Sarah Nyquist
@snyquist2
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Postdoc at Gladstone Institute with @BeEngelhardt, previously @MITCSBPhD student in @shaleklab + @lab_berger, she/her
Joined February 2012
Do you want to work as part of a large collaborative team to drive innovation in #computational drug discovery? My lab is looking for a talented #Postdoc to lead efforts to do this! Please apply here ( https://t.co/ASfVGfZX97)! Compensation is well above the national average.
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Thank you so much for hosting me! What a great day of milk discussions and San Diego sunshine!
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Do you wish you could continually monitor your uterine health? I am helping to scope a women's health monitoring research project! Input from possible users would be incredibly valuable to us. Please fill out this anonymous survey, thank you!
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I am excited to share the preprint of my first postdoc paper (with @blekhman @frankwalbert @DemerathE), “Human milk variation is shaped by maternal genetics and impacts the infant gut microbiome” !!! https://t.co/XNUNQETAd5 This project began…
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Can you accurately scan ~10 million drug-target pairs per minute? We think you can! In our new preprint (with @rohitsingh8080 , Lenore Cowen, and @lab_berger ), we develop ConPLex, a high-throughput method for predicting drug-target interaction (DTI). https://t.co/VkTuuXaPro 🧵
biorxiv.org
Sequence-based prediction of drug-target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such computational prediction needs to be generalizable and...
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We’re very happy to announce that our RFdiffusion manuscript is now on bioRxiv! A lot can change in a week - we’ve now tested over a thousand designs and there’s so much exciting new data! 🧵
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So excited to share our paper "Genome-wide mapping of somatic mutation rates uncovers drivers of cancer" is out on @NatureBiotech (and open access)! https://t.co/XW3QoBV7Z1. Here's a🧵on what we did and what we found!
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Can ML build diverse protein scaffolds around functional motifs? We find equivariant diffusion models and Sequential Monte Carlo may help! Joint work with Jason Yim, and coauthors Doug Tischer, @ta_broderick, David Baker, @BarzilayRegina and Tommi Jaakkola https://t.co/Csqk7LkHk2
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Ellen is amazing — definitely consider joining her group if you can!
I am thrilled to share that I will be starting as an Asst Professor of Computer Science at @Princeton this summer! Really excited for the future of machine learning in structural biology and honored to have the opportunity to lead a group in this exciting area! #proteins247 1/
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So excited that our longitudinal study of the cells in human breast milk is out this week!
1/ Our paper that looks at cells in human breast over the course of lactation was published in PNAS this week! A press release from MIT covers this, but read on for more details! Paper: https://t.co/qm37wXXimh MIT News:
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Britt is such an amazing mentor and person. Check out this post doc opportunity in her group!
My lab is looking to recruit talented post docs interested in #Immunology, #scRNAseq, and #WomensHealth. Check out our job posting linked below, and our lab's website!
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How can we collect good enough data for machine learning driven protein design? We show that random numbers are part of the picture. Work with the David Baker lab (including @erika_alden_d ) and MSRNE (with @KevinKaichuang and @lorin_crawford ). (1/4) https://t.co/hch39I7HgZ
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Ever wondered what are the cells in human milk doing? Check out our latest work to find out!! https://t.co/n80hhiJN58
@WTKLAB @UnivCamPharm @SCICambridge @HelmholtzMunich @Cambridge_SBS @ISRHML
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Thank you @ForbesUnder30! Excited to finally get responses to my cold emails now.
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Ever used a hierarchical model to estimate a parameter and wondered if you’re actually doing better than a simpler baseline like maximum likelihood? We present a method addressing this question!
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1) We are sharing our manuscript identifying that the SARS-CoV-2 receptor ACE2 is an interferon stimulated gene in human upper airway epithelial cells. Results from an amazing team effort: https://t.co/8ACE1RXrYN (n/16)
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Conditional Density Estimation with Bayesian Normalising Flows. (arXiv:1802.04908v1 [ https://t.co/zjV5HgYw5a])
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Overpruning in Variational Bayesian Neural Networks. (arXiv:1801.06230v1 [stat.ML])
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I vote for @RiceUniversity’s "GenomeAssemblyInAWeek" #Innov8POWER8 project. Cast your vote: http://t.co/sLWBuJLuDq
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