Author

Praveen Anand

Dana-Farber Cancer InstitutePrimary ORCID profile (0000-0002-2478-7042 · 3 papers)Unclaimed ORCID profile · follow to get notified of new preprints

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Praveen AnandiD3 papers · Nference (India)

Biology Disciplines

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Research Focus:benchmarking· 2 Approach:deep learning· 2 Research Focus:gene regulatory network inference· 2 Research Focus:protein structure prediction· 2 Approach:statistical model· 2 Assay:DNA methylation· 1 Research Focus:drug response / drug discovery· 1 Assay:proteomics· 1

Frequent Co-Authors

Peter K. KooiD· 2 shared Steffan B. PauliD· 2 shared Antonio MajdandziciD· 1 shared Matthew PloenzkeiD· 1 shared Sean R. EddyiD· 1 shared

Activity by Year

2018-2021 · peak 1/yr

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5 top collaborators · 8 mutual co-author links

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2 shared papersGenomics

Peter K. KooORCID

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2 papers

Recent·Most cited

Global Importance Analysis: An Interpretability Method to Quantify Importance of Genomic Features in Deep Neural Networks

Peter K. Koo, Antonio Majdandzic, Matthew Ploenzke, Praveen Anand, Steffan B. Paul

This paper introduces Global Importance Analysis (GIA), a method to quantify the effect size of genomic features in deep neural networks, demonstrating its application in predicting RNA-protein interactions.

PLoS Computational Biology · May 13, 2021Peer-reviewedGenomics

Inferring Sequence-Structure Preferences of RNA-Binding Proteins with Convolutional Residual Networks

Peter K. Koo, Praveen Anand, Steffan B. Paul, Sean R. Eddy

This paper introduces ResidualBind, a convolutional residual network that infers RNA-binding protein sequence and structure specificities from enriched sequence data, outperforming previous methods and learning RNA structure context from sequence data.

bioRxiv · Sep 15, 2018 · PreprintGenomics