Author

Daniel Esposito

Walter and Eliza Hall Institute of Medical ResearchORCID 0000-0002-8333-5046Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genomics· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:database / resource· 3 Approach:benchmark study· 2 Approach:experimental assay· 2 Assay:GWAS summary statistics· 2 Research Focus:software / tool· 2 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Alan F. RubiniD· 2 shared Douglas M. FowleriD· 2 shared Lea M. StaritaiD· 2 shared Abbye E. McEweniD· 1 shared Aisha Haley BianchiiD· 1 shared Amy TamiD· 1 shared Anthony T. PapenfussiD· 1 shared Debora S. MarksiD· 1 shared

Activity by Year

2018-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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2 shared papersGenomics

Alan F. RubinORCID

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3 papers

Recent·Most cited

GenomicsbioRxivPreprintNov 29, 2021

MaveDB v2: a curated community database with over three million variant effects from multiplexed functional assays

Alan F. Rubin, Joseph Min, Nathan J. Rollins…·Douglas M. Fowler

MaveDB v2 curates over three million variant effects from MAVE studies to facilitate analysis and dissemination of functional assay data.

23 citations

GWAS summary statisticsbenchmark study

𝕏 PostBluesky

GenomicsGenome biologyPeer-reviewedNov 3, 2019

An open-source platform to distribute and interpret data from multiplexed assays of variant effect

Daniel Esposito, Jochen Weile, Jay Shendure…·Alan F. Rubin

The paper introduces MaveDB, a public repository for MAVE data, and MaveVis, a tool to visualize and contextualize variant effect maps.

243 citationstop 1%

GWAS summary statisticsexperimental assay

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BioinformaticsbioRxivPreprintMay 18, 2018

Inferring edge function in protein-protein interaction networks

Daniel Esposito, Joseph Cursons, Melissa J. Davis

The paper infers edge functions in protein-protein interaction networks using machine learning with Gene Ontology, InterPro, and Pfam annotations, achieving high classification accuracy.

3 citations

humanprotein sequence

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