Doug Chapman

836 total citations
16 papers, 366 citations indexed

About

Doug Chapman is a scholar working on Molecular Biology, Rheumatology and Infectious Diseases. According to data from OpenAlex, Doug Chapman has authored 16 papers receiving a total of 366 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 6 papers in Rheumatology and 5 papers in Infectious Diseases. Recurrent topics in Doug Chapman's work include Amyloidosis: Diagnosis, Treatment, Outcomes (8 papers), HIV Research and Treatment (5 papers) and HIV/AIDS drug development and treatment (5 papers). Doug Chapman is often cited by papers focused on Amyloidosis: Diagnosis, Treatment, Outcomes (8 papers), HIV Research and Treatment (5 papers) and HIV/AIDS drug development and treatment (5 papers). Doug Chapman collaborates with scholars based in United States, Brazil and Italy. Doug Chapman's co-authors include C.C. Evans, Jayvant Heera, Hernán Valdez, Winnie Dong, Luke C. Swenson, Ian James, Marilyn Lewis, Conan K. Woods, P. Richard Harrigan and Mark A. Jensen and has published in prestigious journals such as PLoS ONE, Clinical Infectious Diseases and The Journal of Infectious Diseases.

In The Last Decade

Doug Chapman

16 papers receiving 354 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Doug Chapman United States 11 188 153 105 62 60 16 366
Francesco Massei Italy 12 101 0.5× 123 0.8× 46 0.4× 51 0.8× 26 0.4× 30 432
O. Lambotte France 8 168 0.9× 144 0.9× 44 0.4× 48 0.8× 56 0.9× 25 377
Shunji Matsuda Japan 11 113 0.6× 84 0.5× 48 0.5× 25 0.4× 83 1.4× 28 346
H Weigel Netherlands 11 136 0.7× 150 1.0× 30 0.3× 22 0.4× 73 1.2× 15 331
Soichiro Takahama Japan 10 68 0.4× 73 0.5× 35 0.3× 31 0.5× 49 0.8× 41 308
Ilona Tóth United States 11 160 0.9× 132 0.9× 33 0.3× 30 0.5× 101 1.7× 14 498
Yan Ding China 11 123 0.7× 68 0.4× 86 0.8× 25 0.4× 58 1.0× 26 319
Teresa Aldámiz‐Echevarría Spain 13 97 0.5× 125 0.8× 74 0.7× 22 0.4× 277 4.6× 47 483
Amrit Singh United States 5 131 0.7× 133 0.9× 31 0.3× 81 1.3× 107 1.8× 11 341
Dominique Masson France 12 72 0.4× 50 0.3× 53 0.5× 82 1.3× 57 0.9× 49 470

Countries citing papers authored by Doug Chapman

Since Specialization
Citations

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

Fields of papers citing papers by Doug Chapman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Doug Chapman

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

All Works

16 of 16 papers shown
1.
Gentile, Luca, Igor Diemberger, Violaine Planté‐Bordeneuve, et al.. (2024). Phenotypic characteristics of F64L, I68L, I107V, and S77Y ATTRv genotypes from the Transthyretin Amyloidosis Outcomes Survey (THAOS). PLoS ONE. 19(1). e0292435–e0292435. 2 indexed citations
2.
Parnell, Andrew, Vimal Chandran, Lara Fallon, et al.. (2022). POS1045 MASS SPECTROMETRY-BASED PROTEOMICS FOR THE IDENTIFICATION OF CANDIDATE SERUM PROTEIN BIOMARKERS THAT MAY PREDICT TREATMENT RESPONSE IN PATIENTS WITH PSORIATIC ARTHRITIS. Annals of the Rheumatic Diseases. 81. 840–841. 1 indexed citations
3.
Coelho, Teresa, Isabel Conceição, Márcia Waddington‐Cruz, et al.. (2022). A natural history analysis of asymptomatic TTR gene carriers as they develop symptomatic transthyretin amyloidosis in the Transthyretin Amyloidosis Outcomes Survey (THAOS). Amyloid. 29(4). 228–236. 16 indexed citations
4.
Dispenzieri, Angela, Teresa Coelho, Isabel Conceição, et al.. (2022). A Consolidated Overview Of 14 Years Of Global Data From The Transthyretin Amyloidosis Outcomes Survey. Journal of Cardiac Failure. 28(5). S111–S111. 1 indexed citations
5.
Barroso, Fábio, Teresa Coelho, Angela Dispenzieri, et al.. (2022). Characteristics of patients with autonomic dysfunction in the Transthyretin Amyloidosis Outcomes Survey (THAOS). Amyloid. 29(3). 175–183. 15 indexed citations
6.
González‐Moreno, Juan, Inés Losada López, Eugenia Cisneros‐Barroso, et al.. (2021). A Descriptive Analysis of ATTR Amyloidosis in Spain from the Transthyretin Amyloidosis Outcomes Survey. Neurology and Therapy. 10(2). 833–845. 8 indexed citations
7.
Gentile, Luca, Ivailo Tournev, Leslie Amass, et al.. (2021). Phenotypic Differences of Glu89Gln Genotype in ATTR Amyloidosis From Endemic Loci: Update From THAOS. Cardiology and Therapy. 10(2). 481–490. 11 indexed citations
8.
Waddington‐Cruz, Márcia, et al.. (2021). Feasibility of assessing progression of transthyretin amyloid polyneuropathy using nerve conduction studies: Findings from the Transthyretin Amyloidosis Outcomes Survey (THAOS). Journal of the Peripheral Nervous System. 26(2). 160–166. 10 indexed citations
9.
Nativi-Nicolau, José, Angela Dispenzieri, Matthew J. Maurer, et al.. (2020). Temporal Trends of Wild-type Attr Amyloidosis in the Transthyretin Amyloidosis Outcomes Survey. Journal of Cardiac Failure. 26(10). S82–S82. 2 indexed citations
10.
Lee, Guinevere Q., P. Richard Harrigan, Winnie Dong, et al.. (2013). Comparison of Population and 454 “Deep” Sequence Analysis for HIV Type 1 Tropism Versus the Original Trofile Assay in Non-B Subtypes. AIDS Research and Human Retroviruses. 29(6). 979–984. 14 indexed citations
11.
Swenson, Luke C., Winnie Dong, Theresa Mo, et al.. (2013). Use of Cellular HIV DNA to Predict Virologic Response to Maraviroc: Performance of Population-Based and Deep Sequencing. Clinical Infectious Diseases. 56(11). 1659–1666. 24 indexed citations
12.
Swenson, Luke C., Celia Chui, Chanson J. Brumme, et al.. (2013). Genotypic Analysis of the V3 Region of HIV from Virologic Nonresponders to Maraviroc-Containing Regimens Reveals Distinct Patterns of Failure. Antimicrobial Agents and Chemotherapy. 57(12). 6122–6130. 15 indexed citations
13.
Wilkin, Timothy, Matthew Bidwell Goetz, Robert Leduc, et al.. (2011). Reanalysis of Coreceptor Tropism in HIV-1-Infected Adults Using a Phenotypic Assay with Enhanced Sensitivity. Clinical Infectious Diseases. 52(7). 925–928. 35 indexed citations
14.
Swenson, Luke C., Winnie Dong, Xiaolin Zhong, et al.. (2010). Deep Sequencing to Infer HIV-1 Co-Receptor Usage: Application to Three Clinical Trials of Maraviroc in Treatment-Experienced Patients. The Journal of Infectious Diseases. 203(2). 237–245. 119 indexed citations
15.
Chapman, Doug, et al.. (1998). Gastroesophageal reflux disease in children older than two years of age.. PubMed. 94(1). 22–5. 16 indexed citations
16.
Evans, C.C., et al.. (1976). Association of HLA-A9 and HLA-B5 with Buerger's disease.. BMJ. 2(6045). 1165–1166. 77 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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