This account is run by Olga Troyanskaya and her group focusing on computational functional genomics at Princeton University.
Joined August 2011
- Tweets 59
- Following 77
- Followers 545
- Likes 26
2 Photos and videos
Troyanskaya Lab retweeted
12 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
12 Jul 2022
@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!
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Troyanskaya Lab retweeted
18 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
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Troyanskaya Lab retweeted
2 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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Troyanskaya Lab retweeted
3 Aug 2021
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
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2 Aug 2021
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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20 Jul 2021
Check out our review of machine learning methods to model multicellular complexity!
16 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
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Troyanskaya Lab retweeted
16 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
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Troyanskaya Lab retweeted
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
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Troyanskaya Lab retweeted
19 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!
9 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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Troyanskaya Lab retweeted
10 Nov 2020
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
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7 Oct 2020
thrilled to be a part of this collaboration and apply our cell-type specific networks at @HumanBaseFI to COVID-19.
7 Oct 2020
SARS-CoV-2 receptor networks in diabetic and COVID-19 associated kidney disease kidney-international.org/art… . @MiKTMC @UMichKidney @umichmedicine @Berthiercc @OlgaTroyanskaya .
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Troyanskaya Lab retweeted
13 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
4 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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Troyanskaya Lab retweeted
28 May 2019
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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Troyanskaya Lab retweeted
16 Jul 2018
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
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Troyanskaya Lab retweeted
16 Jul 2018
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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Troyanskaya Lab retweeted
12 May 2018
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
23 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
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Troyanskaya Lab retweeted
15 Nov 2017
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…
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