Author

Mehdi Foroozandeh Shahraki

Simon Fraser UniversityORCID 0000-0001-8708-5366Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:ChIP-seq· 3 Research Focus:benchmarking· 2 Approach:statistical model· 2 Assay:ATAC-seq· 1 Approach:contrastive learning· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Maxwell W. LibbrechtiD· 3 shared Abdul Rahman DiabiD· 2 shared Marjan FarahbodiD· 2 shared Amin Boroomand· 1 shared Benjamin C. HitziD· 1 shared Ghasem Hosseini SalekdehiD· 1 shared Habib DaneshpajouhiD· 1 shared Ian WhalingiD· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersGenomics

Maxwell W. LibbrechtORCID

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Author profile →

4 papers

Recent·Most cited

CANDI: self-supervised, confidence-aware denoising imputation of genomic data

Mehdi Foroozandeh Shahraki, Abdul Rahman Diab, Maxwell W. Libbrecht

CANDI improves epigenome imputation by predicting raw counts, handling covariates, and providing uncertainty measures using a self-supervised Transformer model.

bioRxiv · Jan 23, 2025 · PreprintGenomics

Robust chromatin state annotation

Mehdi Foroozandeh Shahraki, Marjan Farahbod, Maxwell W. Libbrecht

The paper evaluates the reproducibility of SAGA chromatin state annotations and introduces SAGAconf to assign confidence scores to these annotations.

Genome Research · Mar 21, 2024Peer-reviewedGenomics

Integrative chromatin state annotation of 234 human ENCODE4 cell types using Segway reveals disease drivers

Marjan Farahbod, Abdul Rahman Diab, Paul Sud, Meenakshi S. Kagda, Ian Whaling, Mehdi Foroozandeh Shahraki, Ishan Goel, Habib Daneshpajouh, Benjamin C. Hitz, J. Michael Cherry, Maxwell W. Libbrecht

The paper presents ENCODE4 Segway annotations for 234 human cell types, improving sensitivity in capturing regulatory patterns relevant to disease and traits.

bioRxiv · Oct 26, 2023 · PreprintGenomics

A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: a xylanase temperature dependence case study

Mehdi Foroozandeh Shahraki, Kiana Farhadyar, Kaveh Kavousi, Mohammad Hadi Azarabad, Amin Boroomand, Shohreh Ariaeenejad, Ghasem Hosseini Salekdeh

This paper develops a machine learning method to predict the thermal dependence of xylanases from metagenomic data, identifying three novel xylanases from rumen microbiota.

Biotechnology and Bioengineering · Oct 23, 2020Peer-reviewedBioinformatics