Author

Sanjiv Kumar Dwivedi

Linköping UniversityORCID 0000-0003-3400-4133Unclaimed 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:benchmarking· 2 Approach:deep learning· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Assay:bulk RNA-seq· 1 Assay:DNA methylation· 1 Organism:human· 1 Approach:statistical model· 1

Frequent Co-Authors

Mika GustafssoniD· 2 shared Andreas TjärnbergiD· 1 shared David Martínez-EnguitaiD· 1 shared Jesper N. TegneriD· 1 shared Rebecka J. JörnsteniD· 1 shared

Activity by Year

2020-2023 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 6 mutual co-author links

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2 shared papersBioinformatics

Mika GustafssonORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintMar 7, 2023

Data-driven representations using deep network-coherent methylation autoencoders to identify robust disease and risk factor signatures

David Martínez-Enguita, Sanjiv Kumar Dwivedi, Rebecka J. Jörnsten, Mika Gustafsson

This study uses deep autoencoders to analyze DNA methylation data and identifies disease and risk factor signatures related to aging, smoking, and disease.

DOI 10.1101/2023.03.07.531501

humanDNA methylation

𝕏 PostBluesky

Systems BiologyNature CommunicationsPeer-reviewedFeb 12, 2020

Deriving Disease Modules from the Compressed Transcriptional Space Embedded in a Deep Auto-encoder

Sanjiv Kumar Dwivedi, Andreas Tjärnberg, Jesper N. Tegner, Mika Gustafsson

The paper uses a deep auto-encoder to discover disease-related gene modules from transcriptional data without relying on prior biological networks, finding significant enrichment of GWAS genes in the third layer.

50 citations

bulk RNA-seqdeep learning

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