Author

Stanley Gill

Harvard UniversityORCID 0000-0001-5430-4550Unclaimed 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:differential expression· 2 Organism:human· 2 Approach:statistical model· 2 Assay:ATAC-seq· 1 Assay:bulk RNA-seq· 1 Research Focus:gene regulatory network inference· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Aissatou Diallo· 1 shared Anne Elizabeth CarpenteriD· 1 shared Aravind SubramanianiD· 1 shared Bin LiuiD· 1 shared Christopher E. HendersoniD· 1 shared Eric C. GriffithiD· 1 shared Erika ReniD· 1 shared Federica PiccioniiD· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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1 shared paperNeuroscience

Aissatou Diallo

Mutual co-authors in cluster

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3 papers

Recent·Most cited

NeuroscienceFrontiers in NeurosciencePeer-reviewedFeb 4, 2026

An induced pluripotent stem cell-based chemical genetic approach for studying spinal muscular atrophy

Richard M. Giadone, Kristina M. Holton, Xiaoyu Hu…·Lee L. Rubin

The paper uses a chemical genetic approach with induced pluripotent stem cells to screen for compounds affecting SMA neuronal gene expression, generating a queryable dataset of transcriptional profiles.

2 citationstop 3%

humanbulk RNA-seq

𝕏 PostBluesky

NeurosciencebioRxivPreprintSep 2, 2024

Cis-regulatory elements driving motor neuron-restricted viral payload expression within the mammalian spinal cord

M. Aurel Nagy, Spencer Price, Kristina Wang…·Siniša Hrvatin

This study identifies cis-regulatory elements that drive motor neuron-specific gene expression in viral vectors, showing selectivity in mice and non-human primates.

3 citations

mouseATAC-seq

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BioinformaticsMolecular Biology of the CellPeer-reviewedFeb 3, 2021

Predicting cell health phenotypes using image-based morphology profiling

Gregory P. Way, Maria Kost‐Alimova, Tsukasa Shibue…·Shantanu Singh

The paper develops a method to predict cell health phenotypes from Cell Painting images using machine learning, validated across multiple perturbations and assays.

145 citationstop 1%

humanimaging

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