Author

Antonio Majdandzic

Cold Spring Harbor LaboratoryORCID 0000-0002-0791-7677Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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

Frequent Co-Authors

Peter K. KooiD· 2 shared Chandana RajeshiD· 1 shared Matthew PloenzkeiD· 1 shared Praveen Anand· 1 shared Steffan B. PauliD· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 7 mutual co-author links

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2 shared papersBioinformatics

Peter K. KooORCID

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2 papers

Recent·Most cited

BioinformaticsGenome biologyPeer-reviewedMay 9, 2023

Correcting gradient-based interpretations of deep neural networks for genomics

Antonio Majdandzic, Chandana Rajesh, Peter K. Koo

The paper corrects gradient-based interpretations of deep neural networks in genomics to reduce noise from off-simplex gradients, improving attribution method interpretability.

49 citationstop 4%

statistical modelbenchmarking

𝕏 PostBluesky

GenomicsPLoS Computational BiologyPeer-reviewedMay 13, 2021

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

Peter K. Koo, Antonio Majdandzic, Matthew Ploenzke…·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.

81 citationstop 4%

DNA methylationwhole-genome sequencing

𝕏 PostBluesky