This website uses cookies
We use cookies to personalise content and ads, to provide social media features and to analyse our traffic. We also share information about your use of our site with our social media, advertising and analytics partners who may combine it with other information that you’ve provided to them or that they’ve collected from your use of their services.
Consent Selection
Details
  • Necessary cookies help make a website usable by enabling basic functions like page navigation and access to secure areas of the website. The website cannot function properly without these cookies.
  • Preference cookies enable a website to remember information that changes the way the website behaves or looks, like your preferred language or the region that you are in.
    • We do not use cookies of this type.

  • Statistic cookies help website owners to understand how visitors interact with websites by collecting and reporting information anonymously.
    • We do not use cookies of this type.

  • Marketing cookies are used to track visitors across websites. The intention is to display ads that are relevant and engaging for the individual user and thereby more valuable for publishers and third party advertisers.
    • We do not use cookies of this type.

  • Unclassified cookies are cookies that we are in the process of classifying, together with the providers of individual cookies.
    • __emg_sidPending
      Maximum Storage Duration: 1 dayType: HTTP Cookie
      __emg_vidPending
      Maximum Storage Duration: 1 yearType: HTTP Cookie
      nl-read-countPending
      Maximum Storage Duration: PersistentType: HTML Local Storage
Cookie declaration last updated on 8/12/26 by Cookiebot
[#IABV2_TITLE#]
[#IABV2_BODY_INTRO#]
[#IABV2_BODY_LEGITIMATE_INTEREST_INTRO#]
[#IABV2_BODY_PREFERENCE_INTRO#]
[#IABV2_BODY_PURPOSES_INTRO#]
[#IABV2_BODY_PURPOSES#]
[#IABV2_BODY_FEATURES_INTRO#]
[#IABV2_BODY_FEATURES#]
[#IABV2_BODY_PARTNERS_INTRO#]
[#IABV2_BODY_PARTNERS#]
About
Cookies are small text files that can be used by websites to make a user's experience more efficient.

The law states that we can store cookies on your device if they are strictly necessary for the operation of this site. For all other types of cookies we need your permission.

This site uses different types of cookies. Some cookies are placed by third party services that appear on our pages.

You can at any time change or withdraw your consent from the Cookie Declaration on our website.

Learn more about who we are, how you can contact us and how we process personal data in our Privacy Policy.

Please state your consent ID and date when you contact us regarding your consent.
NewsLayer.com
NewsLayer PulseLIVEBTC$77,034-0.20%ETH$2,414+0.91%SOL$93.29+2.18%XRP$1.44+2.76%DOGE$0.0904+7.94%ADA$0.2233+1.79%Total Cap$2.72T+0.12%Layer Index58 Neutral

New AI model reveals hidden code behind gene activation

News-Medical reports that a new artificial intelligence model has revealed previously hidden code involved in gene activation. The finding points to AI’s potential to help researchers better understand how genes are switched on.

News-Medical

Publisher

Aug 22, 2026 at 2:40 AM UTC · 2 min de lecture

New AI model reveals hidden code behind gene activation
NewsLayer editorial artwork

Key Signal

~500,000 initiator versions sequenced

Last Updated

il y a 13 heures

Precise activation of tens of thousands of genes is critical for healthy development and growth. Specialized segments of our DNA are responsible for carefully orchestrating genetic sequences that result in the production of enzymes, hormones, proteins and other crucial components underlying cell structure and function. But if these genes are not correctly activated, cells can stop functioning or result in various disorders, including cancer.

To fully understand specific sequences within DNA that enable gene activation, researchers in University of California San Diego Professor James T. Kadonaga's laboratory set out to decipher an important segment of DNA known as the "initiator." This is the site at which the instructions coded in genes are first converted, or expressed, into functional products.

In the new study led by graduate student researcher Torrey Rhyne-Carrigg, scientists used high-throughput DNA sequencing technology to determine the gene expression activity of approximately 500,000 different versions of the initiator. With this information, they employed machine learning, a type of artificial intelligence, to create an AI model that then decoded the initiator's signature DNA pattern. With the initiator's DNA identity unmasked, the researchers could then search for its telltale sequence, finding that about 60% of human genes contain the initiator.

Article Intelligence

Topics

Sponsored

Ad
House — Advertise on NewsLayer
NewsLayerLearn more

NewsLayer Premium

Unlock deeper intelligence.

Ad-free reading, exclusive research, and real-time onchain insights.

Go Premium