David E Malehorn

1.1k total citations
19 papers, 824 citations indexed

About

David E Malehorn is a scholar working on Molecular Biology, Spectroscopy and Infectious Diseases. According to data from OpenAlex, David E Malehorn has authored 19 papers receiving a total of 824 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 6 papers in Spectroscopy and 3 papers in Infectious Diseases. Recurrent topics in David E Malehorn's work include Advanced Proteomics Techniques and Applications (6 papers), Machine Learning in Bioinformatics (3 papers) and Metabolomics and Mass Spectrometry Studies (3 papers). David E Malehorn is often cited by papers focused on Advanced Proteomics Techniques and Applications (6 papers), Machine Learning in Bioinformatics (3 papers) and Metabolomics and Mass Spectrometry Studies (3 papers). David E Malehorn collaborates with scholars based in United States, Australia and Canada. David E Malehorn's co-authors include Vytas A. Bankaitis, Scott D. Emr, R. Greene, William L. Bigbee, Dilip M. Shah, P H Yuen, P.K.Y. Wong, Christine E. Smith, Jeffry R. Borgmeyer and Sofie R. Salama and has published in prestigious journals such as The Journal of Cell Biology, Bioinformatics and PLANT PHYSIOLOGY.

In The Last Decade

David E Malehorn

19 papers receiving 797 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 E Malehorn United States 14 533 286 120 79 65 19 824
Kaye D. Speicher United States 15 636 1.2× 220 0.8× 43 0.4× 65 0.8× 102 1.6× 20 1.2k
Ramsey A. Saleem United States 22 1.1k 2.0× 146 0.5× 50 0.4× 22 0.3× 57 0.9× 32 1.2k
Andrew P. VanDemark United States 23 1.7k 3.1× 224 0.8× 115 1.0× 59 0.7× 101 1.6× 42 2.0k
F. Molemans Belgium 10 1.0k 2.0× 127 0.4× 83 0.7× 42 0.5× 105 1.6× 13 1.3k
Michael A. Hadders Netherlands 16 736 1.4× 384 1.3× 118 1.0× 37 0.5× 188 2.9× 20 1.1k
Michele Tinti United Kingdom 16 885 1.7× 116 0.4× 37 0.3× 33 0.4× 163 2.5× 41 1.1k
Naoyuki Hayashi Japan 25 1.5k 2.8× 230 0.8× 140 1.2× 98 1.2× 54 0.8× 62 1.9k
Raymond V. Fucini United States 16 792 1.5× 365 1.3× 34 0.3× 42 0.5× 48 0.7× 20 1.3k
David R. Loiselle United States 21 927 1.7× 174 0.6× 25 0.2× 30 0.4× 175 2.7× 31 1.3k
Akiyuki Hada Japan 18 936 1.8× 424 1.5× 28 0.2× 123 1.6× 186 2.9× 23 1.5k

Countries citing papers authored by David E Malehorn

Since Specialization
Citations

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

Fields of papers citing papers by David E Malehorn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David E Malehorn

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

All Works

19 of 19 papers shown
1.
Malehorn, David E, et al.. (2011). Transfer learning of classification rules for biomarker discovery and verification from molecular profiling studies. Journal of Biomedical Informatics. 44. S17–S23. 21 indexed citations
2.
LaFramboise, William A., Patti Petrosko, John M. Krill-Burger, et al.. (2010). Proteins secreted by embryonic stem cells activate cardiomyocytes through ligand binding pathways. Journal of Proteomics. 73(5). 992–1003. 21 indexed citations
3.
Hood, Brian L., David E Malehorn, Thomas P. Conrads, & William L. Bigbee. (2009). Serum Proteomics Using Mass Spectrometry. Methods in molecular biology. 520. 107–128. 12 indexed citations
4.
Pelikan, Richard, William L. Bigbee, David E Malehorn, James Lyons‐Weiler, & Miloš Hauskrecht. (2007). Intersession reproducibility of mass spectrometry profiles and its effect on accuracy of multivariate classification models. Bioinformatics. 23(22). 3065–3072. 13 indexed citations
5.
Papachristou, Georgios I., David E Malehorn, Janette Lamb, et al.. (2007). Serum Proteomic Patterns as a Predictor of Severity in Acute Pancreatitis. Pancreatology. 7(4). 317–324. 20 indexed citations
6.
Jahnukainen, Timo, David E Malehorn, Mai Sun, et al.. (2006). Proteomic Analysis of Urine in Kidney Transplant Patients with BK Virus Nephropathy. Journal of the American Society of Nephrology. 17(11). 3248–3256. 39 indexed citations
7.
Yurkovetsky, Zoya R., Faina Linkov, David E Malehorn, & Anna Lokshin. (2006). Multiple Biomarker Panels For Early Detection of Ovarian Cancer. Future Oncology. 2(6). 733–741. 36 indexed citations
8.
Miyamae, Takako, David E Malehorn, Bonnie Lemster, et al.. (2005). Serum protein profile in systemic-onset juvenile idiopathic arthritis differentiates response versus nonresponse to therapy. Arthritis Research & Therapy. 7(4). R746–55. 30 indexed citations
9.
Hauskrecht, Miloš, Richard Pelikan, David E Malehorn, et al.. (2005). Feature Selection for Classification of SELDI-TOF-MS Proteomic Profiles. PubMed. 4(4). 227–246. 23 indexed citations
10.
Lyons‐Weiler, James, Richard Pelikan, Herbert J. Zeh, et al.. (2005). Assessing the statistical significance of the achieved classification error of classifiers constructed using serum peptide profiles, and a prescription for random sampling repeated studies for massive high-throughput genomic and proteomic studies.. PubMed. 1. 53–77. 19 indexed citations
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12.
Telmer, Cheryl A., David E Malehorn, Xuemei Zeng, et al.. (2003). Detection and assignment ofTP53 mutations in tumor DNA using peptide mass signature genotyping. Human Mutation. 22(2). 158–165. 13 indexed citations
13.
Malehorn, David E, et al.. (2003). Detection of Cystic Fibrosis Mutations by Peptide Mass Signature Genotyping. Clinical Chemistry. 49(8). 1318–1330. 4 indexed citations
14.
Malehorn, David E, Jeffry R. Borgmeyer, Christine E. Smith, & Dilip M. Shah. (1994). Characterization and Expression of an Antifungal Zeamatin-like Protein (Zlp) Gene from Zea mays. PLANT PHYSIOLOGY. 106(4). 1471–1481. 64 indexed citations
15.
Malehorn, David E, et al.. (1993). Structure and expression of a barley acidic ?-glucanase gene. Plant Molecular Biology. 22(2). 347–360. 27 indexed citations
16.
Salama, Sofie R., Ann E. Cleves, David E Malehorn, Eric Whitters, & Vytas A. Bankaitis. (1990). Cloning and characterization of Kluyveromyces lactis SEC14, a gene whose product stimulates Golgi secretory function in Saccharomyces cerevisiae. Journal of Bacteriology. 172(8). 4510–4521. 65 indexed citations
17.
Bankaitis, Vytas A., David E Malehorn, Scott D. Emr, & R. Greene. (1989). The Saccharomyces cerevisiae SEC14 gene encodes a cytosolic factor that is required for transport of secretory proteins from the yeast Golgi complex.. The Journal of Cell Biology. 108(4). 1271–1281. 334 indexed citations
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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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