Author

Hooman Zabeti

Simon Fraser University

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· 3 Systems Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 4 Research Focus:benchmarking· 2 Research Focus:drug response / drug discovery· 2 Approach:deep learning· 1 Organism:human· 1 Assay:metagenomics· 1 Research Focus:variant effect prediction· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Leonid ChindelevitchiD· 3 shared Amir Hosein Safari· 2 shared Nafiseh SedaghatiD· 2 shared Alpha Forna· 1 shared David KoslickiiD· 1 shared Maxwell Libbrecht· 1 shared Maxwell W. LibbrechtiD· 1 shared Nick DexteriD· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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3 shared papersBioinformatics

Leonid ChindelevitchORCID

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Author profile →

4 papers

Recent·Most cited

An interpretable classification method for predicting drug resistance in M. tuberculosis

Hooman Zabeti, Nick Dexter, Amir Hosein Safari, Nafiseh Sedaghat, Maxwell W. Libbrecht, Leonid Chindelevitch

This paper introduces an interpretable classification method for predicting drug resistance in M. tuberculosis, achieving high accuracy and flexibility.

Algorithms for Molecular Biology · Aug 10, 2021Peer-reviewedBioinformatics

Predicting drug resistance in M. tuberculosis using a Long-term Recurrent Convolutional Networks architecture

Amir Hosein Safari, Nafiseh Sedaghat, Hooman Zabeti, Alpha Forna, Leonid Chindelevitch, Maxwell Libbrecht

The paper predicts drug resistance in M. tuberculosis using a gene burden-based approach with a Long-term Recurrent Convolutional Network (LRCN) architecture, showing improved performance over existing methods.

bioRxiv · Nov 7, 2020 · PreprintBioinformatics

MCS^2: Minimal coordinated supports for fast enumeration of minimal cut sets in metabolic networks

Reza Miraskarshahi, Hooman Zabeti, Tamon Stephen, Leonid Chindelevitch

The paper introduces MCS^2, a method to compute minimal cut sets in metabolic networks without requiring elementary flux modes, showing improved efficiency over existing methods.

Bioinformatics · May 9, 2019Peer-reviewedSystems Biology

Improving Min Hash via the Containment Index with applications to Metagenomic Analysis

David Koslicki, Hooman Zabeti

The paper improves min hash for estimating Jaccard similarity in sets of different sizes, showing better accuracy and efficiency for metagenomic analysis.

Applied Mathematics and Computation · Mar 1, 2019Peer-reviewedBioinformatics