• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Tuesday, August 5, 2025
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News Biology

AI researchers ask: What’s going on inside the black box?

Bioengineer by Bioengineer
February 8, 2021
in Biology
Reading Time: 2 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

IMAGE

Credit: Ben Wigler/CSHL, 2021

Cold Spring Harbor Laboratory (CSHL) Assistant Professor Peter Koo and collaborator Matt Ploenzke reported a way to train machines to predict the function of DNA sequences. They used “neural nets”, a type of artificial intelligence (AI) typically used to classify images. Teaching the neural net to predict the function of short stretches of DNA allowed it to work up to deciphering larger patterns. The researchers hope to analyze more complex DNA sequences that regulate gene activity critical to development and disease.

Machine-learning researchers can train a brain-like “neural net” computer to recognize objects, such as cats or airplanes, by showing it many images of each. Testing the success of training requires showing the machine a new picture of a cat or an airplane and seeing if it classifies it correctly. But, when researchers apply this technology to analyzing DNA patterns, they have a problem. Humans can’t recognize the patterns, so they may not be able to tell if the computer identifies the right thing. Neural nets learn and make decisions independently of their human programmers. Researchers refer to this hidden process as a “black box”. It is hard to trust the machine’s outputs if we don’t know what is happening in the box.

Koo and his team fed DNA (genomic) sequences into a specific kind of neural network called a convolutional neural network (CNN), which resembles how animal brains process images. Koo says:

“It can be quite easy to interpret these neural networks because they’ll just point to, let’s say, whiskers of a cat. And so that’s why it’s a cat versus an airplane. In genomics, it’s not so straightforward because genomic sequences aren’t in a form where humans really understand any of the patterns that these neural networks point to.”

Koo’s research, reported in the journal Nature Machine Intelligence, introduced a new method to teach important DNA patterns to one layer of his CNN. This allowed his neural network to build on the data to identify more complex patterns. Koo’s discovery makes it possible to peek inside the black box and identify some key features that lead to the computer’s decision-making process.

But Koo has a larger purpose in mind for the field of artificial intelligence. There are two ways to improve a neural net: interpretability and robustness. Interpretability refers to the ability of humans to decipher why machines give a certain prediction. The ability to produce an answer even with mistakes in the data is called robustness. Usually, researchers focus on one or the other. Koo says:

“What my research is trying to do is bridge these two together because I don’t think they’re separate entities. I think that we get better interpretability if our models are more robust.”

Koo hopes that if a machine can find robust and interpretable DNA patterns related to gene regulation, it will help geneticists understand how mutations affect cancer and other diseases.

###

Media Contact
Sara Roncero-Menendez
[email protected]

Related Journal Article

http://dx.doi.org/10.1101/2020.06.14.150706

Tags: BiologyGenesGeneticsRobotry/Artificial IntelligenceTechnology/Engineering/Computer Science
Share12Tweet8Share2ShareShareShare2

Related Posts

blank

Stability and Refolding of Zika Virus EDIII Protein

August 5, 2025
blank

Malaria Rapid Test Accuracy in Young Burkina Faso Children

August 5, 2025

Dual Lactic Acid Fermentation Boosts Corn Juice Benefits

August 5, 2025

TROPOS Researchers Honored with Prestigious Light Scattering Awards

August 5, 2025
Please login to join discussion

POPULAR NEWS

  • blank

    Neuropsychiatric Risks Linked to COVID-19 Revealed

    72 shares
    Share 29 Tweet 18
  • Overlooked Dangers: Debunking Common Myths About Skin Cancer Risk in the U.S.

    61 shares
    Share 24 Tweet 15
  • Predicting Colorectal Cancer Using Lifestyle Factors

    46 shares
    Share 18 Tweet 12
  • Dr. Miriam Merad Honored with French Knighthood for Groundbreaking Contributions to Science and Medicine

    47 shares
    Share 19 Tweet 12

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

Stability and Refolding of Zika Virus EDIII Protein

Assessing Demirjian Method Reliability Among Forensic Experts

Malaria Rapid Test Accuracy in Young Burkina Faso Children

  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.