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Evo: a foundation model for genomics! Using a well suited architecture, Evo learns from billions of bp of genomic sequence and performs well on several zero-shot prediction tasks on RNA, DNA and protein. Beautiful paper from Brian Hie & Patrick Hsu at Arc Institute

52,395 次观看 • 2 年前 •via X (Twitter)

14 条评论

Julia Bauman 的头像
Julia Bauman2 年前

Preprint link here:

ksminnovation 的头像
ksminnovation1 年前

Dr. Tal Patalon explores how AI mega initiatives like the $500B Stargate project & AI-powered genomic analysis by Illumina & NVIDIA are transforming healthcare. @TalPatalon @forbes @edengallery_ #AIinHealthcare #PrecisionMedicine #Multiomics #HealthTech

Matt Durrant 的头像
Matt Durrant2 年前

@BrianHie @pdhsu @arcinstitute Thank you for highlighting our work!

Dmitry Penzar 的头像
Dmitry Penzar2 年前

@BrianHie @pdhsu @arcinstitute What are you about the fact their model do activity prediction worse than gc-content?

Misha 的头像
Misha2 年前

@BrianHie @pdhsu @arcinstitute very good presentation

Ashton C Trotman-Grant 的头像
Ashton C Trotman-Grant2 年前

@BrianHie @pdhsu @arcinstitute Yes! Awesome summary Julia. This paper is so cool

Nishant Jha 的头像
Nishant Jha2 年前

@BrianHie @pdhsu @arcinstitute Been noodling on a playground for evo here:

ريان 的头像
ريان2 年前

@BrianHie @pdhsu @arcinstitute Julia — what do you think about the fact that they claim to generate functional CRISPR-Cas systems without experimental validation?

Julia Bauman 的头像
Julia Bauman2 年前

@BrianHie @pdhsu @arcinstitute I think that they’re going to do the experimental validation :)

Dr Rob Leigh 的头像
Dr Rob Leigh2 年前

@BrianHie @pdhsu @arcinstitute Paging Dr @Cian_Smyth

Neuropunk 的头像
Neuropunk2 年前

@BrianHie @pdhsu @arcinstitute Its about time we move to fully synthetic biology. This is where all the cures, improvements and the most deadly weapons of humanity lie

Ryan Metz 的头像
Ryan Metz2 年前

@BrianHie @pdhsu @arcinstitute Great work

Sean Jackewicz, MD 🧬🛠️ 的头像
Sean Jackewicz, MD 🧬🛠️2 年前

@BrianHie @pdhsu @arcinstitute Oooooo this could be so cool

Bin Shao 的头像
Bin Shao2 年前

Very nice video! I do hope some day people can create a similar one for our language model…

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🚀 We're thrilled to introduce Orthrus 🧬🐕—a groundbreaking mature RNA foundation model designed to push the boundaries of RNA property prediction! 🔬 What is Orthrus? Orthrus is a Mamba-based RNA foundation model, pre-trained using a novel self-supervised contrastive learning objective with biologically inspired augmentations. It optimizes the similarity between splicing isoforms and orthologous transcripts, capturing functional and evolutionary relationships to enhance mature RNA property prediction accuracy. 📑 Preprint: 💻 Code: 🌐 Project Page: 📦 Model Weights: 🧠 Why Orthrus? Decoding the RNA regulatory code is key to understanding biology, but traditional experimental approaches are slow and costly. Existing genomic foundation models rely on techniques like masked language modeling or next-token prediction, which aren't fully aligned with the complexities of genomic data—leading to suboptimal results. 🌟 Orthrus Highlights: - Biologically-Informed Contrastive Learning 🧪: A novel contrastive learning objective designed specifically for genomics, maximizing similarity between splicing isoforms and orthologous transcripts across species. - Extensive Pre-training 📊: Trained on splicing annotations from 10 species and orthologous alignments from 400+ mammalian species (Zoonomia Project), with a focus on sequences of high functional importance. - Superior Representations🏅: Orthrus outperforms existing genomic models on 5 mRNA property prediction tasks, often surpassing supervised methods with just a simple linear transformation. - Efficiency in Low-Data Settings📉: Orthrus excels in low-data regimes, achieving state-of-the-art results with as few as 45 labeled examples for fine-tuning on RNA half-life prediction. Shoutout to the amazing leading authors Phil (Phil Fradkin) and Ian (Ian Shi)! Also the work is impossible without an outstanding collaboration by Karina (Karin(a) Isaev), Brendan (Brendan Frey) , Quaid (Quaid Morris), Leo J. Lee! Vector Institute University Health Network U of T Department of Computer Science Temerty Centre for AI in Medicine (T-CAIREM) Department of Laboratory Medicine & Pathobiology

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115,050 次观看 • 2 年前

Dialectic 60: Patrick Hsu! Patrick Hsu & Arc Institute are combining biology, engineering, and AI to turn a sci-fi future into reality. Patrick got expelled from college at 13, returned at 15, later worked on early CRISPR breakthroughs, and teamed up with Silvana Konermann & Patrick Collison to found Arc. He works 3 jobs: core investigator at Arc, professor at Stanford, and venture partner at Thrive Capital. For all his intensity and ambition, he carries a lightness and boldness that is contagious. We discuss: - why research is 99% failure and even your best ideas kind of suck - infinite games and speedrunning school - the reach of GLP-1s and power laws in biotech - what makes a good biologist - Arc’s bet on people over projects - lab robots that finally work - AI-designed organisms and new forms of life - why heart disease could go the way of infectious disease - bro science, peptides, and biofuturism - research taste in the age of AI Timestamps: (0:00) Opening Highlights (1:39) Intro: Patrick Hsu (4:29) Start: Boldness, Infinite Games, & Ambition (12:54) GLP-1s and Power Laws in Biotechnology (20:17) Expelled at 13 and Falling for Biology (25:51) What Makes a Good Biologist, Research Taste, and Biology's Data Challenges (37:58) The Arc Model: Full-Stack Science (52:01) Betting on People, Style, & Learning to Simplify (57:22) Perturbation, World Models, and Evo (1:03:27) Virtual Cell Models (1:09:56) Designing Life: From Stem Cells to Genome Writing (1:18:39) Testing, Bro Science, and Human Data (1:24:26) Can It Exist vs. Will It Exist: Science Meets Markets (1:29:59) Biofuturism: Interoception, Peptides, Muscle Modulation, Sleep, and New Forms of Life (1:43:47) Fun, Obsolescence, Going Slow, and the Future Dialectic with Jackson Dahl 60: Patrick Hsu - Speedrunning the Infinite Game - is available on all platforms, including X. Links below.

Jackson Dahl

40,526 次观看 • 16 天前