David Higgins

1.7k total citations
20 papers, 946 citations indexed

About

David Higgins is a scholar working on Molecular Biology, Genetics and Cellular and Molecular Neuroscience. According to data from OpenAlex, David Higgins has authored 20 papers receiving a total of 946 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 6 papers in Genetics and 3 papers in Cellular and Molecular Neuroscience. Recurrent topics in David Higgins's work include Genetic Mapping and Diversity in Plants and Animals (6 papers), Genetic and phenotypic traits in livestock (4 papers) and Neuroscience and Neuropharmacology Research (3 papers). David Higgins is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (6 papers), Genetic and phenotypic traits in livestock (4 papers) and Neuroscience and Neuropharmacology Research (3 papers). David Higgins collaborates with scholars based in United States, Germany and Ireland. David Higgins's co-authors include Beverly Paigen, Vince I. Madai, Xiao‐Song Wang, Gary A. Churchill, Haralambos Gavras, Fumihiro Sugiyama, Conrado Johns, Ron Korstanje, K McMillan and John Newell and has published in prestigious journals such as Nature Genetics, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

David Higgins

19 papers receiving 929 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Higgins United States 14 306 269 176 109 89 20 946
Hyun‐Seok Jin South Korea 20 374 1.2× 510 1.9× 54 0.3× 124 1.1× 102 1.1× 93 1.3k
Jiawen Xu China 17 99 0.3× 299 1.1× 222 1.3× 93 0.9× 26 0.3× 55 902
Dongdong Xie China 18 70 0.2× 328 1.2× 71 0.4× 162 1.5× 73 0.8× 71 1.2k
Guimin Gao United States 22 610 2.0× 849 3.2× 79 0.4× 179 1.6× 44 0.5× 76 1.7k
Ola Hansson Sweden 21 432 1.4× 862 3.2× 64 0.4× 436 4.0× 61 0.7× 61 1.6k
David Amar Israel 19 197 0.6× 624 2.3× 79 0.4× 116 1.1× 61 0.7× 46 1.2k
Sayonara Rangel Oliveira Brazil 21 56 0.2× 265 1.0× 180 1.0× 87 0.8× 47 0.5× 34 1.1k
Sarah L. Dunn United States 11 82 0.3× 265 1.0× 46 0.3× 204 1.9× 54 0.6× 16 878
Yuan Qu China 17 48 0.2× 263 1.0× 47 0.3× 58 0.5× 32 0.4× 34 728
Gregory Collier Australia 18 284 0.9× 595 2.2× 45 0.3× 767 7.0× 76 0.9× 24 2.0k

Countries citing papers authored by David Higgins

Since Specialization
Citations

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

Fields of papers citing papers by David Higgins

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Higgins

This figure shows the co-authorship network connecting the top 25 collaborators of David Higgins. A scholar is included among the top collaborators of David Higgins 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 David Higgins. David Higgins 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
1.
2.
Higgins, David, et al.. (2023). Validation of Artificial Intelligence Containing Products Across the Regulated Healthcare Industries. Therapeutic Innovation & Regulatory Science. 57(4). 797–809. 11 indexed citations
3.
Rieger, J., et al.. (2023). A Machine-learning–based Algorithm Improves Prediction of Preeclampsia-associated Adverse Outcomes. Obstetric Anesthesia Digest. 43(2). 81–82. 1 indexed citations
4.
5.
Rieger, J., et al.. (2022). A machine-learning–based algorithm improves prediction of preeclampsia-associated adverse outcomes. American Journal of Obstetrics and Gynecology. 227(1). 77.e1–77.e30. 39 indexed citations
6.
Higgins, David & Vince I. Madai. (2020). From Bit to Bedside: A Practical Framework for Artificial Intelligence Product Development in Healthcare. SHILAP Revista de lepidopterología. 2(10). 64 indexed citations
7.
Bouvier, Guy, David Higgins, Maria Spolidoro, et al.. (2016). Burst-Dependent Bidirectional Plasticity in the Cerebellum Is Driven by Presynaptic NMDA Receptors. Cell Reports. 15(1). 104–116. 41 indexed citations
8.
Anunciado‐Koza, Rea P., David Higgins, & Robert A. Koza. (2015). Adipose tissue Mest and Sfrp5 are concomitant with variations of adiposity among inbred mouse strains fed a non-obesogenic diet. Biochimie. 124. 134–140. 15 indexed citations
9.
Higgins, David, Michael Graupner, & Nicolas Brunel. (2014). Memory Maintenance in Synapses with Calcium-Based Plasticity in the Presence of Background Activity. PLoS Computational Biology. 10(10). e1003834–e1003834. 22 indexed citations
10.
Kongstad, Kenneth T., et al.. (2014). Positive allosteric modulation of the GHB high-affinity binding site by the GABAA receptor modulator monastrol and the flavonoid catechin. European Journal of Pharmacology. 740. 570–577. 19 indexed citations
11.
Higgins, David, et al.. (2009). Engaging Physician Leaders in Performance Measurement and Quality. Healthcare Quarterly. 12(2). 66–69. 5 indexed citations
12.
Newell, John, et al.. (2007). Software for calculating blood lactate endurance markers. Journal of Sports Sciences. 25(12). 1403–1409. 120 indexed citations
13.
Wang, Xiaosong, Massimiliano Ria, Peter M. Kelmenson, et al.. (2005). Positional identification of TNFSF4, encoding OX40 ligand, as a gene that influences atherosclerosis susceptibility. Nature Genetics. 37(4). 365–372. 224 indexed citations
14.
Wang, Xiao‐Song, Ron Korstanje, David Higgins, & Beverly Paigen. (2004). Haplotype Analysis in Multiple Crosses to Identify a QTL Gene. Genome Research. 14(9). 1767–1772. 87 indexed citations
15.
Kelmenson, Peter M., Petko M. Petkov, Xiao‐Song Wang, et al.. (2004). A Torrid Zone on Mouse Chromosome 1 Containing a Cluster of Recombinational Hotspots. Genetics. 169(2). 833–841. 31 indexed citations
16.
Phelan, Shelley A., David R. Beier, David Higgins, & Beverly Paigen. (2002). Confirmation and high resolution mapping of an atherosclerosis susceptibility gene in mice on Chromosome 1. Mammalian Genome. 13(10). 548–553. 34 indexed citations
17.
Sugiyama, Fumihiro, Gary A. Churchill, David Higgins, et al.. (2001). Concordance of Murine Quantitative Trait Loci for Salt-Induced Hypertension with Rat and Human Loci. Genomics. 71(1). 70–77. 176 indexed citations
18.
Purcell, Maureen K., et al.. (2001). Fine mapping of Ath6, a quantitative trait locus for atherosclerosis in mice. Mammalian Genome. 12(7). 495–500. 23 indexed citations
19.
Sampson, Stephen B., David Higgins, Benjamin A. Taylor, et al.. (1998). An edited linkage map for the AXB and BXA recombinant inbred mouse strains. Mammalian Genome. 9(9). 688–694. 27 indexed citations
20.
Higgins, David & Beverly Paigen. (1997). An additional 150 SSLP markers typed for the AXB and BXA recombinant inbred mouse strains. Mammalian Genome. 8(11). 846–849. 6 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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