Author

Pinki Dey

UNSW SydneyORCID 0000-0001-8822-3599Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Developmental Biology· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 3 Research Focus:gene regulatory network inference· 2 Approach:machine learning (classical)· 2 Research Focus:protein structure prediction· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:experimental assay· 1 Organism:human· 1

Frequent Co-Authors

Abhibhav SharmaiD· 2 shared Anand GururajaniD· 1 shared Brian GlossiD· 1 shared Brooke A. KeatingiD· 1 shared Lee L. MarshalliD· 1 shared Mark Evan GrahamiD· 1 shared Markus J. HoferiD· 1 shared Morgan C. BucknoriD· 1 shared

Activity by Year

2021-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersBioinformatics

Abhibhav SharmaORCID

Author profile →

3 papers

Recent·Most cited

Developmental BiologybioRxivPreprintMar 19, 2025

Cumulative pregnancy and postnatal environmental exposures impact social behaviour in male mice associated with epigenetic, ribosomal, and immune dysregulation

Morgan C. Bucknor, Brooke A. Keating, Velda Xinying Han…·Markus J. Hofer

The paper shows that early life environmental exposures in male mice lead to neurodevelopmental behaviors and molecular changes, while females are unaffected.

3 citations

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BioinformaticsbioRxivPreprintJul 28, 2021

Unravelling the effects of disease-associated mutations in TDP-43 protein via molecular dynamics simulation and machine learning

Abhibhav Sharma, Pinki Dey

The paper uses molecular dynamics simulation and machine learning to analyze disease-associated mutations in TDP-43 and identifies structural features linked to protein stability and pathogenesis.

DOI 10.1101/2021.07.28.454112

protein structuremachine learning (classical)

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GenomicsGenomicsPeer-reviewedApr 18, 2021

A Machine Learning Approach to Unmask Novel Gene Signatures and Prediction of Alzheimers Disease Within Different Brain Regions.

Abhibhav Sharma, Pinki Dey

The paper uses machine learning to identify novel gene signatures and predict Alzheimer's disease in specific brain regions with high accuracy.

46 citationstop 4%

humanbulk RNA-seq

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