Maria Gule-Monroe

82 total papers · 1.5k total citations
42 papers, 827 citations indexed

About

Maria Gule-Monroe is a scholar working on Endocrinology, Diabetes and Metabolism, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Maria Gule-Monroe has authored 42 papers receiving a total of 827 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Endocrinology, Diabetes and Metabolism, 15 papers in Oncology and 13 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Maria Gule-Monroe's work include Thyroid Cancer Diagnosis and Treatment (16 papers), Cancer-related Molecular Pathways (9 papers) and Glioma Diagnosis and Treatment (7 papers). Maria Gule-Monroe is often cited by papers focused on Thyroid Cancer Diagnosis and Treatment (16 papers), Cancer-related Molecular Pathways (9 papers) and Glioma Diagnosis and Treatment (7 papers). Maria Gule-Monroe collaborates with scholars based in United States, Italy and Germany. Maria Gule-Monroe's co-authors include Naifa L. Busaidy, Maria E. Cabanillas, Mark Zafereo, Ramona Dadu, Michelle D. Williams, Renata Ferrarotto, Priyanka Iyer, Neil D. Gross, Jennifer Wang and G. Brandon Gunn and has published in prestigious journals such as Journal of Clinical Oncology, Cancer Research and Radiology.

In The Last Decade

Maria Gule-Monroe

37 papers receiving 820 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Maria Gule-Monroe	533	368	179	136	133	42	827
Priyanka Iyer	616 1.2×	560 1.5×	157 0.9×	208 1.5×	158 1.2×	38	1.0k
Alessia Caleo	470 0.9×	310 0.8×	138 0.8×	271 2.0×	96 0.7×	38	948
Yann Godbert	607 1.1×	224 0.6×	189 1.1×	92 0.7×	142 1.1×	45	832
Sien‐Yi Sheu	510 1.0×	218 0.6×	151 0.8×	262 1.9×	78 0.6×	50	1.0k
Matti L. Gild	496 0.9×	253 0.7×	98 0.5×	206 1.5×	95 0.7×	53	809
Daniela Kodetová	355 0.7×	183 0.5×	92 0.5×	254 1.9×	132 1.0×	35	773
T Brown	442 0.8×	292 0.8×	123 0.7×	261 1.9×	182 1.4×	43	894
R Heimann	170 0.3×	219 0.6×	119 0.7×	237 1.7×	92 0.7×	48	801
Alessandra Bisagni	176 0.3×	281 0.8×	258 1.4×	169 1.2×	51 0.4×	51	752
Arivarasan Karunamurthy	169 0.3×	331 0.9×	140 0.8×	258 1.9×	193 1.5×	48	1.1k

Countries citing papers authored by Maria Gule-Monroe

Since Specialization

Citations

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

Fields of papers citing papers by Maria Gule-Monroe

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maria Gule-Monroe. 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 Maria Gule-Monroe. The network helps show where Maria Gule-Monroe may publish in the future.

Co-authorship network of co-authors of Maria Gule-Monroe

This figure shows the co-authorship network connecting the top 25 collaborators of Maria Gule-Monroe. A scholar is included among the top collaborators of Maria Gule-Monroe 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 Maria Gule-Monroe. Maria Gule-Monroe is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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