Krishnaswamy Lab Profile
Krishnaswamy Lab

@KrishnaswamyLab

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We develop data geometric, topological, dynamic, deep learning methods for analysis, visualization and representation of big data, especially biomedical data.

Yale University
Joined February 2017
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@KrishnaswamyLab
Krishnaswamy Lab
4 years
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 🥳🎉🧪. (1/16).
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@KrishnaswamyLab
Krishnaswamy Lab
1 day
RT @YaleMed: A multinational team co-led by @KrishnaswamyLab has developed and tested a new #AI tool that can better characterize individua….
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@KrishnaswamyLab
Krishnaswamy Lab
2 days
RT @NatureBiotech: Defining and benchmarking open problems in single-cell analysis
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@KrishnaswamyLab
Krishnaswamy Lab
9 days
RT @ElizSMcKenna: Now online in @CD_AACR: AAnet Resolves a Continuum of Spatially-Localized Cell States to Unveil Intratumoral Heterogeneit….
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@KrishnaswamyLab
Krishnaswamy Lab
9 days
RT @genophoria: Preprint drop from our team at @arcinstitute! Introducing STATE: a model that learns and predicts transcriptomic responses….
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@KrishnaswamyLab
Krishnaswamy Lab
9 days
AI in the air at @AIXBIO, presented on our Immunostruct and Cellspicenet models as well as @emblebi on cellular dynamics. Thanks for hosting @e_petsalaki ! Nice also to see @AIXBIO organizers @mo_lotfollahi @deboramarks @mariabrbic !
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@KrishnaswamyLab
Krishnaswamy Lab
15 days
Glad to be (briefly) @Mila_Quebec again for MOML Thanks for organizing @dom_beaini and colleagues!
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@KrishnaswamyLab
Krishnaswamy Lab
28 days
RT @AlexanderTong7: So excited to share our new paper, FORT! 🎉 We're showing a simple regression approach to train discrete normalizing flo….
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@KrishnaswamyLab
Krishnaswamy Lab
1 month
RT @KavliAtYale: Mark your calendars 🗓️ for June 23 for a @KavliAtYale Awards Research in Progress!🧠. Hear from awardees @dbhaskar92, @LaSh….
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@KrishnaswamyLab
Krishnaswamy Lab
1 month
RT @valence_ai: 1/ Introducing TxPert: a new model that predicts transcriptional responses across diverse biological contexts . It’s design….
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@KrishnaswamyLab
Krishnaswamy Lab
2 months
RT @YaleEngineering: Transforming ordinary wood into an extraordinary innovation!.Proud to highlight Yale Engineering's Prof. Liangbing Hu,….
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@KrishnaswamyLab
Krishnaswamy Lab
2 months
I just realized the neurips deadline is AOE time and not 4 pm mountain or whatever! Need ice cream!.
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@KrishnaswamyLab
Krishnaswamy Lab
2 months
Very excited!!.
@gsp_workshop
Graph Signal Processing Workshop 2025
2 months
It's almost time for #GSP2025 ⏱️🥳. We're super excited to host you at @Mila_Quebec this coming Wednesday, 14th May! Hope those posters are coming along 😉. 👉🏻 FYI: Registrations are still open, so we encourage you to visit our website for details:
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@KrishnaswamyLab
Krishnaswamy Lab
2 months
Submitted all the grades today—- semester over!!.
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@KrishnaswamyLab
Krishnaswamy Lab
2 months
RT @ozalabCP: A really neat read while I wait bedside @StanfordChild for my daughter to receive a heart ♥️ transplant. Seeing the active re….
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@KrishnaswamyLab
Krishnaswamy Lab
2 months
@YNHH @RameshkBatra (8/n) I also want to thanks the @YNHH for the Innovation award we received to pursue this work! For code please see: Thanks for reading!.
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@KrishnaswamyLab
Krishnaswamy Lab
2 months
(7/n) We could not have done this study without valuable data from @YNHH and careful guidance of transplant surgeon @RameshkBatra! We trained the model on a cohort of 3,238 and validated on an external cohort of 1,908 patients from six hospitals across Connecticut!.
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@KrishnaswamyLab
Krishnaswamy Lab
2 months
(6/n) Overall our model has accuracies of 95.3 ± 1.0% and 95.4 ± 0.7% for predicting whether death would occur in the first 30 and 60 minutes, Heart rate, respiratory rate, mean arterial blood pressure (MAP), oxygen saturation (SpO2), and Glasgow Coma Scale (GCS) scores were.
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@KrishnaswamyLab
Krishnaswamy Lab
2 months
(5/n) In addition to simply producing a classification result, our ODE-RNN produces a latent space representation of a patient phenotype, allowing us to create a Phenoscape visualization using our PHATE dimensionality reduction method. This Phenoscape produces 3 data-driven
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@KrishnaswamyLab
Krishnaswamy Lab
2 months
(4/n) We show that our model outperforms other neural network and commonly used random forest-based methods.
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@KrishnaswamyLab
Krishnaswamy Lab
2 months
(3/n) We developed an ODE-RNN model, combining RNNs for accumulating patient history and ODEs for modeling continuous dynamics to time-to-death following terminal extubation in the ICU, and trained it using retrospective observational data.
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