SH
Soroor Hediyeh-zadeh
Also known as: Soroor Zadeh
Helmholtz Munich, Broad Institute, The University of Melbourne, Walter and Eliza Hall Institute of Medical Research
Research areas: Medicine · Oncology · Cancer Immunotherapy and Biomarkers · Developmental Biology and Gene Regulation · Cancer Cells and Metastasis
0000-0001-7513-6779
Employment
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Broad Institute Visiting Graduate Student2024 - 2024
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Helmholtz Zentrum München Graduate researcher2021 - Present
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Walter and Eliza Hall Institute of Medical Research Research Assistant2016 - 2020
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University of Melbourne Research Technician2014 - 2016
Education
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Helmholtz Zentrum München Doctor of Philosophy2021 - Present
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The University of Melbourne Master of Philosophy (MPhil)2020 - 2022
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University of Melbourne Master of Science (Mathematics and Statistics)2017 - 2019
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University of Melbourne Bachelor of Science (Mathematics and Statistics)2012 - 2015
Projects & Funding
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PIPP: Improving peptide identity propagation using neural networks Colonial Foundation
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Mechanism transport between multiple data modalities using causality-inspired neural networks German Academic Exchange Service (DAAD)
Publications (53)
- Perturbation-guided mapping of colorectal cancer cell states to causal mechanisms Save
- Weakly supervised learning uncovers phenotypic signatures in single-cell data Save
- GraphCompass: spatial metrics for differential analyses of cell organization across conditions Save
- ERG and c-MYC regulate a critical gene network in BCR::ABL1-driven B cell acute lymphoblastic leukemia Save
- Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR Save
- GraphCompass: Spatial metrics for differential analyses of cell organization across conditions Save
- Population-level integration of single-cell datasets enables multi-scale analysis across samples Save
- Identification of cell types, states and programs by learning gene set representations Save
- MsImpute: Estimation of Missing Peptide Intensity Data in Label-Free Quantitative Mass Spectrometry Save
- Data from A Gene Signature Predicting Natural Killer Cell Infiltration and Improved Survival in Melanoma Patients Save
- Data from A Gene Signature Predicting Natural Killer Cell Infiltration and Improved Survival in Melanoma Patients Save
- Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR Save
- Supplementary Figures 1-9 and Table 2 from A Gene Signature Predicting Natural Killer Cell Infiltration and Improved Survival in Melanoma Patients Save
- Supplementary Figures 1-9 and Table 2 from A Gene Signature Predicting Natural Killer Cell Infiltration and Improved Survival in Melanoma Patients Save
- Supplementary Table 1 from A Gene Signature Predicting Natural Killer Cell Infiltration and Improved Survival in Melanoma Patients Save
- Supplementary Table 1 from A Gene Signature Predicting Natural Killer Cell Infiltration and Improved Survival in Melanoma Patients Save
- Data from SOX9 Defines Distinct Populations of Cells in SHH Medulloblastoma but Is Not Required for Math1-Driven Tumor Formation Save
- Data from SOX9 Defines Distinct Populations of Cells in SHH Medulloblastoma but Is Not Required for Math1-Driven Tumor Formation Save
- Supplementary Figure 1 from SOX9 Defines Distinct Populations of Cells in SHH Medulloblastoma but Is Not Required for Math1-Driven Tumor Formation Save
- Supplementary Figure 1 from SOX9 Defines Distinct Populations of Cells in SHH Medulloblastoma but Is Not Required for Math1-Driven Tumor Formation Save