hessian

[hessian @HessianInf]

@ThatMrE @SynBio1 Legions imperialis as well.

[hessian @HessianInf]

@BritaBelli @drrichjlaw @SynBioBeta @StacieCT @nvidia Meso scale modeling.

[hessian @HessianInf]

@SimonDBarnett @anshulkundaje There are tools for this. Use them. Ask the LLM what tool to use. Download and use it.

[hessian @HessianInf]

@maorshlomo @anshulkundaje Clockworkbio.

[hessian @HessianInf]

@Ronalfa If only you knew someone who thought like that.

[hessian @HessianInf]

@SynBio1 https://t.co/WUXO11ZjAl

[hessian @HessianInf]

@robertskmiles Like this https://t.co/WUXO11ZjAl

[hessian @HessianInf]

@Ronalfa Exactly.

[hessian @HessianInf]

@draparente Slacks and discords.

[hessian @HessianInf]

@hereisramji What’s the base variation in biology ;)

[Kexin Huang @KexinHuang5]

🧬GWAS is fundamental in drug discovery, linking disease to genetic variants. However, studying rare and uncommon diseases with GWAS is hard due to the huge sample sizes required. How can we use AI to help GWAS with small cohorts?

In a multi-year collaboration @GSK @StanfordAILab @StanfordMed @SCSatCMU, we are thrilled to share Knowledge Graph GWAS (KGWAS), the largest AI model that integrates >10 millions of multi-modal and multi-scale functional genomics data to improve GWAS power by 100% while discovering novel disease-critical variants, genes, cells, and networks!

A huge shoutout to our stellar team of AI, statistics, and human genetics scientists: @tkyzeng Soner Koc, Alexandra Pettet @zhou_jingtian @MikaSarkinJain Dongbo @_camiloruiz @ren_hongyu @laurencejmshowe Tom Richardson, Adrián Cortés, Katie Aiello, Kim Branson, @apfenning @jengreitz @martinjzhang @jure

Paper: https://t.co/t0tAratBZI

1/15🧵

[Patrick Schwab @schwabpa]

Understanding human biology across scales - from molecules to cells to entire organisms - remains one of biomedicine's greatest challenges in the fight against disease.

Today, we are announcing Phenformer - a multi-scale genetic language model that learns to read and interpret human genomes by connecting DNA, cell and tissue context, molecules and clinical outcomes.

Phenformer is a generative model of molecular mechanisms that enables researchers to unravel the mysteries underlying disease, and could thereby accelerate the development of precise future therapeutics.

[hessian @HessianInf]

@ImranSHaque 3 burners.. weak sauce.

[hessian @HessianInf]

@Biohazard3737 Occam global

[Danielle Belgrave @DaniCMBelg]

Drug discovery and development requires integration of data from different disciplines. To this end, we have developed JulesOS, an LLM-based operating system which allows users to explore research questions. Check out the demo here https://t.co/k2fyaYX4u3

[hessian @HessianInf]

@chris_wigley @GenomicsEngland @Nature @ExploreWellcome Read the intro to volume 1 issue 1. I remember doing that as an undergraduate. It’s a strangely prescient paragraph.

[hessian @HessianInf]

@ImranSHaque This is the way.

[Patrick Schwab @schwabpa]

The advent of high throughput genetic perturbation screening at single cell resolution (e.g. perturb-seq) holds great promise to potentially help uncover the wiring diagram of cellular biology.

However, when we first studied this topic we were surprised to find .. 👇

[hessian @HessianInf]

@jchodera I think your quote was “we have real computers now “

[hessian @HessianInf]

@ImranSHaque @hereisramji Yeah but who did you vote for ?