{"id":221889,"date":"2025-09-15T00:16:38","date_gmt":"2025-09-15T05:16:38","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2025\/09\/ai-to-integrate-bulk-multi-omics-data-for-precision-oncology"},"modified":"2025-09-15T00:16:38","modified_gmt":"2025-09-15T05:16:38","slug":"ai-to-integrate-bulk-multi-omics-data-for-precision-oncology","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2025\/09\/ai-to-integrate-bulk-multi-omics-data-for-precision-oncology","title":{"rendered":"AI to integrate bulk multi-omics data for precision oncology"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/ai-to-integrate-bulk-multi-omics-data-for-precision-oncology2.jpg\"><\/a><\/p>\n<p>\u201cCancer and other complex diseases arise from the interplay of various biological factors, for example, at the DNA, RNA, and protein levels,\u201d explains the author. Characteristic changes at these levels \u2014 such as the amount of HER2 protein produced in breast or stomach cancer \u2014 are often recorded, but typically not yet analyzed in conjunction with all other therapy-relevant factors.<\/p>\n<p>This is where Flexynesis comes in. \u201cComparable tools so far have often been either difficult to use, or only useful for answering certain questions,\u201d says the author. \u201cFlexynesis, by contrast, can answer various medical questions at the same time: for example, what type of cancer is involved, what drugs are particularly effective in this case, and how these will affect the patient\u2019s chances of survival.\u201d The tool also helps identify suitable biomarkers for diagnosis and prognosis, or \u2014 if metastases of unknown origin are discovered \u2014 to identify the primary tumor. \u201cThis makes it easier to develop comprehensive and personalized treatment strategies for all kinds of cancer patients,\u201d says the author.<\/p>\n<hr>\n<p>Nearly 50 new cancer therapies are approved every year. This is good news. \u201cBut for patients and their treating physicians, it is becoming increasingly difficult to keep track and to select the treatment methods from which the people affected \u2014 each with their very individual tumor characteristics \u2014 will benefit the most,\u201d says the senior author. The researcher has been working for some time on developing tools that use artificial intelligence to make more precise diagnoses and that also determine the best form of therapy tailored to individual patients.<\/p>\n<p>The team has now developed a toolkit called Flexynesis, which does not rely solely on classical machine learning but also uses deep learning to evaluate very different types of data simultaneously \u2014 for example, multi-omics data as well as specially processed texts and images, such as CT or MRI scans. \u201cIn this way, it enables doctors to make better diagnoses, prognoses, and develop more precise treatment strategies for their patients,\u201d says the author. Flexynesis is described in detail in a paper published in \u201cNature Communications.\u201d<\/p>\n<p>\u201cWe are running multiple translational projects with medical doctors who want to identify biomarkers from multi-omics data that align with disease outcomes,\u201d says the first and co-corresponding author of the publication. \u201cAlthough many deep-learning based methods have been published for this purpose, most have turned out to be inflexible, tied to specific modeling tasks, or difficult to install and reuse. That gap motivated us to build Flexynesis as a proper toolkit, which is flexible for different modeling tasks and packaged on PyPI, Guix, Docker, Bioconda, and Galaxy, so others can readily apply it in their own pipelines.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u201cCancer and other complex diseases arise from the interplay of various biological factors, for example, at the DNA, RNA, and protein levels,\u201d explains the author. Characteristic changes at these levels \u2014 such as the amount of HER2 protein produced in breast or stomach cancer \u2014 are often recorded, but typically not yet analyzed in conjunction [\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-221889","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\/221889","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=221889"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/221889\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=221889"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=221889"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=221889"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}