Test slide for the lab - Target prioritization Maori Ito
This document discusses candidate gene prioritization for large-scale experiments. It notes that there are challenges in combining available information from different data sources due to lack of annotations for individual genes. The process involves taking many candidate genes as input and applying integrated knowledge discovery to output a smaller number of selected target genes.
This presentation discusses ways to improve biomedical database search services through the use of semantic web technologies like schema.org and metadata tagging. It explains how marking up database entries with metadata and declaring vocabularies allows search engines to better understand and return more informative results for biomedical queries. The presentation provides examples of how Sagace and other search collaboration organizations are already reflecting semantic metadata in their results.
Test slide for the lab - Target prioritization Maori Ito
This document discusses candidate gene prioritization for large-scale experiments. It notes that there are challenges in combining available information from different data sources due to lack of annotations for individual genes. The process involves taking many candidate genes as input and applying integrated knowledge discovery to output a smaller number of selected target genes.
This presentation discusses ways to improve biomedical database search services through the use of semantic web technologies like schema.org and metadata tagging. It explains how marking up database entries with metadata and declaring vocabularies allows search engines to better understand and return more informative results for biomedical queries. The presentation provides examples of how Sagace and other search collaboration organizations are already reflecting semantic metadata in their results.
Life Science Database Cross Search and MetadataMaori Ito
Life science databases are sometimes difficult to understand due to lack of information. I'd like to add metadata into databases and improve search results.
7. Ten simple rules for publishing
Linked Data for the Life Sciences
• https://github.com/dbcls/bh14/wiki/Ten-simple-rules-for-publishing-
Linked-Data-for-the-Life-Sciences
• 抜粋
– はじめる前にユースケースを作ろう
– Schemaを出そう
– SPARQL endpointで利用可能なデータにしよう
– 言葉で検索出来るようなクエリを作ろう
– ライセンスを明示しよう
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8. 関連の有りそうなTopics
• Node.js framework to query the EBI RDF
platform
– javascriptを使用してEBIで提供しているRDFの
Schemaをもっと見やすくしようという取り組み
• PDB2Uniprot
– Combining SIFTS with wwPDB/RDF using FALDO.
– https://github.com/dbcls/bh14/wiki/PDB2UniProt
• A chemical substance database, Nikkaji
– InChIKeyを通じて国内外の化合物データをつなげよ
うという取り組み
– https://drive.google.com/file/d/0B6E9_nujMg6QUXc3
V1lhdFotX3M/view
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