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Our pathway graph query algebra interprets relationship queries given by selected examples to find a match for, extract identical parts between, or trace a path from any pathway components. To support the relationship query, users can composite pathway visualizations through visual proximity rules that use proximity to infer users\u2019 intentions in the exploration process. By allowing selection of one or more objects from multiple on-screen grouped graphs as query inputs and using the query outputs as next-query inputs, pathway graph query algebra and visual proximity rules achieve intuitiveness, concurrence, and dynamics for pathway exploration.<\/jats:p>","DOI":"10.1177\/1473871616666394","type":"journal-article","created":{"date-parts":[[2016,9,12]],"date-time":"2016-09-12T21:39:24Z","timestamp":1473716364000},"page":"217-231","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Graph query algebra and visual proximity rules for biological pathway exploration"],"prefix":"10.1177","volume":"16","author":[{"given":"Keqin","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, 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