Daniel W. Chan

16.5k total citations · 3 hit papers
161 papers, 9.4k citations indexed

About

Daniel W. Chan is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine and Spectroscopy. According to data from OpenAlex, Daniel W. Chan has authored 161 papers receiving a total of 9.4k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Molecular Biology, 51 papers in Pulmonary and Respiratory Medicine and 40 papers in Spectroscopy. Recurrent topics in Daniel W. Chan's work include Prostate Cancer Diagnosis and Treatment (41 papers), Prostate Cancer Treatment and Research (40 papers) and Advanced Proteomics Techniques and Applications (39 papers). Daniel W. Chan is often cited by papers focused on Prostate Cancer Diagnosis and Treatment (41 papers), Prostate Cancer Treatment and Research (40 papers) and Advanced Proteomics Techniques and Applications (39 papers). Daniel W. Chan collaborates with scholars based in United States, Netherlands and Canada. Daniel W. Chan's co-authors include Lori J. Sokoll, Peter B. Luppa, Danni Li, Alan W. Partin, Martin G. Sanda, Leong L. Ng, Eric T. Fung, Hui Zhang, Zhen Zhang and Harry G. Rittenhouse and has published in prestigious journals such as New England Journal of Medicine, Circulation and Nature Medicine.

In The Last Decade

Daniel W. Chan

156 papers receiving 9.1k citations

Hit Papers

Three Biomarkers Identified from Serum Proteomic Analysis... 2001 2026 2009 2017 2004 2008 2001 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel W. Chan United States 50 4.1k 2.6k 2.0k 1.4k 1.1k 161 9.4k
Lori J. Sokoll United States 54 3.8k 0.9× 4.6k 1.8× 1.1k 0.5× 3.3k 2.4× 2.3k 2.0× 231 12.4k
Kim Pettersson Finland 58 3.4k 0.8× 3.6k 1.4× 492 0.2× 905 0.6× 956 0.8× 291 10.9k
Rosamonde E. Banks United Kingdom 51 5.1k 1.2× 1.4k 0.5× 1.7k 0.9× 1.6k 1.1× 1.5k 1.3× 162 8.8k
George L. Wright United States 42 2.5k 0.6× 2.2k 0.8× 1.7k 0.9× 461 0.3× 837 0.7× 135 6.8k
Theo M. Luider Netherlands 48 4.8k 1.2× 695 0.3× 1.9k 0.9× 1.3k 0.9× 958 0.8× 268 8.1k
John A. Petros United States 46 4.9k 1.2× 3.3k 1.3× 310 0.2× 1.8k 1.2× 1.3k 1.1× 133 10.9k
Bruce Furie United States 72 5.6k 1.4× 1.9k 0.7× 527 0.3× 1.6k 1.1× 1.3k 1.2× 238 19.1k
Barbara C. Furie United States 58 4.1k 1.0× 1.6k 0.6× 398 0.2× 1.3k 0.9× 1.0k 0.9× 156 14.8k
Dan Theodorescu United States 69 7.5k 1.8× 3.1k 1.2× 739 0.4× 2.8k 2.0× 3.6k 3.1× 335 14.6k
Helmut Klocker Austria 67 6.9k 1.7× 6.6k 2.5× 495 0.2× 3.1k 2.2× 3.0k 2.6× 364 15.1k

Countries citing papers authored by Daniel W. Chan

Since Specialization
Citations

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

Fields of papers citing papers by Daniel W. Chan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel W. Chan. 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 Daniel W. Chan. The network helps show where Daniel W. Chan may publish in the future.

Co-authorship network of co-authors of Daniel W. Chan

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel W. Chan. A scholar is included among the top collaborators of Daniel W. Chan 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 Daniel W. Chan. Daniel W. Chan 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.
Shore, Neal D., Christopher Pieczonka, Ralph J. Henderson, et al.. (2020). Development and evaluation of the MiCheck test for aggressive prostate cancer. Urologic Oncology Seminars and Original Investigations. 38(8). 683.e11–683.e18. 4 indexed citations
2.
Wang, Jing, Zihao Ma, Steven A. Carr, et al.. (2016). Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction. Molecular & Cellular Proteomics. 16(1). 121–134. 94 indexed citations
3.
Li, Danni, et al.. (2012). Validation of a multiplex immunoassay for serum angiogenic factors as biomarkers for aggressive prostate cancer. Clinica Chimica Acta. 413(19-20). 1506–1511. 25 indexed citations
4.
Marzinke, Mark A., Caitlin H. Choi, Li Chen, et al.. (2012). Proteomic Analysis of Temporally Stimulated Ovarian Cancer Cells for Biomarker Discovery. Molecular & Cellular Proteomics. 12(2). 356–368. 20 indexed citations
5.
Loeb, Stacy, Edward M. Schaeffer, Daniel W. Chan, et al.. (2009). Investigation of Human Anti-mouse Antibodies as Potential Cause of Postprostatectomy PSA Elevation. Urology. 73(5). 947–949. 2 indexed citations
6.
Yoshimura, Toru, Rahul Patil, Gangamani S. Beligere, et al.. (2009). Development and analytical performance evaluation of an automated chemiluminescent immunoassay for pro-gastrin releasing peptide (ProGRP). Clinical Chemistry and Laboratory Medicine (CCLM). 47(12). 1557–63. 12 indexed citations
7.
Zhang, Zhen, Yinhua Yu, Fengji Xu, et al.. (2007). Combining multiple serum tumor markers improves detection of stage I epithelial ovarian cancer. Gynecologic Oncology. 107(3). 526–531. 80 indexed citations
8.
Chan, Daniel W., et al.. (2007). Enzymes and related proteins as cancer biomarkers: A proteomic approach. Clinica Chimica Acta. 381(1). 93–97. 62 indexed citations
10.
Zhang, Zhen, et al.. (2004). Protein identification and immunoassay evaluation of a panel of biomarkers discovered through proteomic profiling for the detection of ovarian cancer. Cancer Research. 64. 242–242.
11.
Chan, Daniel W., et al.. (2004). The Use of Laser Capture Microscopy in Proteomics Research – A Review. Disease Markers. 20(3). 155–160. 13 indexed citations
12.
Sokoll, Lori J., Leslie A. Mangold, Alan W. Partin, et al.. (2002). Complexed prostate-specific antigen as a staging tool for prostate cancer: a prospective study in 420 men. Urology. 60(4). 18–23. 12 indexed citations
13.
Sokoll, Lori J. & Daniel W. Chan. (1998). Total, free, and complexed PSA: Analysis and clinical utility. 21(2). 171–179. 11 indexed citations
15.
Green, Gary B., et al.. (1998). Use of Troponin T and Creatine Kinase-MB Subunit Levels for Risk Stratification of Emergency Department Patients With Possible Myocardial Ischemia. Annals of Emergency Medicine. 31(1). 19–29. 37 indexed citations
16.
Pearson, Jay D., Albert A. Luderer, E. Jeffrey Metter, et al.. (1996). Longitudinal analysis of serial measurements of free and total PSA among men with and without prostatic cancer. Urology. 48(6). 4–9. 52 indexed citations
17.
Carter, H. Ballentine, Jay D. Pearson, E. Jeffrey Metter, et al.. (1995). Longitudinal evaluation of serum androgen levels in men with and without prostate cancer. The Prostate. 27(1). 25–31. 107 indexed citations
18.
Hsu, Chaur-Dong, et al.. (1994). Elevated serum human chorionic gonadotropin as evidence of secretory response in severe preeclampsia. American Journal of Obstetrics and Gynecology. 170(4). 1135–1138. 78 indexed citations
19.
Chan, Daniel W. & M.T. Perlstein. (1987). Immunoassay : a practical guide. Academic Press eBooks. 137 indexed citations
20.
Belizán, José M., et al.. (1983). Preliminary evidence of the effect of calcium supplementation on blood pressure in normal pregnant women. American Journal of Obstetrics and Gynecology. 146(2). 175–180. 100 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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