{"id":331390,"date":"2024-06-26T11:55:54","date_gmt":"2024-06-26T06:25:54","guid":{"rendered":"https:\/\/dripp.zone\/news\/?p=331390"},"modified":"2024-06-26T11:55:54","modified_gmt":"2024-06-26T06:25:54","slug":"ai-cancer-detector-boasts-98-accuracy-across-13-types-study-crypto-news","status":"publish","type":"post","link":"https:\/\/dripp.zone\/news\/ai-cancer-detector-boasts-98-accuracy-across-13-types-study-crypto-news\/","title":{"rendered":"AI Cancer Detector Boasts 98% Accuracy Across 13 Types: Study &#8211; Crypto News"},"content":{"rendered":"<div style=\"position:relative;overflow:visible;font-size:1.2em;line-height:1.58\">\n<p class=\"font-meta-serif-pro scene:font-noto-sans scene:text-base scene:md:text-lg font-normal text-lg md:text-xl md:leading-9 tracking-px text-body gg-dark:text-neutral-100\"><span style=\"font-weight:400\">A new artificial intelligence (AI) model was able to detect 13 different types of cancer with 98.2% accuracy using only DNA data from tissue samples, according to a new study. <\/span><span style=\"font-weight:400\">The AI model, dubbed EMethylNET, was developed by researchers at the University of Cambridge in the U.K. and could potentially accelerate early cancer detection, diagnosis, and treatment.<\/span><\/p>\n<p class=\"font-meta-serif-pro scene:font-noto-sans scene:text-base scene:md:text-lg font-normal text-lg md:text-xl md:leading-9 tracking-px text-body gg-dark:text-neutral-100\">The findings, published last week in <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/academic.oup.com\/biomethods\/article\/9\/1\/bpae028\/7696058?login=false\" class=\"sc-adb616fe-0 bJsyml\"><i>Biology Methods and Protocols<\/i><\/a>, focused on DNA methylation, a chemical process that occurs early on when cells start to grow\u2014including cancer cells. The researchers trained the machine learning model to spot early-building cancer structures and pathways.<\/p>\n<p class=\"font-meta-serif-pro scene:font-noto-sans scene:text-base scene:md:text-lg font-normal text-lg md:text-xl md:leading-9 tracking-px text-body gg-dark:text-neutral-100\"><span style=\"font-weight:400\">\u201cCancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide,\u201d the study noted. \u201cUsually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths.<\/span><\/p>\n<p class=\"font-meta-serif-pro scene:font-noto-sans scene:text-base scene:md:text-lg font-normal text-lg md:text-xl md:leading-9 tracking-px text-body gg-dark:text-neutral-100\"><span style=\"font-weight:400\">\u201cTherefore, the early detection of cancer\u2014combined with current therapies\u2014would have a significant impact on survival and treatment of various cancer types,\u201d it continued.<\/span><\/p>\n<p class=\"font-meta-serif-pro scene:font-noto-sans scene:text-base scene:md:text-lg font-normal text-lg md:text-xl md:leading-9 tracking-px text-body gg-dark:text-neutral-100\"><span style=\"font-weight:400\">The researchers trained EMethylNET on data from more than 6,000 tissue samples from The Cancer Genome Atlas, representing 13 cancer types including breast, lung, and colorectal cancers. They then tested it on more than 900 samples from independent datasets.<\/span><\/p>\n<p class=\"font-meta-serif-pro scene:font-noto-sans scene:text-base scene:md:text-lg font-normal text-lg md:text-xl md:leading-9 tracking-px text-body gg-dark:text-neutral-100\"><span style=\"font-weight:400\">The top-line finding was more than 98% accuracy in classifying 13 cancer types and non-cancer samples. The study also highlighted the fact that the method performed well a<\/span>cross diverse datasets from different countries. Researchers were also able to identify <span style=\"font-weight:400\">3,388 methylation sites linked to cancer-related genes and pathways.<\/span><\/p>\n<p class=\"font-meta-serif-pro scene:font-noto-sans scene:text-base scene:md:text-lg font-normal text-lg md:text-xl md:leading-9 tracking-px text-body gg-dark:text-neutral-100\"><span style=\"font-weight:400\">According to the study, the AI model combines two AI approaches: XGBoost, which selects relevant features, and a deep neural network for classification. This allows it to not only accurately detect cancer, but also provide insights into the body\u2019s regulation of non-genetic factors that mutate normal cells into cancer cells.\u00a0<\/span><\/p>\n<p class=\"font-meta-serif-pro scene:font-noto-sans scene:text-base scene:md:text-lg font-normal text-lg md:text-xl md:leading-9 tracking-px text-body gg-dark:text-neutral-100\"><span style=\"font-weight:400\">\u201cThese <\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.cancer.gov\/search\/results?swKeyword=epigenetics\" class=\"sc-adb616fe-0 bJsyml\"><span style=\"font-weight:400\">epigenetic<\/span><\/a><span style=\"font-weight:400\"> modifications are some of the earliest <\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.cancer.gov\/publications\/dictionaries\/cancer-terms\/def\/neoplasm\" class=\"sc-adb616fe-0 bJsyml\"><span style=\"font-weight:400\">neoplastic<\/span><\/a><span style=\"font-weight:400\"> events associated with <\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.cancer.gov\/search\/results?swKeyword=carcinogenesis\" class=\"sc-adb616fe-0 bJsyml\"><span style=\"font-weight:400\">carcinogenesis<\/span><\/a><span style=\"font-weight:400\">,&#8221; the study noted, reinforcing the potential of this approach in early cancer detection.<\/span><\/p>\n<p class=\"font-meta-serif-pro scene:font-noto-sans scene:text-base scene:md:text-lg font-normal text-lg md:text-xl md:leading-9 tracking-px text-body gg-dark:text-neutral-100\"><span style=\"font-weight:400\">While this initial research is promising, the authors caution that the technology requires further study and testing before clinical use. <\/span><span style=\"font-weight:400\">The research team said it is now working to adapt the model for liquid-tissue samples, which could provide for non-invasive early cancer screening.<\/span><\/p>\n<p class=\"font-meta-serif-pro scene:font-noto-sans scene:text-base scene:md:text-lg font-normal text-lg md:text-xl md:leading-9 tracking-px text-body gg-dark:text-neutral-100\"><span style=\"font-weight:400\">&#8220;Depending on the availability of training data, this method can be extended to detect hundreds of cancer types,&#8221; the report asserts.<\/span><\/p>\n<p class=\"font-meta-serif-pro scene:font-noto-sans scene:text-base scene:md:text-lg font-normal text-lg md:text-xl md:leading-9 tracking-px text-body gg-dark:text-neutral-100\"><span style=\"font-weight:400\">As AI continues to make inroads in healthcare, EMethylNET represents a strong step towards leveraging machine learning for earlier, more accurate cancer diagnosis. Such innovations could have far-reaching implications for public health.<\/span><\/p>\n<p class=\"font-meta-serif-pro scene:font-noto-sans scene:text-base scene:md:text-lg font-normal text-lg md:text-xl md:leading-9 tracking-px text-body gg-dark:text-neutral-100\"><span style=\"font-weight:400\">More than 19 million new cases of cancer are diagnosed and 10 million cancer deaths occur annually, <\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/gco.iarc.who.int\/media\/globocan\/factsheets\/cancers\/39-all-cancers-fact-sheet.pdf\" class=\"sc-adb616fe-0 bJsyml\"><span style=\"font-weight:400\">according to the latest estimates<\/span><\/a><span style=\"font-weight:400\"> from the International Agency for Research on Cancer.<\/span><\/p>\n<p class=\"font-meta-serif-pro scene:font-noto-sans scene:text-base scene:md:text-lg font-normal text-lg md:text-xl md:leading-9 tracking-px text-body gg-dark:text-neutral-100\"><span style=\"font-weight:400\">The lead researcher did not respond to a request for comment from <\/span><i><span style=\"font-weight:400\">Decrypt.<\/span><\/i><span style=\"font-weight:400\">\u00a0<\/span><\/p>\n<p class=\"font-meta-serif-pro scene:font-noto-sans scene:text-base scene:md:text-lg font-normal text-lg md:text-xl md:leading-9 tracking-px text-body gg-dark:text-neutral-100\"><i>Edited by <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/decrypt.co\/author\/ryanozawa\" class=\"sc-adb616fe-0 bJsyml\">Ryan Ozawa<\/a>.<\/i><\/p>\n<div class=\"my-4 border-b border-decryptGridline\">\n<div class=\"text-start p-8 md:py-12 md:px-12 max-w-prose relative\"><span class=\"border-t-4 border-l-4 w-4 h-4 md:border-t-[6px] md:border-l-[6px] md:w-6 md:h-6 border-decryptPurple dark:border-decryptNeon gg-dark:border-cc-pink-2 absolute top-4 left-4 md:top-6 md:left-6\" \/><span class=\"border-t-4 border-l-4 w-4 h-4 md:border-t-[6px] md:border-l-[6px] md:w-6 md:h-6 border-decryptPurple dark:border-decryptNeon gg-dark:border-cc-pink-2 absolute rotate-180 bottom-4 right-4 md:bottom-6 md:right-6\" \/><\/p>\n<h3 class=\"font-akzidenz-grotesk font-bold text-xl md:text-3xl md:text-center gg-dark:text-white\">Generally Intelligent<!-- --> Newsletter<\/h3>\n<p>A weekly AI journey narrated by Gen, a generative AI model.<\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>A new artificial intelligence (AI) model was able to detect 13 different types of cancer with 98.2% accuracy using only DNA data from tissue samples, according to a new study. The AI model, dubbed EMethylNET, was developed by researchers at the University of Cambridge in the U.K. and could potentially accelerate early cancer detection, diagnosis, [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":331392,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[230,225,221,227,226,228,229,60,223,224,222],"class_list":["post-331390","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cryptocurrency","tag-brave","tag-coinbase","tag-crypto","tag-decentralised","tag-decentralized","tag-decentralized-exchange","tag-erc-20","tag-featured","tag-meme-coin","tag-robinhood","tag-solana"],"_links":{"self":[{"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/posts\/331390","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/comments?post=331390"}],"version-history":[{"count":3,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/posts\/331390\/revisions"}],"predecessor-version":[{"id":331395,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/posts\/331390\/revisions\/331395"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/media\/331392"}],"wp:attachment":[{"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/media?parent=331390"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/categories?post=331390"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dripp.zone\/news\/wp-json\/wp\/v2\/tags?post=331390"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}