William M. Wood

4.0k citations
88 papers · 3.3k indexed · h-index 34

William M. Wood

87 papers receiving 3.2k citations

Peers

William M. Wood
Comparison fields: 5 of 105
  • Endocrinology, Diabetes and Metabolism 1.2k
  • Reproductive Medicine 399
  • Genetics 1.1k
  • Developmental Neuroscience 100
  • Molecular Biology 1.6k
Replace Jean‐Marie Gasc with:
Jean‐Marie Gasc France
R A Maurer United States
Vera M. Nikodem United States
Richard P. DiAugustine United States
Chon‐Hwa Tsai‐Morris United States
Elizabeth A. Allegretto United States
Deepak S. Lala United States
Kevin Docherty United Kingdom
Chetana M. Revankar United States
Martin L. Privalsky United States
William M. Wood relative to Jean‐Marie Gasc France Jean‐Marie Gasc's profile →
Citations per field
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Citations per year

Countries citing papers authored by William M. Wood

Since Specialization
Citations

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

Fields of papers citing papers by William M. Wood

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William M. Wood. 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 William M. Wood. The network helps show where William M. Wood may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 202118
2 202174
3 201525
4 201353
5 201056
6 200511
7 20022
8 200228
9 20026
10 20009
11 199915
12 199737
13 199620
14 199632
15 199316
16 199124
17 199116
18 198919
19 198841
20 198835

About William M. Wood

William M. Wood is a scholar working on Endocrinology, Diabetes and Metabolism, Reproductive Medicine and Developmental Neuroscience, having authored 88 papers that have together received 3.3k indexed citations. Recurring topics across this work include Growth Hormone and Insulin-like Growth Factors (32 papers), Thyroid Disorders and Treatments (30 papers), Estrogen and related hormone effects (18 papers), Pituitary Gland Disorders and Treatments (13 papers), Hypothalamic control of reproductive hormones (11 papers), Retinoids in leukemia and cellular processes (6 papers), RNA Research and Splicing (6 papers) and RNA modifications and cancer (5 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (1.2k citations), Reproductive Medicine (399 citations) and Genetics (1.1k citations). William M. Wood has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include David F. Gordon, E. Chester Ridgway, Bryan R. Haugen, Kathryn B. Horwitz, Lisa L. Wei, Virginia D. Sarapura, Ian H. Maxwell, F Maxwell, Gail S. Harrison and D. F. Gordon. Their work appears in journals such as Nucleic Acids Research, Journal of Biological Chemistry and Circulation.

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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