Send Link mailing list submissions to
[email protected]
To subscribe or unsubscribe via the World Wide Web, visit
https://mailman.anu.edu.au/mailman/listinfo/link
or, via email, send a message with subject or body 'help' to
[email protected]
You can reach the person managing the list at
[email protected]
When replying, please edit your Subject line so it is more specific
than "Re: Contents of Link digest..."
Today's Topics:
1. GROVER has basically learned how to 'speak' DNA (Stephen Loosley)
----------------------------------------------------------------------
Message: 1
Date: Thu, 08 Aug 2024 17:21:24 +0930
From: Stephen Loosley <[email protected]>
To: "link" <[email protected]>
Subject: [LINK] GROVER has basically learned how to 'speak' DNA
Message-ID: <[email protected]>
Content-Type: text/plain; charset="UTF-8"
`
Science News
Cracking the code of life: new AI model learns DNA's hidden language
Date: August 5, 2024 Sources:
https://www.sciencedaily.com/releases/2024/08/240805134159.htm Technische
Universit?t Dresden
https://tu-dresden.de/tu-dresden/newsportal/news/den-code-des-lebens-knacken-neues-ki-modell-entschluesselt-die-versteckte-sprache-der-dna
Summary:
With GROVER, a new large language model trained on human DNA, researchers could
now attempt to decode the complex information hidden in our genome.
GROVER treats human DNA as a text, learning its rules and context to draw
functional information about the DNA sequences.
Share: FULL STORY
DNA contains foundational information needed to sustain life. Understanding how
this information is stored and organized has been one of the greatest
scientific challenges of the last century.
With GROVER, a new large language model trained on human DNA, researchers could
now attempt to decode the complex information hidden in our genome.
Developed by a team at the Biotechnology Center (BIOTEC) of Dresden University
of Technology, GROVER treats human DNA as a text, learning its rules and
context to draw functional information about the DNA sequences.
This new tool, published in Nature Machine Intelligence, has the potential to
transform genomics and accelerate personalized medicine.
Since the discovery of the double helix, scientists have sought to understand
the information encoded in DNA. 70 years later, it is clear that the
information hidden in the DNA is multilayered. Only 1-2 % of the genome
consists of genes, the sequences that code for proteins.
"DNA has many functions beyond coding for proteins. Some sequences regulate
genes, others serve structural purposes, most sequences serve multiple
functions at once. Currently, we don't understand the meaning of most of the
DNA.
When it comes to understanding the non-coding regions of the DNA, it seems that
we have only started to scratch the surface.
This is where AI and large language models can help," says Dr. Anna Poetsch,
research group leader at the BIOTEC.
DNA as a Language
Large language models, like GPT, have transformed our understanding of
language. Trained exclusively on text, the large language models developed the
ability to use the language in many contexts.
"DNA is the code of life. Why not treat it like a language?" says Dr. Poetsch.
The Poetsch team trained a large language model on a reference human genome.
The resulting tool named GROVER, or "Genome Rules Obtained via Extracted
Representations," can be used to extract biological meaning from the DNA.
"GROVER learned the rules of DNA. In terms of language, we are talking about
grammar, syntax, and semantics. For DNA this means learning the rules governing
the sequences, the order of the nucleotides and sequences, and the meaning of
the sequences. Like GPT models learning human languages, GROVER has basically
learned how to 'speak' DNA," explains Dr. Melissa Sanabria, the researcher
behind the project.
The team showed that GROVER can not only accurately predict the following DNA
sequences but can also be used to extract contextual information that has
biological meaning, e.g., identify gene promoters or protein binding sites on
DNA. GROVER also learns processes that are generally considered to be
"epigenetic," i.e., regulatory processes that happen on top of the DNA rather
than being encoded.
"It is fascinating that by training GROVER with only the DNA sequence, without
any annotations of functions, we are actually able to extract information on
biological function. To us, it shows that the function, including some of the
epigenetic information, is also encoded in the sequence," says Dr. Sanabria.
The DNA Dictionary
"DNA resembles language. It has four letters that build sequences and the
sequences carry a meaning. However, unlike a language, DNA has no defined
words," says Dr. Poetsch. DNA consists of four letters (A, T, G, and C) and
genes, but there are no predefined sequences of different lengths that combine
to build genes or other meaningful sequences.
To train GROVER, the team had to first create a DNA dictionary. They used a
trick from compression algorithms. "This step is crucial and sets our DNA
language model apart from the previous attempts," says Dr. Poetsch.
"We analyzed the whole genome and looked for combinations of letters that occur
most often. We started with two letters and went over the DNA, again and again,
to build it up to the most common multi-letter combinations.
In this way, in about 600 cycles, we have fragmented the DNA into 'words' that
let GROVER perform the best when it comes to predicting the next sequence,"
explains Dr. Sanabria.
The Promise of AI in Genomics
GROVER promises to unlock the different layers of genetic code. DNA holds key
information on what makes us human, our disease predispositions, and our
responses to treatments.
"We believe that understanding the rules of DNA through a language model is
going to help us uncover the depths of biological meaning hidden in the DNA,
advancing both genomics and personalized medicine," says Dr. Poetsch.
Materials provided by Technische Universit?t Dresden.
------------------------------
Subject: Digest Footer
_______________________________________________
Link mailing list
[email protected]
https://mailman.anu.edu.au/mailman/listinfo/link
------------------------------
End of Link Digest, Vol 381, Issue 6
************************************
