{"id":231668,"date":"2026-02-20T01:04:39","date_gmt":"2026-02-20T07:04:39","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2026\/02\/ai-tool-debuts-with-better-genomic-predictions-and-explanations"},"modified":"2026-02-20T01:04:39","modified_gmt":"2026-02-20T07:04:39","slug":"ai-tool-debuts-with-better-genomic-predictions-and-explanations","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2026\/02\/ai-tool-debuts-with-better-genomic-predictions-and-explanations","title":{"rendered":"AI tool debuts with better genomic predictions and explanations"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/ai-tool-debuts-with-better-genomic-predictions-and-explanations.jpg\"><\/a><\/p>\n<p>Artificial intelligence has taken the world by storm. In biology, AI tools called deep neural networks (DNNs) have proven invaluable for predicting the results of genomic experiments. Their usefulness has these tools poised to set the stage for efficient, AI-guided research and potentially lifesaving discoveries\u2014if scientists can work out the kinks. The findings are <a href=\"https:\/\/www.nature.com\/articles\/s44387-025-00053-3\" target=\"_blank\">published<\/a> in the journal npj Artificial Intelligence.<\/p>\n<p>\u201cRight now, there are a lot of different AI tools where you\u2019ll give an input, and they\u2019ll give an output, but we don\u2019t have a good way of assessing the certainty, or how confident they are, in their answers,\u201d explains Cold Spring Harbor Laboratory (CSHL) Associate Professor Peter Koo. \u201cThey all come out in the same format, whether you\u2019re using a large language model or DNNs used in genomics and other fields of biology.\u201d<\/p>\n<p>It\u2019s one of the greatest challenges today\u2019s researchers face. Now, Koo, former CSHL postdoc Jessica Zhou, and graduate student Kaeli Rizzo have devised a potential solution\u2014DEGU (Distilling Ensembles for Genomic Uncertainty-aware models). DNNs trained using DEGU are more efficient and more accurate in their predictions than those learning via standard methods.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Artificial intelligence has taken the world by storm. In biology, AI tools called deep neural networks (DNNs) have proven invaluable for predicting the results of genomic experiments. Their usefulness has these tools poised to set the stage for efficient, AI-guided research and potentially lifesaving discoveries\u2014if scientists can work out the kinks. The findings are published [\u2026]<\/p>\n","protected":false},"author":662,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11,6],"tags":[],"class_list":["post-231668","post","type-post","status-publish","format-standard","hentry","category-biotech-medical","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/231668","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\/662"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=231668"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/231668\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=231668"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=231668"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=231668"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}