Daniela Gallo

69 papers receiving 2.8k citations

Peers

Daniela Gallo
Comparison fields: 5 of 116
  • Reproductive Medicine 422
  • Oncology 1.0k
  • Cancer Research 431
  • Cell Biology 460
  • Pathology and Forensic Medicine 474
Replace Paweł Surowiak with:
Paweł Surowiak Poland
Charlotta Dabrosin Sweden
Anni Wärri Finland
Peeter Karihtala Finland
Insoo Bae United States
Zhaojian Liu China
Wilma M. McKoy United States
Ramesh Narayanan United States
Shin‐ichi Hayashi Japan
Suzanne E. Wardell United States
Daniela Gallo relative to Paweł Surowiak Poland Paweł Surowiak's profile →
Citations per field
00.5×1.5×2.3×
Paweł Surowiak · 1×
Citations per year

Countries citing papers authored by Daniela Gallo

Since Specialization
Citations

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

Fields of papers citing papers by Daniela Gallo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniela Gallo, 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 Daniela Gallo Line = papers co-authored together Daniela Gallo links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 72 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Class III beta-tubulin overexpression is a prominent mechanism of paclitaxel resistance in ovarian cancer patients.
2005286
2 2005270
3 2000187
4 2009132
5 2009127
6 2003122
7 200593
8 200975
9 201169
10 200369
11 200269
12 202157
13 201455
14 200854
15 200852
16 201051
17 201250
18 201044
19 201343
20 199943

About Daniela Gallo

Daniela Gallo is a scholar working on Molecular Biology, Oncology, Genetics, Reproductive Medicine and Pathology and Forensic Medicine, having authored 72 papers that have together received 2.9k indexed citations. Recurring topics across this work include Estrogen and related hormone effects (20 papers), Ovarian cancer diagnosis and treatment (19 papers), Cancer Treatment and Pharmacology (11 papers), Microtubule and mitosis dynamics (11 papers), Phytoestrogen effects and research (11 papers), Endometrial and Cervical Cancer Treatments (7 papers), Cancer Risks and Factors (6 papers) and Cancer Mechanisms and Therapy (5 papers). The work is most often cited by research in Reproductive Medicine (422 citations), Oncology (1.0k citations), Cancer Research (431 citations), Cell Biology (460 citations) and Pathology and Forensic Medicine (474 citations). Daniela Gallo has collaborated with scholars based in Italy, Japan and United States. Frequent co-authors include Giovanni Scambia, Cristiano Ferlini, Giuseppina Raspaglio, Simona Mozzetti, Gian Franco Zannoni, Gabriella Ferrandina, Flavia Filippetti, Enrica Martinelli, Silvia Prislei and Franco O. Ranelletti. Their work appears in journals such as Gynecologic Oncology, Cancers, Menopause The Journal of The North American Menopause Society, Journal of Experimental & Clinical Cancer Research and Histopathology.

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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