Author

David H. Brookes

University of California, BerkeleyORCID 0000-0002-5049-5799Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Assay:protein sequence· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Amirali AghazadehiD· 2 shared Jennifer ListgarteniD· 2 shared Hunter M. NisonoffiD· 1 shared Kannan RamchandraniD· 1 shared O. Ozan KoyluogluiD· 1 shared Orhan ÖçaliD· 1 shared Yijie HuangiD· 1 shared

Activity by Year

2021-2021 · peak 2/yr

Co-Author Collaboration Network

7 top collaborators · 21 mutual co-author links

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2 shared papersBioinformatics

Amirali AghazadehORCID

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2 papers

Recent·Most cited

BioinformaticsProceedings of the National Academy of SciencesPeer-reviewedDec 22, 2021

On the sparsity of fitness functions and implications for learning

David H. Brookes, Amirali Aghazadeh, Jennifer Listgarten

The paper studies the sparsity of fitness functions in biological sequences and develops a framework to estimate the number of samples needed for accurate recovery.

46 citationstop 5%

protein sequenceDNA methylation

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BioinformaticsNature CommunicationsPeer-reviewedSep 1, 2021

Sparse Epistatic Regularization of Deep Neural Networks for Inferring Fitness Functions

Amirali Aghazadeh, Hunter M. Nisonoff, Orhan Öçal…·Kannan Ramchandran

This paper introduces a method to improve deep neural network predictions of fitness functions by leveraging sparse epistatic interactions in genetic sequences.

42 citations

deep learning

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