Author

Tahmid Mehdi

University of Toronto

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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Assay:ChIP-seq· 3 Assay:DNA methylation· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Organism:mouse· 2 Approach:statistical model· 2 Research Focus:clustering· 1 Approach:variational autoencoder· 1

Frequent Co-Authors

Alan M MosesiD· 2 shared Gurdeep SinghiD· 2 shared Jennifer A. MitchelliD· 2 shared Andrew G. Duncan· 1 shared Mathieu LupieniD· 1 shared Paul GuilhamoniD· 1 shared Richard ZhangiD· 1 shared Ruxiao Tian· 1 shared

Activity by Year

2018-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersGenomics

Alan M MosesORCID

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3 papers

Recent·Most cited

A flexible repertoire of transcription factor binding sites and diversity threshold determines enhancer activity in embryonic stem cells

Gurdeep Singh, Shanelle Mullany, Sakthi D. Moorthy, Richard Zhang, Tahmid Mehdi, Ruxiao Tian, Andrew G. Duncan, Alan M Moses, Jennifer A. Mitchell

The paper identifies a diversity threshold of transcription factor binding sites (TFBS) required for enhancer activity in embryonic stem cells, showing that a diverse set of TFBS can replace optimized regulatory sequences.

Genome Research · Apr 1, 2021Peer-reviewedGenomics

Variational Infinite Heterogeneous Mixture Model for Semi-supervised Clustering of Heart Enhancers

Tahmid Mehdi, Gurdeep Singh, Jennifer A. Mitchell, Alan M Moses

The paper introduces a semi-supervised clustering model for identifying active enhancers in mouse heart tissues using heterogeneous genomic data.

Bioinformatics · Feb 6, 2019Peer-reviewedBioinformatics

C3D: A tool to predict 3D genomic interactions between cis-regulatory elements

Tahmid Mehdi, Swneke Donovan Bailey, Paul Guilhamon, Mathieu Lupien

C3D predicts 3D genomic interactions between cis-regulatory elements using DNA accessibility correlations, achieving 32.7% accuracy in K562 cells.

Bioinformatics · Aug 22, 2018Peer-reviewedBioinformatics