Troyanskaya Lab (@TroyanskayaLab) header
Troyanskaya Lab (@TroyanskayaLab) profile picture

Troyanskaya Lab

@TroyanskayaLab

This account is run by Olga Troyanskaya and her group focusing on computational functional genomics at Princeton University.

 Princeton, NJ
function.princeton.edu
Joined August 2011
  • Tweets59
  • Following77
  • Followers542
2Media
Photo by Troyanskaya Lab (@TroyanskayaLab)Photo by Troyanskaya Lab (@TroyanskayaLab)
Troyanskaya Lab retweeted
Jian Zhou (@zhou_jian) profile picture
Jian Zhou@zhou_jian12 Jul 2022
The Sei web server on humanbase (hb.flatironinstitute.org/sei) is now updated to add the capability of computing Sei predictions including sequence class scores for any user-provided sequences or variants (big thanks to @wongak!)
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Troyanskaya Lab retweeted
Jian Zhou (@zhou_jian) profile picture
Jian Zhou@zhou_jian12 Jul 2022
Replying to @kathyxchen
@kathyxchen’s paper for sequence-based global map of regulatory activity is now out (nature.com/articles/s41588-0…)! Congratulations to all co-authors @wongak @OlgaTroyanskaya. Thanks to @anshulkundaje and @nameluem for the News & Views!
A sequence-based global map of regulatory activity for deciphering human genetics

A sequence-based global map of regulatory activity for deciphering human genetics

Nature Genetics - Sei is a new framework for integrating human genetics data with a sequence-based mapping of predicted regulatory activities to elucidate mechanisms contributing to complex traits...

nature.com
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Troyanskaya Lab retweeted
NatRevMaterials (@NatRevMater) profile picture
NatRevMaterials@NatRevMater18 Aug 2021
Machine learning in materials science! All our articles on machine learning in one collection, featuring @EKumachevaGroup @A_Aspuru_Guzik @TroyanskayaLab @draykol @SmitBerend @GormleyLab @xmwebb @NAresgroup @MKrallinger and many more - Check it out 😎go.nature.com/3gdwuVw
Image from NatRevMaterials (@NatRevMater)
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Troyanskaya Lab retweeted
Kathy Chen (@kathyxchen) profile picture
Kathy Chen@kathyxchen2 Aug 2021
Our new preprint (w/ @zhou_jian) is out! 🎉 We developed sequence classes, which allow for easily interpretable yet systematic quantification of the regulatory activities for any sequence & variant, using deep learning-based sequence modeling. (1/13) doi.org/10.1101/2021.07.29.4…
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Decoding disease: from genomes to networks to phenotypes go.nature.com/3fq7VnT #Review by @wongak, Rachel S. G. Sealfon, Chandra L. Theesfeld & @OlgaTroyanskaya @TroyanskayaLab @FlatironCCB @FlatironInst @Princeton @PrincetonHive
Decoding disease: from genomes to networks to phenotypes

Decoding disease: from genomes to networks to phenotypes

Nature Reviews Genetics - In this Review, the authors discuss computational methods for interpreting the molecular and clinical effects of genetic variants. They focus on methods leveraging machine...

nature.com
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Check out our latest review in NRG on decoding disease - from genomes to networks to phenotypes! nature.com/articles/s41576-0…
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Check out our review of machine learning methods to model multicellular complexity!
NatRevMaterials (@NatRevMater) profile pictureNatRevMaterials@NatRevMater16 Jul 2021
High-throughput experiments generate large data sets that allow the study of multicellular complexity; #MachineLearning can help analyse, interpret and model these datasets. Read more in the Review by Rachel Sealfon, @wongak & @OlgaTroyanskaya: go.nature.com/3hJ0ucT
Image from NatRevMaterials (@NatRevMater)
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Troyanskaya Lab retweeted
NatRevMaterials (@NatRevMater) profile picture
NatRevMaterials@NatRevMater16 Jul 2021
High-throughput experiments generate large data sets that allow the study of multicellular complexity; #MachineLearning can help analyse, interpret and model these datasets. Read more in the Review by Rachel Sealfon, @wongak & @OlgaTroyanskaya: go.nature.com/3hJ0ucT
Image from NatRevMaterials (@NatRevMater)
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Combing through millions of possible protein combinations to assemble a catalog that could be used to precisely target only cancer cells while leaving normal ones alone. Kudos to @limlab @UCSF @OlgaTroyanskaya @TroyanskayaLab ow.ly/QXMh50CylWC
Image from UCSF Helen Diller Family Comprehensive Cancer Ctr (@UCSFCancer)
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Troyanskaya Lab retweeted
Arjun Krishnan (@compbiologist) profile picture
Arjun Krishnan@compbiologist19 Nov 2020
It's #OlgaFest today in Michigan! 😀 @OlgaTroyanskaya's academic children & grandchildren are speaking in today's @WomenPlusData webinar hosted jointly by @UMich and @michiganstateu!
Women+ Data Science (@WomenPlusData) profile pictureWomen+ Data Science@WomenPlusData9 Nov 2020
Excited to host the final webinar of this season: 'Data Science in Biomedical Research' featuring Maria @ChikinaLab as our keynote speaker @PittTweet! ⚡ talks Anna @annakopoulos Kayla @kaylainbio Stephanie @slepphickey @CMSE_at_MSU @KrishnanLab Regstr: forms.gle/nbWrEdPR7fDPcKBy6
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Check out humanbase.io - from tissue-specific networks to mutational regulatory impact to disease gene prediction. Project lead by the amazing @wongak Nicely described in simonsfoundation.org/2020/11… @SimonsFdn @FlatironInst @FlatironCCB
Finding Signals in the Noise With HumanBase

Finding Signals in the Noise With HumanBase

What once seemed impossible — sequencing an entire genome — is now fast, routine and cheap. With sequences from hundreds of thousands of human genomes now available to scientists, as well as growing…

simonsfoundation.org
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thrilled to be a part of this collaboration and apply our cell-type specific networks at @HumanBaseFI to COVID-19.
Abhijit (Jeet) Naik (@minimalchange) profile pictureAbhijit (Jeet) Naik@minimalchange7 Oct 2020
SARS-CoV-2 receptor networks in diabetic and COVID-19 associated kidney disease kidney-international.org/art… . @MiKTMC @UMichKidney @umichmedicine @Berthiercc @OlgaTroyanskaya .
SARS-CoV-2 receptor networks in diabetic and COVID-19–associated kidney disease

SARS-CoV-2 receptor networks in diabetic and COVID-19–associated kidney disease

COVID-19 morbidity and mortality are increased via unknown mechanisms in patients with diabetes and kidney disease. SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) for entry into host cells....

kidney-international.org
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Troyanskaya Lab retweeted
medRxiv (@medrxivpreprint) profile picture
medRxiv@medrxivpreprint13 May 2020
SARS-CoV-2 receptor networks in diabetic kidney disease, BK-Virus nephropathy and COVID-19 associated acute kidney injury medrxiv.org/cgi/content/shor… #medRxiv
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Troyanskaya Lab retweeted
Jian Zhou (@zhou_jian) profile picture
Jian Zhou@zhou_jian4 Dec 2019
Hi genomics community! I recently started my own group at UTSW. We are looking for postdoc and students interested in machine learning and genomics to join us in our adventures in new frontiers! Please RT or forward to any potential interested candidates zhoulab.io/home#join-us
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Our latest paper on noncoding contributions in #autism using deep learning models is out in @NatureGenet. De novo noncoding variants contribute to # of cases comparable to coding. With @darnelr @zhou_jian @pazpark @chandralt4 @simonsFdn @princeton #WGS rdcu.be/bEAto
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Great description of our work out at Nature Genetics: "AI Accurately Predicts Effects of Genetic Mutations in Biological Dark Matter" simonsfoundation.org/2018/07… @FlatironInst @FlatironCCB @SimonsFdn @EPrinceton @PrincetonCS @princetonideas
AI Accurately Predicts Effects of Genetic Mutations in Biological Dark Matter

AI Accurately Predicts Effects of Genetic Mutations in Biological Dark Matter

A new machine learning framework, dubbed ExPecto, can predict the effects of genetic mutations in the so-called “dark matter” regions of the human genome. ExPecto pinpoints how specific mutations can…

simonsfoundation.org
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ExPecto predicts tissue-specific expression and variant effect ab initio from sequence, prioritizes all GWAS variants, generates in silico mutagenesis of all TSS proximal regions, and provides a link to evolutionary constraints on gene expression. rdcu.be/233k
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Proud of our discovery of noncoding regulatory signal in autism. Independently confirmed in discovery and replication cohorts for both DNA and RNA regulation. #deeplearning #WGS #autism #ASD biorxiv.org/content/early/20…
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Troyanskaya Lab retweeted
Vijay Sankaran (@bloodgenes) profile picture
Vijay Sankaran
@bloodgenes23 Apr 2018
Great talk from @OlgaTroyanskaya on predicting effects of mutations with DeepSEA and now being able to predict tissue specific gene expression with new ExPecto approach. Valuable for GWAS causal variant prioritization and de novo calling of disease variants #T2G18
Image from Vijay Sankaran (@bloodgenes)
Image from Vijay Sankaran (@bloodgenes)
Image from Vijay Sankaran (@bloodgenes)
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CS Prof. Olga Troyanskaya and team presented big-data approaches to drug development at the "Celebrate Princeton Invention" event. @OlgaTroyanskaya princeton.edu/news/2017/11/1…
Image from Princeton Computer Science (@PrincetonCS)
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