{"id":8136,"date":"2021-10-11T15:11:07","date_gmt":"2021-10-11T13:11:07","guid":{"rendered":"https:\/\/www.igb.cnr.it\/?post_type=mec-events&#038;p=8136"},"modified":"2021-10-11T15:11:07","modified_gmt":"2021-10-11T13:11:07","slug":"ngs-for-evolutionary-biologists-from-basic-scripting-to-variant-calling-2-843-909","status":"publish","type":"mec-events","link":"https:\/\/www.igb.cnr.it\/index.php\/events\/ngs-for-evolutionary-biologists-from-basic-scripting-to-variant-calling-2-843-909\/","title":{"rendered":"Machine Learning for Biologists"},"content":{"rendered":"<p>The aim of the course is to provide a practical introduction to the analysis of \u201comics\u201d data. Topics will range from data visualization\/exploration to univariate\/multivariate analysis and machine learning. Practical examples and applications will be illustrated by using R and Python.<\/p>\n<p>Course Milestones:<\/p>\n<ul>\n<li>Data exploration and visualization<\/li>\n<li>Univariate\/Multivariate analysis<\/li>\n<li>Introduction to machine learning: classifiers, performance measures, diagnostics<\/li>\n<li>Machine learning tools for the analysis of Gene Expression data<\/li>\n<li>The Data Analysis Plan (DAP) &#8211; intro to unbiased pipelines for (binary) classification<\/li>\n<li>Performance measures and diagnostic plots &#8211; Accuracy, MCC, Stability: theory and graphics<\/li>\n<li>Differential network analysis \u2013 co-expression networks, graph comparison, community detection: theory and examples in R\/Python, visualization by the igraph library and use of the ReNette web interface<\/li>\n<li>Basic application of ML to gene prediction<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>The aim of the course is to provide a practical introduction to the analysis of \u201comics\u201d data. Topics will range from data visualization\/exploration to univariate\/multivariate analysis and machine learning. Practical examples and applications will be illustrated by using R and Python. Course Milestones: Data exploration and visualization Univariate\/Multivariate analysis Introduction to machine learning: classifiers, performance [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":8117,"comment_status":"closed","ping_status":"closed","template":"","tags":[],"mec_category":[217],"class_list":{"0":"post-8136","1":"mec-events","2":"type-mec-events","3":"status-publish","4":"has-post-thumbnail","6":"mec_category-elixir","7":"czr-hentry"},"_links":{"self":[{"href":"https:\/\/www.igb.cnr.it\/index.php\/wp-json\/wp\/v2\/mec-events\/8136","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.igb.cnr.it\/index.php\/wp-json\/wp\/v2\/mec-events"}],"about":[{"href":"https:\/\/www.igb.cnr.it\/index.php\/wp-json\/wp\/v2\/types\/mec-events"}],"author":[{"embeddable":true,"href":"https:\/\/www.igb.cnr.it\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.igb.cnr.it\/index.php\/wp-json\/wp\/v2\/comments?post=8136"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.igb.cnr.it\/index.php\/wp-json\/wp\/v2\/media\/8117"}],"wp:attachment":[{"href":"https:\/\/www.igb.cnr.it\/index.php\/wp-json\/wp\/v2\/media?parent=8136"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.igb.cnr.it\/index.php\/wp-json\/wp\/v2\/tags?post=8136"},{"taxonomy":"mec_category","embeddable":true,"href":"https:\/\/www.igb.cnr.it\/index.php\/wp-json\/wp\/v2\/mec_category?post=8136"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}