C Torrance

1.9k total citations
35 papers, 1.6k citations indexed

About

C Torrance is a scholar working on Molecular Biology, Oncology and Occupational Therapy. According to data from OpenAlex, C Torrance has authored 35 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 7 papers in Oncology and 6 papers in Occupational Therapy. Recurrent topics in C Torrance's work include Advanced biosensing and bioanalysis techniques (5 papers), Pressure Ulcer Prevention and Management (4 papers) and Microtubule and mitosis dynamics (3 papers). C Torrance is often cited by papers focused on Advanced biosensing and bioanalysis techniques (5 papers), Pressure Ulcer Prevention and Management (4 papers) and Microtubule and mitosis dynamics (3 papers). C Torrance collaborates with scholars based in United Kingdom, United States and Australia. C Torrance's co-authors include Bert Vogelstein, Kenneth W. Kinzler, Peta E. Jackson, Philip Frost, Maria Nunes, Carolyn Discafani, Allan Wissner, Elizabeth A. Montgomery, Patricia Lyne and G. Lynis Dohm and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Medicine and Nature Biotechnology.

In The Last Decade

C Torrance

34 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
C Torrance United Kingdom 22 722 441 189 188 160 35 1.6k
Julie Beitz United States 20 697 1.0× 493 1.1× 114 0.6× 151 0.8× 127 0.8× 39 2.0k
Eugene Chang United States 26 788 1.1× 429 1.0× 191 1.0× 97 0.5× 165 1.0× 50 1.8k
Susan Kilroy United States 11 982 1.4× 242 0.5× 441 2.3× 132 0.7× 114 0.7× 24 1.7k
W J Nooijen Netherlands 19 528 0.7× 1.3k 2.9× 187 1.0× 62 0.3× 158 1.0× 29 1.9k
Yvonne Lo Hong Kong 17 776 1.1× 311 0.7× 206 1.1× 115 0.6× 69 0.4× 30 2.0k
Donna S. Cox United States 26 881 1.2× 739 1.7× 234 1.2× 214 1.1× 240 1.5× 55 2.4k
Shingo Hatakeyama Japan 22 542 0.8× 264 0.6× 134 0.7× 42 0.2× 523 3.3× 176 1.8k
Floris A. de Jong Netherlands 23 648 0.9× 1.2k 2.6× 121 0.6× 229 1.2× 331 2.1× 54 1.9k
N Brock Germany 26 536 0.7× 509 1.2× 243 1.3× 59 0.3× 222 1.4× 76 2.2k
Ashok Rakhit Switzerland 25 518 0.7× 981 2.2× 98 0.5× 133 0.7× 617 3.9× 65 2.2k

Countries citing papers authored by C Torrance

Since Specialization
Citations

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

Fields of papers citing papers by C Torrance

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of C Torrance

This figure shows the co-authorship network connecting the top 25 collaborators of C Torrance. A scholar is included among the top collaborators of C Torrance 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 C Torrance. C Torrance 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.
Watt, Paul M., Vikrant Kumar, Kavitha Bharatham, et al.. (2021). Target identification for small-molecule discovery in the FOXO3a tumor-suppressor pathway using a biodiverse peptide library. Cell chemical biology. 28(11). 1602–1615.e9. 8 indexed citations
2.
Cairney, Claire J., Alan Bilsland, Sharon Burns, et al.. (2017). A ‘synthetic-sickness’ screen for senescence re-engagement targets in mutant cancer backgrounds. PLoS Genetics. 13(8). e1006942–e1006942. 10 indexed citations
3.
Narvaez, A.J., Suzan Ber, Estrella Guarino, et al.. (2017). Modulating Protein-Protein Interactions of the Mitotic Polo-like Kinases to Target Mutant KRAS. Cell chemical biology. 24(8). 1017–1028.e7. 23 indexed citations
4.
Costa-Cabral, Sara, Rachel Brough, Asha Konde, et al.. (2016). CDK1 Is a Synthetic Lethal Target for KRAS Mutant Tumours. PLoS ONE. 11(2). e0149099–e0149099. 58 indexed citations
5.
Sadaie, Mahito, Christian Dillon, Masashi Narita, et al.. (2015). Cell-based screen for altered nuclear phenotypes reveals senescence progression in polyploid cells after Aurora kinase B inhibition. Molecular Biology of the Cell. 26(17). 2971–2985. 39 indexed citations
6.
Glaysher, Sharon, et al.. (2014). Activity of EGFR, mTOR and PI3K inhibitors in an isogenic breast cell line model. BMC Research Notes. 7(1). 397–397. 15 indexed citations
7.
Glaysher, Sharon, et al.. (2013). Targeting EGFR and PI3K pathways in ovarian cancer. British Journal of Cancer. 109(7). 1786–1794. 53 indexed citations
8.
Torrance, C, Ian Mansell, & Christine Wilson. (2012). Learning Objects? Nurse Educators′ Views on Using Patients for Student Learning : Ethics and Consent. Education for Health. 25(2). 92–92. 12 indexed citations
9.
Cairney, Claire J., Alan Bilsland, T.R. Jeffry Evans, et al.. (2012). Cancer cell senescence: a new frontier in drug development. Drug Discovery Today. 17(5-6). 269–276. 44 indexed citations
10.
Wallin, Jeffrey J., Jane Guan, Kyle A. Edgar, et al.. (2012). Active PI3K Pathway Causes an Invasive Phenotype Which Can Be Reversed or Promoted by Blocking the Pathway at Divergent Nodes. PLoS ONE. 7(5). e36402–e36402. 39 indexed citations
11.
Richardson, Christine M., D.S. Williamson, Martin J. Parratt, et al.. (2007). Discovery of a potent CDK2 inhibitor with a novel binding mode, using virtual screening and initial, structure-guided lead scoping. Bioorganic & Medicinal Chemistry Letters. 17(14). 3880–3885. 38 indexed citations
12.
Richardson, Christine M., D.S. Williamson, Martin J. Parratt, et al.. (2005). Triazolo[1,5-a]pyrimidines as novel CDK2 inhibitors: Protein structure-guided design and SAR. Bioorganic & Medicinal Chemistry Letters. 16(5). 1353–1357. 56 indexed citations
13.
Zawel, Leigh, Jian Yu, C Torrance, et al.. (2002). DEC1 is a downstream target of TGF-β with sequence-specific transcriptional repressor activities. Proceedings of the National Academy of Sciences. 99(5). 2848–2853. 81 indexed citations
14.
Torrance, C, et al.. (2001). Use of isogenic human cancer cells for high-throughput screening and drug discovery. Nature Biotechnology. 19(10). 940–945. 194 indexed citations
15.
Shih, Ie−Ming, C Torrance, Lori J. Sokoll, et al.. (2000). Assessing tumors in living animals through measurement of urinary β-human chorionic gonadotropin. Nature Medicine. 6(6). 711–714. 36 indexed citations
16.
Torrance, C, et al.. (1999). Pressure sore survey Part 2: nurses' knowledge. Journal of Wound Care. 8(2). 49–52. 31 indexed citations
17.
Torrance, C, et al.. (1999). Pressure sore survey: part one. Journal of Wound Care. 8(1). 27–30. 26 indexed citations
18.
Torrance, C. (1997). Effects of Thyroid Hormone on GLUT4 Glucose Transporter Gene Expression and NIDDM in Rats. Endocrinology. 138(3). 1204–1214. 32 indexed citations
19.
Torrance, C, James DeVente, Jared P. Jones, & G. Lynis Dohm. (1997). Effects of Thyroid Hormone on GLUT4 Glucose Transporter Gene Expression and NIDDM in Rats. Endocrinology. 138(3). 1204–1214. 93 indexed citations
20.
Torrance, C. (1986). The physiology of wound healing.. PubMed. 3(5). 162–98. 4 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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