Fred Parham

1.9k citations
32 papers · 1.2k indexed · h-index 19
Topics
Computational Drug Discovery Methods (9 papers)Animal testing and alternatives (9 papers)Effects and risks of endocrine disrupting chemicals (8 papers)

In The Last Decade

Fred Parham

30 papers receiving 1.2k citations

Peers

Fred Parham
Comparison fields: 5 of 117
  • Health, Toxicology and Mutagenesis 556
  • Molecular Biology 429
  • Pollution 151
  • Small Animals 136
  • Cancer Research 135
Replace Jui‐Hua Hsieh with:
Jui‐Hua Hsieh United States
Fabian A. Grimm United States
Shirlee Tan United States
Tomasz Sobański Poland
Andrea Terron Italy
Martin Paparella Italy
Derik E. Haggard United States
Ram Ramabhadran United States
Jorge M. Naciff United States
Ila Cote United States
Fred Parham relative to Jui‐Hua Hsieh United States Jui‐Hua Hsieh's profile →
Citations per field
00.5×1.6×
Jui‐Hua Hsieh · 1×
Citations per year

Countries citing papers authored by Fred Parham

Since Specialization
Citations

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

Fields of papers citing papers by Fred Parham

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fred Parham

This figure shows the co-authorship network connecting the top 25 collaborators of Fred Parham. A scholar is included among the top collaborators of Fred Parham 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 Fred Parham. Fred Parham 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
#WorkIndexed citations
1 0
2 8
3 0
4 13
5 153
6 61
7 66
8 101
9 3
10 20
11 19
12 8
13 36
14 87
15 18
16 19
17 35
18 12
19 70
20 79

About Fred Parham

Fred Parham is a scholar working on Small Animals, Health, Toxicology and Mutagenesis and Computational Theory and Mathematics, having authored 32 papers that have together received 1.2k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (9 papers), Animal testing and alternatives (9 papers) and Effects and risks of endocrine disrupting chemicals (8 papers). The work is most often cited by research in Health, Toxicology and Mutagenesis (556 citations), Small Animals (136 citations) and Developmental Neuroscience (76 citations). Fred Parham has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Christopher J. Portier, Mamta Behl, Kristen Ryan, Raymond R. Tice, Scott A. Masten, H.B. Matthews, Oksana Sirenko, Scott S. Auerbach, Daniel Svoboda and Jui‐Hua Hsieh. Their work appears in journals such as Bioinformatics, The Science of The Total Environment and Environmental Health Perspectives.

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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