{"id":37658,"date":"2020-12-04T16:35:58","date_gmt":"2020-12-04T21:35:58","guid":{"rendered":"https:\/\/web.uri.edu\/rinsfepscor\/?p=37658"},"modified":"2020-12-10T13:41:21","modified_gmt":"2020-12-10T18:41:21","slug":"analysis-of-metagenomic-data-collected-in-the-narragansett-bay-ri-using-machine-learning-approaches","status":"publish","type":"post","link":"https:\/\/web.uri.edu\/rinsfepscor\/2020\/12\/04\/analysis-of-metagenomic-data-collected-in-the-narragansett-bay-ri-using-machine-learning-approaches\/","title":{"rendered":"IO-3: Analysis of metagenomic data collected in the Narragansett Bay using machine learning approaches"},"content":{"rendered":"

Cecile Cres<\/strong>, University of Rhode Island<\/em><\/p>\n

Project Location<\/h3>\n

University of Rhode Island-Kingston<\/p>\n

SURF Flex eligible?<\/h3>\n

Yes<\/p>\n

Project Description<\/h3>\n
\"\"
Cecile Cres<\/figcaption><\/figure>\n

Metagenomics is a useful technique for probing the functional potentials represented by microbial communities. While the acquisition of metagenomic data can be readily achieved with the development of high-throughput sequencing, the accurate assembly and functional analysis is still a non-trivial task for large datasets. In this project, we plan to apply machine learning tools for the analysis of metagenomic data obtained from a year-round sampling of the Narragansett Bay. Students will gain skills in high-performance computing, data analysis and machine learning. This research will help develop new tools to process and analyze sequencing data and therefore further our understanding of complex environmental microbial communities.<\/p>\n

This project involves primarily lab or computer work<\/strong><\/p>\n

Required\/Preferred Skills<\/h3>\n

Prior experience in python programming language. Course preparation in machine learning. Previous experience in deep learning is preferred but not required.<\/p>\n

Will students require their own transportation to field sites and\/or other off-campus locations?<\/h3>\n

No<\/p>\n

Apply to SURF<\/a> Back to Projects<\/a> SURF Homepage<\/a><\/div>\n
 <\/div>\n","protected":false},"excerpt":{"rendered":"

IO-3: Analysis of metagenomic data collected in the Narragansett Bay using machine learning approaches
\nCecile Cres, University of Rhode Island<\/p>\n","protected":false},"author":1994,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[947],"tags":[955,958,741],"class_list":["post-37658","post","type-post","status-publish","format-standard","hentry","category-21-surf","tag-io-21-surf","tag-flex-21-surf","tag-university-of-rhode-island"],"acf":[],"_links":{"self":[{"href":"https:\/\/web.uri.edu\/rinsfepscor\/wp-json\/wp\/v2\/posts\/37658","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/web.uri.edu\/rinsfepscor\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/web.uri.edu\/rinsfepscor\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/rinsfepscor\/wp-json\/wp\/v2\/users\/1994"}],"replies":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/rinsfepscor\/wp-json\/wp\/v2\/comments?post=37658"}],"version-history":[{"count":5,"href":"https:\/\/web.uri.edu\/rinsfepscor\/wp-json\/wp\/v2\/posts\/37658\/revisions"}],"predecessor-version":[{"id":38389,"href":"https:\/\/web.uri.edu\/rinsfepscor\/wp-json\/wp\/v2\/posts\/37658\/revisions\/38389"}],"wp:attachment":[{"href":"https:\/\/web.uri.edu\/rinsfepscor\/wp-json\/wp\/v2\/media?parent=37658"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/web.uri.edu\/rinsfepscor\/wp-json\/wp\/v2\/categories?post=37658"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/web.uri.edu\/rinsfepscor\/wp-json\/wp\/v2\/tags?post=37658"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}