{"id":126929,"date":"2021-08-29T23:22:38","date_gmt":"2021-08-30T06:22:38","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2021\/08\/rna-structures-predicted-with-uncanny-accuracy"},"modified":"2021-08-29T23:22:38","modified_gmt":"2021-08-30T06:22:38","slug":"rna-structures-predicted-with-uncanny-accuracy","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2021\/08\/rna-structures-predicted-with-uncanny-accuracy","title":{"rendered":"RNA Structures Predicted with Uncanny Accuracy"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/rna-structures-predicted-with-uncanny-accuracy.jpg\"><\/a><\/p>\n<p>\u201cThe network learned to find fundamental concepts that are key to molecular structure formation, but without explicitly being told to,\u201d Townshend added. \u201cThe exciting aspect is that the algorithm has clearly recovered things that we knew were important, but it has also recovered characteristics that we didn\u2019t know about before.\u201d<\/p>\n<p>Having shown success with proteins, the researchers turned their attention to RNA molecules. The researchers tested their algorithm in a series of \u201cRNA Puzzles\u201d from a longstanding competition in their field, and in every case, the tool outperformed all the other puzzle participants and did so without being designed specifically for RNA structures.<\/p>\n<p>\u201cWe introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures,\u201d the authors of the <em>Science<\/em> article wrote. \u201cThe resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u201cThe network learned to find fundamental concepts that are key to molecular structure formation, but without explicitly being told to,\u201d Townshend added. \u201cThe exciting aspect is that the algorithm has clearly recovered things that we knew were important, but it has also recovered characteristics that we didn\u2019t know about before.\u201d Having shown success with proteins, [\u2026]<\/p>\n","protected":false},"author":556,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[41,6],"tags":[],"class_list":["post-126929","post","type-post","status-publish","format-standard","hentry","category-information-science","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/126929","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/556"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=126929"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/126929\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=126929"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=126929"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=126929"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}