(1/10)Pleased to share that our work on leveraging deep transformer-based representation learning for protein sequence optimization is out today in @NatMachIntell !
nature.com/articles/s4225… (2/10) First this work would not have been possible without the efforts of @egbert_castro, @dbhaskar92, Abhinav Godavarthi, @rubinfien, @KevinGivechian

Today, I’m proud to share our latest work published in @NatureBiotech describing MELD, a #MachineLearning algorithm for #SingleCell perturbation analysis.

Read this #tweetorial to learn about the work led by @dbburkhardt and Jay Stanley 🥳🎉🧪

nature.com/articles/s4158…
(1/16) Before we get into the details of the paper, I want to give a shout out to our excellent collaborators: @david_van_dijk, Guy Wolf, @giraldezlab, and Kevan Herold. This work was possible thanks to countless discussions, experimental support, and input along the way (2/16)

(1/n) Excited to present collaborative work with @VirusesImmunity @fperrywilson, Yale Impact, @Mila_Quebec, @mrguywolf! We explore 54 million cells, 18 clinical variables from YNHH patients with Multiscale PHATE to find features predictive of outcome. biorxiv.org/content/10.110… (2/n)If you like PHATE, you’ll love Multiscale PHATE which provides fast visualizations at multiple resolutions! Multiscale PHATE preserves manifold affinity and structure at each level of granularity. Kudos to first authors @mkuchroo @JcsHuang Patrick Wong.

TWEETORIAL: PHATE, our dimensionality reduction and #dataviz method featured on the cover of today’s @NatureBiotech! To help spread the word, @scottgigante and @DBBurkhardt put together this #tweetorial for you. nature.com/articles/s4158… (1/10)

https://twitter.com/NatureBiotech/status/1201929959490670593

PHATE is a dimensionality reduction algorithm designed for visualizing all kinds of data. Here we show 16K differentiating stem cells measured with scRNA-seq. Unlike tSNE and UMAP, PHATE doesn’t create “blobs” and instead preserves continuous structures in the data (2/10)