Petia Mitchell

13 papers receiving 375 citations

Peers

Petia Mitchell
Comparison fields: 5 of 42
  • Endocrinology, Diabetes and Metabolism 131
  • Oncology 185
  • Cancer Research 75
  • Genetics 92
  • Immunology 65
Replace Ivo Marchetti with:
Ivo Marchetti Italy
Xingyun Su China
Tomasz Tyszkiewicz Poland
О. С. Ларін United States
Bangbo Zhao China
Monika Kowal Poland
René Scheiden Luxembourg
Nita Williams United States
Javier Aller Pardo Spain
Eleonora Zanetti Italy
Petia Mitchell relative to Ivo Marchetti Italy Ivo Marchetti's profile →
Citations per field
00.5×4.8×
Ivo Marchetti · 1×
Citations per year

Countries citing papers authored by Petia Mitchell

Since Specialization
Citations

This map shows the geographic impact of Petia Mitchell'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 Petia Mitchell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Petia Mitchell more than expected).

Fields of papers citing papers by Petia Mitchell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Petia Mitchell. 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 Petia Mitchell. The network helps show where Petia Mitchell may publish in the future.

Co-authors

The 25 scholars most cited alongside Petia Mitchell, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Petia Mitchell Line = papers co-authored together Petia Mitchell links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 2009113
2 201781
3 201165
4 201441
5 201328
6 201419
7 20238
8 20157
9
AMG 479, a fully human anti IGF-1 receptor monoclonal antibody, is efficacious against Ewing’s sarcoma and osteosarcoma xenografts
20076
10
Inhibition of endocrine IGF-1 signaling in normal murine tissues and human tumor xenografts with AMG 479, a fully human anti IGF-1R monoclonal antibody
20074
11 20164
12 20153
13 20091

About Petia Mitchell

Petia Mitchell is a scholar working on Cancer Research, Endocrinology, Diabetes and Metabolism, Molecular Biology, Genetics and Immunology, having authored 13 papers that have together received 380 indexed citations. Recurring topics across this work include Cancer, Hypoxia, and Metabolism (8 papers), Growth Hormone and Insulin-like Growth Factors (7 papers), Virus-based gene therapy research (4 papers), Metabolism, Diabetes, and Cancer (3 papers), Immunotherapy and Immune Responses (3 papers), CAR-T cell therapy research (2 papers), Herpesvirus Infections and Treatments (2 papers) and Sarcoma Diagnosis and Treatment (2 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (131 citations), Oncology (185 citations), Cancer Research (75 citations), Genetics (92 citations) and Immunology (65 citations). Petia Mitchell has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Pedro J. Beltran, Frank J. Calzone, Elaina Cajulis, Robert Radinsky, Richard Kendall, Steven Vonderfecht, Brian Belmontes, Gordon Moody, John Lu and Joanne Ho. Their work appears in journals such as Molecular Cancer Therapeutics, Journal for ImmunoTherapy of Cancer, Clinical Cancer Research, Journal of Endocrinology and Journal of Pharmacology and Experimental Therapeutics.

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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