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Vida Ravanmehr

Showing results (1-10 of 17) with videos related to

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IEEE/ACM Transactions on Computational Biology and Bioinformatics|April 6, 2011
An information theoretic approach to constructing robust Boolean gene regulatory networksBane Vasić, Vida Ravanmehr, Anantha Raman Krishnan
Bioinformatics (Oxford, England)|November 1, 2017
ChIPWig: a random access-enabling lossless and lossy compression method for ChIP-seq dataVida Ravanmehr, Minji Kim, Zhiying Wang, et al.
Nature Computational Science|January 4, 2024
GRAPE for fast and scalable graph processing and random-walk-based embeddingLuca Cappelletti, Tommaso Fontana, Elena Casiraghi, et al.
Nature Communications|March 19, 2025
Diverse ancestral representation improves genetic intolerance metricsAlexander L Han, Chloe F Sands, Dorota Matelska, et al.
Biorxiv : the Preprint Server for Biology|August 26, 2020
KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 responseJustin Reese, Deepak Unni, Tiffany J Callahan, et al.
American Journal of Human Genetics|August 7, 2020
Interpretable Clinical Genomics with a Likelihood Ratio ParadigmPeter N Robinson, Vida Ravanmehr, Julius O B Jacobsen, et al.
NAR Genomics and Bioinformatics|December 10, 2021
Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancerVida Ravanmehr, Hannah Blau, Luca Cappelletti, et al.
Plos Genetics|February 3, 2026
Rare heterozygous missense variants in VSX2 are associated with retinal detachmentDaniel C Brock, Justin S Dhindsa, Yifan Chen, et al.
Bioinformatics Advances|April 5, 2024
Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learningLuca Cappelletti, Lauren Rekerle, Tommaso Fontana, et al.
Patterns (New York, N.Y.)|November 16, 2020
KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 ResponseJustin T Reese, Deepak Unni, Tiffany J Callahan, et al.
Pageof 2

Showing results (1-10 of 17) with videos related to

Sort By:
Pageof 2
IEEE/ACM Transactions on Computational Biology and Bioinformatics|April 6, 2011
An information theoretic approach to constructing robust Boolean gene regulatory networksBane Vasić, Vida Ravanmehr, Anantha Raman Krishnan
Bioinformatics (Oxford, England)|November 1, 2017
ChIPWig: a random access-enabling lossless and lossy compression method for ChIP-seq dataVida Ravanmehr, Minji Kim, Zhiying Wang, et al.
Nature Computational Science|January 4, 2024
GRAPE for fast and scalable graph processing and random-walk-based embeddingLuca Cappelletti, Tommaso Fontana, Elena Casiraghi, et al.
Nature Communications|March 19, 2025
Diverse ancestral representation improves genetic intolerance metricsAlexander L Han, Chloe F Sands, Dorota Matelska, et al.
Biorxiv : the Preprint Server for Biology|August 26, 2020
KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 responseJustin Reese, Deepak Unni, Tiffany J Callahan, et al.
American Journal of Human Genetics|August 7, 2020
Interpretable Clinical Genomics with a Likelihood Ratio ParadigmPeter N Robinson, Vida Ravanmehr, Julius O B Jacobsen, et al.
NAR Genomics and Bioinformatics|December 10, 2021
Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancerVida Ravanmehr, Hannah Blau, Luca Cappelletti, et al.
Plos Genetics|February 3, 2026
Rare heterozygous missense variants in VSX2 are associated with retinal detachmentDaniel C Brock, Justin S Dhindsa, Yifan Chen, et al.
Bioinformatics Advances|April 5, 2024
Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learningLuca Cappelletti, Lauren Rekerle, Tommaso Fontana, et al.
Patterns (New York, N.Y.)|November 16, 2020
KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 ResponseJustin T Reese, Deepak Unni, Tiffany J Callahan, et al.
Pageof 2