Merry Sullivan

460 total citations
8 papers, 391 citations indexed

About

Merry Sullivan is a scholar working on Molecular Biology, Surgery and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Merry Sullivan has authored 8 papers receiving a total of 391 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 4 papers in Surgery and 1 paper in Endocrinology, Diabetes and Metabolism. Recurrent topics in Merry Sullivan's work include Peroxisome Proliferator-Activated Receptors (3 papers), RNA and protein synthesis mechanisms (3 papers) and Cholesterol and Lipid Metabolism (2 papers). Merry Sullivan is often cited by papers focused on Peroxisome Proliferator-Activated Receptors (3 papers), RNA and protein synthesis mechanisms (3 papers) and Cholesterol and Lipid Metabolism (2 papers). Merry Sullivan collaborates with scholars based in United States and United Kingdom. Merry Sullivan's co-authors include Lawrence Chan, Kazuhiro Oka, Kazumi Ishimura‐Oka, Catherine C.Y. Chang, Ta‐Yuan Chang, Julia Krushkal, Wen‐Hsiung Li, Oliver Tiebel, Makoto Nakamuta and Antonio Baldini and has published in prestigious journals such as Journal of Biological Chemistry, Journal of Molecular Biology and Brain Research.

In The Last Decade

Merry Sullivan

8 papers receiving 387 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Merry Sullivan United States	7	224	152	78	49	44	8	391
Martina Munck Germany	12	915 4.1×	201 1.3×	90 1.2×	55 1.1×	49 1.1×	15	1.1k
DE James United States	5	503 2.2×	218 1.4×	30 0.4×	31 0.6×	26 0.6×	9	594
Y Haraguchi Japan	12	326 1.5×	120 0.8×	26 0.3×	46 0.9×	134 3.0×	30	612
Sharon Grehan Ireland	7	224 1.0×	108 0.7×	35 0.4×	34 0.7×	14 0.3×	11	421
Toru Yoshida Japan	12	480 2.1×	174 1.1×	111 1.4×	119 2.4×	23 0.5×	20	723
June Chunqiu Hou United States	14	506 2.3×	243 1.6×	32 0.4×	33 0.7×	14 0.3×	16	680
Catherine Brunel‐Guitton Canada	14	454 2.0×	41 0.3×	33 0.4×	38 0.8×	47 1.1×	28	621
Yen-Ying Ma Taiwan	13	179 0.8×	84 0.6×	67 0.9×	45 0.9×	33 0.8×	17	414
Qilin Gu China	10	202 0.9×	49 0.3×	30 0.4×	35 0.7×	16 0.4×	14	307
Jennifer R. Dwyer United States	8	426 1.9×	136 0.9×	40 0.5×	99 2.0×	196 4.5×	11	634

Countries citing papers authored by Merry Sullivan

Since Specialization

Citations

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

Fields of papers citing papers by Merry Sullivan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Merry Sullivan

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

All Works

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8 of 8 papers shown

Sakai, Keiko, Oliver Tiebel, M. Cecilia Ljungberg, et al.. (2009). A neuronal VLDLR variant lacking the third complement-type repeat exhibits high capacity binding of apoE containing lipoproteins. Brain Research. 1276. 11–21. 15 indexed citations

Tiebel, Oliver, Kazuhiro Oka, Kathy Robinson, et al.. (1999). Mouse very low-density lipoprotein receptor (VLDLR): gene structure, tissue-specific expression and dietary and developmental regulation. Atherosclerosis. 145(2). 239–251. 63 indexed citations

Oka, Kazuhiro, Kunihisa Kobayashi, Merry Sullivan, et al.. (1997). Tissue-specific Inhibition of Apolipoprotein B mRNA Editing in the Liver by Adenovirus-mediated Transfer of a Dominant Negative Mutant APOBEC-1 Leads to Increased Low Density Lipoprotein in Mice. Journal of Biological Chemistry. 272(3). 1456–1460. 37 indexed citations

Oka, Kazuhiro, et al.. (1995). Tissue-specific Expression and Cholesterol Regulation of Acylcoenzyme A:Cholesterol Acyltransferase (ACAT) in Mice. Journal of Biological Chemistry. 270(44). 26192–26201. 110 indexed citations

Ishimura‐Oka, Kazumi, et al.. (1995). Partial Structure of the Mouse Glucokinase Gene. Genomics. 29(3). 751–754. 6 indexed citations

Oka, Kazuhiro, Kazumi Ishimura‐Oka, Merry Sullivan, et al.. (1994). Mouse Very‐Low‐Density‐Lipoprotein Receptor (VLDLR) cDNA Cloning, Tissue‐specific Expression and Evolutionary Relationship with the Low‐density‐lipoprotein Receptor. European Journal of Biochemistry. 224(3). 975–982. 96 indexed citations

Oka, Kazuhiro, Keh‐Weei Tzung, Merry Sullivan, et al.. (1994). Human Very-Low-Density Lipoprotein Receptor Complementary DNA and Deduced Amino Acid Sequence and Localization of Its Gene (VLDLR) to Chromosome Band 9p24 by Fluorescence in Situ Hybridization. Genomics. 20(2). 298–300. 47 indexed citations

Miller, Mitchell D., et al.. (1991). Crystallization and preliminary crystallographic analysis of a novel nuclease from Serratia marcescens. Journal of Molecular Biology. 222(1). 27–30. 17 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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