Ash A. Alizadeh

75.6k total citations · 19 hit papers
251 papers, 35.5k citations indexed

About

Ash A. Alizadeh is a scholar working on Molecular Biology, Cancer Research and Pathology and Forensic Medicine. According to data from OpenAlex, Ash A. Alizadeh has authored 251 papers receiving a total of 35.5k indexed citations (citations by other indexed papers that have themselves been cited), including 92 papers in Molecular Biology, 91 papers in Cancer Research and 84 papers in Pathology and Forensic Medicine. Recurrent topics in Ash A. Alizadeh's work include Cancer Genomics and Diagnostics (86 papers), Lymphoma Diagnosis and Treatment (76 papers) and CAR-T cell therapy research (29 papers). Ash A. Alizadeh is often cited by papers focused on Cancer Genomics and Diagnostics (86 papers), Lymphoma Diagnosis and Treatment (76 papers) and CAR-T cell therapy research (29 papers). Ash A. Alizadeh collaborates with scholars based in United States, Iran and Germany. Ash A. Alizadeh's co-authors include Chih Long Liu, Aaron M. Newman, Maximilian Diehn, Andrew J. Gentles, Chuong D. Hoang, Weiguo Feng, Yue Xu, Michael R. Green, Michael S. Khodadoust and Patrick O. Brown and has published in prestigious journals such as New England Journal of Medicine, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Ash A. Alizadeh

232 papers receiving 35.0k citations

Hit Papers

Robust enumeration of cell subsets... 1999 2026 2008 2017 2015 2018 2019 2015 2014 2.5k 5.0k 7.5k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Robust enumeration of cell subsets from tissue expression profiles
    2015 8.3k citations Aaron M. Newman, Chih Long Liu et al. Nature Methods profile →
  2. Profiling Tumor Infiltrating Immune Cells with CIBERSORT
    2018 2.4k citations Binbin Chen, Michael S. Khodadoust et al. profile →
  3. Determining cell type abundance and expression from bulk tissues with digital cytometry
    2019 2.4k citations Aaron M. Newman, Chloé B. Steen et al. Nature Biotechnology profile →
  4. The prognostic landscape of genes and infiltrating immune cells across human cancers
    2015 2.2k citations Andrew J. Gentles, Aaron M. Newman et al. profile →
  5. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage
    2014 1.5k citations Aaron M. Newman, Scott V. Bratman et al. profile →
  6. CD47 Is an Adverse Prognostic Factor and Therapeutic Antibody Target on Human Acute Myeloid Leukemia Stem Cells
    2009 1.3k citations Ravindra Majeti, Mark P. Chao et al. profile →
  7. Genome-wide analysis of DNA copy-number changes using cDNA microarrays
    1999 1.1k citations Jonathan R. Pollack, Charles M. Perou et al. profile →
  8. Anti-CD47 Antibody Synergizes with Rituximab to Promote Phagocytosis and Eradicate Non-Hodgkin Lymphoma
    2010 845 citations Mark P. Chao, Ash A. Alizadeh et al. profile →
  9. Relation of Gene Expression Phenotype to Immunoglobulin Mutation Genotype in B Cell Chronic Lymphocytic Leukemia
    2001 815 citations Ash A. Alizadeh, David Botstein et al. profile →
  10. Integrated digital error suppression for improved detection of circulating tumor DNA
    2016 716 citations Aaron M. Newman, Alexander F. Lovejoy et al. Nature Biotechnology profile →
  11. Gene Expression Signature of Fibroblast Serum Response Predicts Human Cancer Progression: Similarities between Tumors and Wounds
    2004 711 citations Ash A. Alizadeh, Robert B. West et al. profile →
  12. Prediction of Survival in Diffuse Large-B-Cell Lymphoma Based on the Expression of Six Genes
    2004 708 citations Debra K. Czerwinski, Ash A. Alizadeh et al. profile →
  13. Individuality and variation in gene expression patterns in human blood
    2003 618 citations Maximilian Diehn, Ash A. Alizadeh et al. profile →
  14. Calreticulin Is the Dominant Pro-Phagocytic Signal on Multiple Human Cancers and Is Counterbalanced by CD47
    2010 594 citations Mark P. Chao, Rachel Weissman-Tsukamoto et al. profile →
  15. Genome-wide analysis of DNA copy number variation in breast cancer using DNA microarrays
    1999 531 citations Jonathan R. Pollack, Charles M. Perou et al. profile →
  16. From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment
    2023 282 citations Ash A. Alizadeh et al. profile →
  17. Profiling Cell Type Abundance and Expression in Bulk Tissues with CIBERSORTx
    2020 275 citations Chloé B. Steen, Chih Long Liu et al. profile →
  18. Postbiotics produced by lactic acid bacteria: The next frontier in food safety
    2020 247 citations Ash A. Alizadeh et al. profile →
  19. Risk of Second Tumors and T-Cell Lymphoma after CAR T-Cell Therapy
    2024 93 citations Chih Long Liu, Maximilian Diehn et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ash A. Alizadeh United States 66 15.5k 11.0k 10.6k 9.5k 8.6k 251 35.5k
Maximilian Diehn United States 61 12.2k 0.8× 9.2k 0.8× 6.3k 0.6× 9.8k 1.0× 9.6k 1.1× 266 27.8k
Scott J. Rodig United States 85 10.5k 0.7× 18.7k 1.7× 10.9k 1.0× 4.4k 0.5× 6.9k 0.8× 347 33.8k
Gad Getz United States 74 27.7k 1.8× 10.6k 1.0× 5.0k 0.5× 18.7k 2.0× 8.1k 0.9× 225 42.6k
Aravind Subramanian United States 29 29.6k 1.9× 7.6k 0.7× 7.2k 0.7× 9.9k 1.0× 7.1k 0.8× 53 45.9k
William C. Hahn United States 101 26.5k 1.7× 11.2k 1.0× 4.7k 0.4× 7.8k 0.8× 4.9k 0.6× 293 40.9k
David L. Rimm United States 102 17.9k 1.2× 19.9k 1.8× 7.0k 0.7× 8.2k 0.9× 7.9k 0.9× 547 41.4k
H. Phillip Koeffler United States 101 22.4k 1.4× 9.9k 0.9× 5.6k 0.5× 6.6k 0.7× 4.3k 0.5× 666 42.0k
Erik Larsson Sweden 56 21.9k 1.4× 8.3k 0.8× 3.7k 0.3× 9.7k 1.0× 6.9k 0.8× 208 34.1k
Robert S. Kerbel Canada 99 24.5k 1.6× 16.5k 1.5× 5.5k 0.5× 13.4k 1.4× 5.8k 0.7× 402 41.7k
Amanda G. Paulovich United States 40 26.0k 1.7× 7.0k 0.6× 6.3k 0.6× 8.3k 0.9× 5.6k 0.7× 87 40.1k

Countries citing papers authored by Ash A. Alizadeh

Since Specialization
Citations

This map shows the geographic impact of Ash A. Alizadeh's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ash A. Alizadeh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ash A. Alizadeh more than expected).

Fields of papers citing papers by Ash A. Alizadeh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ash A. Alizadeh. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ash A. Alizadeh. The network helps show where Ash A. Alizadeh may publish in the future.

Co-authorship network of co-authors of Ash A. Alizadeh

This figure shows the co-authorship network connecting the top 25 collaborators of Ash A. Alizadeh. A scholar is included among the top collaborators of Ash A. Alizadeh based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ash A. Alizadeh. Ash A. Alizadeh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Goldstein, Jordan, Mark Roschewski, Won-Seog Kim, et al.. (2024). Baseline Prognostic Factors Do Not Predict End of Treatment Ctdna MRD Status and Have Limited Impact on MRD Prognostic Performance in DLBCL. Blood. 144(Supplement 1). 651–651.
2.
3.
Nagy, Ákos, Gregory J. Hogan, Jacob J. Chabon, et al.. (2023). Phased Variants Allow Robust Profiling of Circulating Tumor DNA in Untreated Follicular Lymphomas. Blood. 142(Supplement 1). 1626–1626. 1 indexed citations
4.
Roschewski, Mark, David M. Kurtz, Jason R. Westin, et al.. (2023). MRD‐NEGATIVITY AFTER FRONTLINE DLBCL THERAPY: POOLED ANALYSIS OF 6 CLINICAL TRIALS. Hematological Oncology. 41(S2). 177–179. 5 indexed citations
5.
Shree, Tanaya, Vishnu Shankar, Debra K. Czerwinski, et al.. (2022). CD20-Targeted Therapy Ablates De Novo Antibody Response to Vaccination but Spares Preestablished Immunity. Blood Cancer Discovery. 3(2). 95–102. 27 indexed citations
6.
Alizadeh, Ash A., Peyman Akbari, Johan Garssen, Johanna Fink‐Gremmels, & Saskia Braber. (2021). Epithelial integrity, junctional complexes, and biomarkers associated with intestinal functions. Tissue Barriers. 10(3). 1996830–1996830. 51 indexed citations
7.
Steen, Chloé B., Bogdan Luca, Mohammad Shahrokh Esfahani, et al.. (2021). The landscape of tumor cell states and ecosystems in diffuse large B cell lymphoma. Cancer Cell. 39(10). 1422–1437.e10. 149 indexed citations
8.
Accomando, William P., Daniel J. Hogan, Aaron M. Newman, et al.. (2020). Molecular and Immunologic Signatures are Related to Clinical Benefit from Treatment with Vocimagene Amiretrorepvec (Toca 511) and 5-Fluorocytosine (Toca FC) in Patients with Glioma. Clinical Cancer Research. 26(23). 6176–6186. 17 indexed citations
9.
Chen, Binbin, Michael S. Khodadoust, Niclas Olsson, et al.. (2019). Predicting HLA class II antigen presentation through integrated deep learning. Nature Biotechnology. 37(11). 1332–1343. 209 indexed citations
10.
Chen, Binbin, Michael S. Khodadoust, Niclas Olsson, et al.. (2019). Maria-I: A Deep-Learning Approach for Accurate Prediction of MHC Class I Tumor Neoantigen Presentation. Blood. 134(Supplement_1). 84–84. 4 indexed citations
11.
Dudley, Jonathan C., Joseph G. Schroers‐Martin, Daniel Lazzareschi, et al.. (2018). Detection and Surveillance of Bladder Cancer Using Urine Tumor DNA. Cancer Discovery. 9(4). 500–509. 151 indexed citations
12.
Przybył, Joanna, Jacob J. Chabon, Lien Spans, et al.. (2018). Combination Approach for Detecting Different Types of Alterations in Circulating Tumor DNA in Leiomyosarcoma. Clinical Cancer Research. 24(11). 2688–2699. 44 indexed citations
13.
Alizadeh, Ash A., Saskia Braber, Peyman Akbari, et al.. (2016). Deoxynivalenol and Its Modified Forms: Are There Major Differences?. Toxins. 8(11). 334–334. 41 indexed citations
14.
Jeong, Youngtae, Ngoc T. Hoang, Alexander F. Lovejoy, et al.. (2016). Role of KEAP1 / NRF2 and TP53 Mutations in Lung Squamous Cell Carcinoma Development and Radiation Resistance. Cancer Discovery. 7(1). 86–101. 243 indexed citations
15.
Newman, Aaron M., Chih Long Liu, Michael R. Green, et al.. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature Methods. 12(5). 453–457. 8298 indexed citations breakdown →
16.
Green, Michael R., Scott V. Bratman, Chih Long Liu, et al.. (2013). Utility Of Non-Invasive Monitoring Of Circulating Tumor DNA At Diagnosis, Interim Therapy, and Relapse Of DLBCL Using High-Throughput Sequencing Of Immunoglobulin Genes. Blood. 122(21). 4264–4264. 2 indexed citations
17.
McClellan, James Scott, Holbrook E. Kohrt, Steven Coutré, et al.. (2011). Treatment advances have not improved the early death rate in acute promyelocytic leukemia. Haematologica. 97(1). 133–136. 101 indexed citations
18.
Chao, Mark P., Ash A. Alizadeh, Chad Tang, et al.. (2010). Therapeutic Antibody Targeting of CD47 Eliminates Human Acute Lymphoblastic Leukemia. Cancer Research. 71(4). 1374–1384. 294 indexed citations
19.
Alizadeh, Ash A. & Amir Shabani. (2008). The specific pattern of obsessive-compulsive symptoms in patients with bipolar disorder. SHILAP Revista de lepidopterología. 10 indexed citations
20.
Botstein, David, Ash A. Alizadeh, Joseph L. DeRisi, et al.. (1998). DNA MICROARRAYS AS "MICROSCOPES" FOR WATCHING A GENOME IN ACTION. 21. 172. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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