Author

Michael Domaratzki

University of ManitobaORCID 0000-0001-9129-6676Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:whole-genome sequencing· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:gene regulatory network inference· 1 Approach:graph neural network· 1 Assay:GWAS summary statistics· 1

Frequent Co-Authors

Amir MorshedianiD· 2 shared Aleisha R. ReimeriD· 1 shared Cecile L. TremblayiD· 1 shared Chrystal L. BerryiD· 1 shared Eric Marinier· 1 shared Franco PagottoiD· 1 shared Gary Van DomselaariD· 1 shared J B Mahony· 1 shared

Activity by Year

2017-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersBioinformatics

Amir MorshedianORCID

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4 papers

Recent·Most cited

Learning Environment-Associated Marker Rankings with Attention-Based Graph Neural Networks

Amir Morshedian, Otilia Pasculescu, Michael Domaratzki

This paper introduces a graph neural network to estimate environment-associated marker rankings from crop trial data and evaluates its performance on maize datasets.

bioRxiv · Sep 25, 2026 · PreprintBioinformatics

LSTM-Attention-Guided Graph Neural Networks for Integrated Genotype-Environment Modeling in Maize Yield Prediction

Amir Morshedian, Michael Domaratzki

The paper introduces a deep-learning framework combining LSTM, GNN, and attention for maize yield prediction by modeling genotype-environment interactions, showing improved performance with a novel architecture.

PLoS Computational Biology · May 20, 2026Peer-reviewedBioinformatics

Sparse Bayesian learning for predicting phenotypes and ranking influential markers in yeast

Maryam Ayat, Michael Domaratzki

This paper applies sparse Bayesian learning to predict yeast phenotypes and rank influential markers, showing it performs as well as other methods and identifies key markers affecting growth.

bioRxiv · Dec 6, 2018 · PreprintBioinformatics

Neptune: A Bioinformatics Tool for Rapid Discovery of Genomic Variation in Bacterial Populations

Eric Marinier, Rahat Zaheer, Chrystal L. Berry, Kelly A. Weedmark, Michael Domaratzki, Philip Mabon, Natalie Knox, Aleisha R. Reimer, Morag Graham, Linda Chui, Laura M. Patterson-Fortin, Jian Zhang, Franco Pagotto, Jeff Farber, J B Mahony, , Gary Van Domselaar

Neptune identifies differentially abundant genomic loci in bacterial populations using k-mer matching and probabilistic models, showing sensitivity and specificity on simulated and real data.

Nucleic Acids Research · Aug 1, 2017Peer-reviewedBioinformatics