Daniel J. Gans

768 total citations
16 papers, 504 citations indexed

About

Daniel J. Gans is a scholar working on Computational Theory and Mathematics, Statistics and Probability and Pharmacology. According to data from OpenAlex, Daniel J. Gans has authored 16 papers receiving a total of 504 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computational Theory and Mathematics, 4 papers in Statistics and Probability and 3 papers in Pharmacology. Recurrent topics in Daniel J. Gans's work include Computational Drug Discovery Methods (4 papers), Advanced Statistical Methods and Models (3 papers) and Analytical Chemistry and Chromatography (2 papers). Daniel J. Gans is often cited by papers focused on Computational Drug Discovery Methods (4 papers), Advanced Statistical Methods and Models (3 papers) and Analytical Chemistry and Chromatography (2 papers). Daniel J. Gans collaborates with scholars based in United States and Canada. Daniel J. Gans's co-authors include Janet B. McGill, Lori M. Laffel, James W. McFarland, Ivan G. Otterness, Edward H. Wiseman, Jerome G. Porush, Tomás Berl, Julia B. Lewis, Edmund J. Lewis and Jean‐Lucien Rouleau and has published in prestigious journals such as JAMA, Technometrics and Kidney International.

In The Last Decade

Daniel J. Gans

16 papers receiving 457 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel J. Gans United States	11	200	169	127	56	51	16	504
P. A. de Graeff Netherlands	16	349 1.7×	61 0.4×	90 0.7×	49 0.9×	7 0.1×	27	594
H.F. Woods United Kingdom	8	98 0.5×	34 0.2×	77 0.6×	102 1.8×	16 0.3×	9	708
Bernard E. Ilson United States	17	360 1.8×	44 0.3×	204 1.6×	72 1.3×	13 0.3×	33	825
Iris Rajman Switzerland	17	372 1.9×	44 0.3×	221 1.7×	55 1.0×	24 0.5×	31	885
DA Willard United States	13	140 0.7×	16 0.1×	86 0.7×	118 2.1×	39 0.8×	18	592
David A Willard United States	11	190 0.9×	26 0.2×	72 0.6×	62 1.1×	19 0.4×	14	569
John Pears United Kingdom	12	94 0.5×	44 0.3×	273 2.1×	37 0.7×	49 1.0×	22	976
HL Elliott United Kingdom	17	381 1.9×	24 0.1×	147 1.2×	126 2.3×	21 0.4×	57	876
Sampat M. Singhvi Malaysia	8	191 1.0×	35 0.2×	46 0.4×	60 1.1×	23 0.5×	12	544
Julio D. Duarte United States	14	264 1.3×	21 0.1×	168 1.3×	44 0.8×	34 0.7×	53	772

Countries citing papers authored by Daniel J. Gans

Since Specialization

Citations

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

Fields of papers citing papers by Daniel J. Gans

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel J. Gans

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

All Works

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16 of 16 papers shown

Anavekar, Nagesh S., Daniel J. Gans, Tomás Berl, et al.. (2004). Predictors of cardiovascular events in patients with type 2 diabetic nephropathy and hypertension: A case for albuminuria. Kidney International. 66(92). S50–S55. 93 indexed citations

Aguilar, David, Samuel Z. Goldhaber, Daniel J. Gans, et al.. (2004). Clinically unrecognized Q-wave myocardial infarction in patients with diabetes mellitus, systemic hypertension, and nephropathy. The American Journal of Cardiology. 94(3). 337–339. 19 indexed citations

Laffel, Lori M., Janet B. McGill, & Daniel J. Gans. (1995). The beneficial effect of angiotensin-converting enzyme inhibition with captopril on diabetic nephropathy in normotensive IDDM patients with microalbuminuria. The American Journal of Medicine. 99(5). 497–504. 192 indexed citations

McFarland, James W. & Daniel J. Gans. (1994). On Identifying Likely Determinants of Biological Activity in High Dimensional QSAR Problems. Quantitative Structure-Activity Relationships. 13(1). 11–17. 24 indexed citations

Otterness, Ivan G., et al.. (1991). A Radioimmunoassay for Colchicine Using a Highly Specific, High Affinity Monoclonal Antibody. Drug Development and Industrial Pharmacy. 17(17). 2391–2400. 4 indexed citations

McFarland, James W. & Daniel J. Gans. (1990). Cluster Significance Analysis: A New Qsar Tool for Asymmetric Data Sets. Drug Information Journal. 24(4). 705–711. 5 indexed citations

Otterness, Ivan G. & Daniel J. Gans. (1988). Nonsteroidal Anti-inflammatory Drugs: An Analysis of the Relationship between Laboratory Animal and Clinical Doses. Including Species Scaling. Journal of Pharmaceutical Sciences. 77(9). 790–795. 25 indexed citations

McFarland, James W. & Daniel J. Gans. (1987). Cluster significance analysis contrasted with three other quantitative structure-activity relationship methods. Journal of Medicinal Chemistry. 30(1). 46–49. 20 indexed citations

McFarland, James W. & Daniel J. Gans. (1986). On the significance of clusters in the graphical display of structure-activity data. Journal of Medicinal Chemistry. 29(4). 505–514. 29 indexed citations

10.

Gans, Daniel J.. (1985). Persistence of Party Success in American Presidential Elections. The Journal of Interdisciplinary History. 16(2). 221–221. 13 indexed citations

11.

Gans, Daniel J.. (1984). The search for significance: different tests on the same data. Journal of Statistical Computation and Simulation. 19(1). 1–21. 10 indexed citations

12.

Gans, Daniel J.. (1982). A simple method based on broken‐line interpolation for displaying data from long‐term clinical trials. Statistics in Medicine. 1(2). 131–137. 5 indexed citations

13.

Gans, Daniel J.. (1981). Use of a preliminary test in comparing two sample means. Communications in Statistics - Simulation and Computation. 10(2). 163–174. 30 indexed citations

14.

Gans, Daniel J.. (1981). Corrected and Extended Tables for Tukey's Quick Test. Technometrics. 23(2). 193–195. 6 indexed citations

15.

Otterness, Ivan G., Edward H. Wiseman, & Daniel J. Gans. (1979). A comparison of the carrageenan edema test and ultraviolet light-induced erythema test as predictors of the clinical dose in rheumatoid arthritis. Inflammation Research. 9(2). 177–183. 28 indexed citations

16.

Gans, Daniel J.. (1975). Coronary Drug Project. JAMA. 234(1). 21–21. 1 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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