Author

M. Mirzaie

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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Biochemistry· 2 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Research Focus:differential expression· 2 Assay:bulk RNA-seq· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Assay:metabolomics· 1

Frequent Co-Authors

M. Jafari· 3 shared A. I. Nieminen· 1 shared A. S. Dibavar· 1 shared C. A. Heckman· 1 shared E. Gholizadeh· 1 shared F. Ianevski· 1 shared H. Rezadoost· 1 shared J. J. Miettinen· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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3 shared papersBiochemistry

M. Jafari

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling

M. Mirzaie, E. Gholizadeh, J. J. Miettinen, T. Ruokoranta, F. Ianevski, J. Saarela, M. Manninen, S. Miettinen, C. A. Heckman, M. Jafari

This paper identifies effective and low-toxicity drug combinations for AML using a bipartite network model and patient-derived data, finding ruxolitinib-ulixertinib and sapanisertib-LY3009120 as the most synergistic.

bioRxiv · May 25, 2023Cancer Biology

Monitoring Functional Post-Translational Modifications Using a Data-Driven Proteome Informatic Pipeline

P. Nickchi, U. Vadadokhau, M. Mirzaie, M. Baumann, A. S. Dibavar, M. Jafari

The paper introduces a PTM-centric proteome informatic pipeline to predict and analyze post-translational modifications in mass spectrometry data, demonstrating its application in glycoproteomics and drug response studies.

bioRxiv · Nov 9, 2022Biochemistry

Amino acids targeted based metabolomics study in non-segmental Vitiligo: a pilot study

R. Marzabani, H. Rezadoost, P. Choopanian, S. Kolahdooz, N. Mozafari, M. Mirzaie, M. Karimi, A. I. Nieminen, M. Jafari

This study uses metabolomics to analyze amino acid profiles in vitiligo patients, identifying biomarkers associated with disease severity and potential pathogenic mechanisms.

bioRxiv · Jan 4, 2021Biochemistry