Author

Elisabeth M. Paietta

Albert Einstein College of MedicineORCID 0000-0002-0561-5544Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genetic & Genomic Medicine· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:differential expression· 2 Research Focus:dimensionality reduction· 2 Approach:statistical model· 2 Assay:ATAC-seq· 1 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Approach:deep learning· 1

Frequent Co-Authors

Brennan P. BergeroniD· 2 shared Ching‐Hon PuiiD· 2 shared Christopher S. ManringiD· 2 shared Daniel SaviciD· 2 shared Elias Joseph JabbouriD· 2 shared Hiroto InabaiD· 2 shared Jonathan D. DiedrichiD· 2 shared Kashi Raj BhattaraiiD· 2 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Brennan P. BergeronORCID

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3 papers

Recent·Most cited

Genetic & Genomic MedicineNature CommunicationsPeer-reviewedMay 1, 2024

Functional investigation of inherited noncoding genetic variation impacting the pharmacogenomics of childhood acute lymphoblastic leukemia treatment

Kashi Raj Bhattarai, Robert J. Mobley, Kelly R. Barnett…·Daniel Savic

The paper identifies noncoding genetic variants affecting chemotherapy response in childhood ALL by linking them to gene regulatory elements and showing functional impacts on drug sensitivity.

10 citations

humanbulk RNA-seq

𝕏 PostBluesky

GenomicsCell GenomicsPeer-reviewedNov 20, 2023

Epigenomic mapping in B-cell acute lymphoblastic leukemia identifies transcriptional regulators and noncoding variants promoting distinct chromatin architectures

Kelly R. Barnett, Robert J. Mobley, Jonathan D. Diedrich…·Daniel Savic

The paper maps chromatin accessibility in B-ALL subtypes using ATAC-seq, identifies TFs and genetic variants influencing chromatin architecture and gene regulation.

20 citationstop 4%

humanATAC-seq

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BioinformaticsbioRxivPreprintJan 14, 2021

Variational autoencoders learn universal latent representations of metabolomics data

Daniel P. Gomari, Annalise Schweickart, Leandro C. A. Cerchietti…·Jan Krumsiek

This study trains a variational autoencoder on metabolomics data to learn nonlinear latent representations and validates its generalizability across different disease cohorts.

2 citations

humanmetabolomics

𝕏 PostBluesky