Samuel T. Nadler

1.3k citations
17 papers · 1.1k indexed · h-index 11
Topics
Adipose Tissue and Metabolism (7 papers)Metabolism, Diabetes, and Cancer (5 papers)Adipokines, Inflammation, and Metabolic Diseases (4 papers)

In The Last Decade

Samuel T. Nadler

16 papers receiving 1.0k citations

Peers

Samuel T. Nadler
Comparison fields: 5 of 99
  • Molecular Biology 477
  • Physiology 383
  • Epidemiology 242
  • Surgery 191
  • Genetics 187
Replace David Kramer with:
David Kramer Sweden
Lee Moir United Kingdom
Takahiro Ueno Japan
Renée Komorowski United States
Letícia Prates Roma Germany
A.L. García-Otín Spain
Valeria G. Antico Arciuch Argentina
John B. Cheng United States
Hak Joo Lee United States
Sinju Sundaresan United States
Samuel T. Nadler relative to David Kramer Sweden David Kramer's profile →
Citations per field
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David Kramer · 1×
Citations per year

Countries citing papers authored by Samuel T. Nadler

Since Specialization
Citations

This map shows the geographic impact of Samuel T. Nadler'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 Samuel T. Nadler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Samuel T. Nadler more than expected).

Fields of papers citing papers by Samuel T. Nadler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Samuel T. Nadler. 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 Samuel T. Nadler. The network helps show where Samuel T. Nadler may publish in the future.

Co-authorship network of co-authors of Samuel T. Nadler

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

All Works

17 of 17 papers shown
#WorkIndexed citations
1 0
2 42
3 105
4 26
5 78
6 49
7 116
8 53
9 85
10
Mining for Low-abundance Transcripts in Microarray Data
8
11 47
12 1
13 323
14 114
15 10
16 1
17 4

About Samuel T. Nadler

Samuel T. Nadler is a scholar working on Endocrine and Autonomic Systems, Public Administration and Physiology, having authored 17 papers that have together received 1.1k indexed citations. Recurring topics across this work include Adipose Tissue and Metabolism (7 papers), Metabolism, Diabetes, and Cancer (5 papers) and Adipokines, Inflammation, and Metabolic Diseases (4 papers). The work is most often cited by research in Physiology (383 citations), Endocrine and Autonomic Systems (89 citations) and Biochemistry (73 citations). Samuel T. Nadler has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Alan Attie, Jonathan P. Stoehr, Brian S. Yandell, Kathryn L. Schueler, Mary E. Rabaglia, Susanne M. Clee, Qinglang Li, Peter Chen, William C. Parks and Lan Hong. Their work appears in journals such as Proceedings of the National Academy of Sciences, PLoS ONE and Diabetes.

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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