer experts could use this platform to interactively isolate genes or relations of interest and perform a comparative analysis on the 20.4 billion triples Linked Cancer Genome Atlas (TCGA) datasets.

GenomeSnip: Fragmenting the Genomic Wheel to augment discovery in cancer research from Maulik Kamdar

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0 Isolation and characterization of an extracellular antifungal protein from an endophytic fungal isolate /slideshow/isolation-of-an-extracellular-antifungal-protein-from-an-endophytic-fungal-isolate/31497257 btpthesispresentation-140221165729-phpapp01
Protein and Peptide Letters Published Article in Vol 20, Issue 2, February 2013 Abstract available here : http://www.ncbi.nlm.nih.gov/pubmed/22894154 ]]>
Protein and Peptide Letters Published Article in Vol 20, Issue 2, February 2013 Abstract available here : http://www.ncbi.nlm.nih.gov/pubmed/22894154 ]]> Fri, 21 Feb 2014 16:57:29 GMT /slideshow/isolation-of-an-extracellular-antifungal-protein-from-an-endophytic-fungal-isolate/31497257 thesilverhelix@slideshare.net(thesilverhelix) Isolation and characterization of an extracellular antifungal protein from an endophytic fungal isolate thesilverhelix Protein and Peptide Letters Published Article in Vol 20, Issue 2, February 2013 Abstract available here : http://www.ncbi.nlm.nih.gov/pubmed/22894154 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/btpthesispresentation-140221165729-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Protein and Peptide Letters Published Article in Vol 20, Issue 2, February 2013 Abstract available here : http://www.ncbi.nlm.nih.gov/pubmed/22894154

Isolation and characterization of an extracellular antifungal protein from an endophytic fungal isolate from Maulik Kamdar

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0 ReVeaLD: A user-driven domain-specific interactive search platform for biomedical research /slideshow/reveald-poster/31188110 revealdposter-140213171457-phpapp01
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]]> Thu, 13 Feb 2014 17:14:57 GMT /slideshow/reveald-poster/31188110 thesilverhelix@slideshare.net(thesilverhelix) ReVeaLD: A user-driven domain-specific interactive search platform for biomedical research thesilverhelix <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/revealdposter-140213171457-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

ReVeaLD: A user-driven domain-specific interactive search platform for biomedical research from Maulik Kamdar

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0 ReVeaLD: A User-driven Domain Specific Interactive Search Platform for Biomedical Research /slideshow/reveald-a-userdriven-domain-specific-interactive-search-platform-for-biomedical-research/18353810 deripresentation-130407081725-phpapp01
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]]> Sun, 07 Apr 2013 08:17:25 GMT /slideshow/reveald-a-userdriven-domain-specific-interactive-search-platform-for-biomedical-research/18353810 thesilverhelix@slideshare.net(thesilverhelix) ReVeaLD: A User-driven Domain Specific Interactive Search Platform for Biomedical Research thesilverhelix <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/deripresentation-130407081725-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

ReVeaLD: A User-driven Domain Specific Interactive Search Platform for Biomedical Research from Maulik Kamdar

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0 https://cdn.slidesharecdn.com/profile-photo-thesilverhelix-48x48.jpg?cb=1662570685 I currently work as a Senior Data Scientist and Software Engineer at Elsevier Health Markets. I completed my PhD research in Biomedical Informatics at Stanford University under Dr. Mark Musen on developing methods to retrieve, integrate, and analyze data and knowledge from multiple, heterogeneous biomedical sources for discovering novel associations in pharmacovigilance. In the past, I have worked as a Linked Data Researcher at the Digital Enterprise Research Institute (DERI), NUI Galway (2012-2014), and also collaborated with the Reactome Project under Google Summer of Code Program (2011-12). www.maulik-kamdar.com https://cdn.slidesharecdn.com/ss_thumbnails/kgc-deck-main-210506030212-thumbnail.jpg?width=320&height=320&fit=bounds thesilverhelix/elseviers-healthcare-knowledge-graph-an-actionable-medical-knowledge-platform-to-power-diverse-applications Elsevier's Healthcare ... https://cdn.slidesharecdn.com/ss_thumbnails/amia-maulik-200605131849-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/text-snippets-to-corroborate-medical-relations-an-unsupervised-approach-using-a-knowledge-graph-and-embeddings/235058643 Text Snippets to Corro... https://cdn.slidesharecdn.com/ss_thumbnails/nasatalkfinal-180817214729-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/invited-talk-at-nasa-ames-research-center/110334867 Invited Talk at NASA A...