Matthew DeJongh

15.2k total citations · 1 hit paper
16 papers, 1.1k citations indexed

About

Matthew DeJongh is a scholar working on Molecular Biology, Biomedical Engineering and Artificial Intelligence. According to data from OpenAlex, Matthew DeJongh has authored 16 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 4 papers in Biomedical Engineering and 3 papers in Artificial Intelligence. Recurrent topics in Matthew DeJongh's work include Bioinformatics and Genomic Networks (8 papers), Microbial Metabolic Engineering and Bioproduction (7 papers) and Biofuel production and bioconversion (3 papers). Matthew DeJongh is often cited by papers focused on Bioinformatics and Genomic Networks (8 papers), Microbial Metabolic Engineering and Bioproduction (7 papers) and Biofuel production and bioconversion (3 papers). Matthew DeJongh collaborates with scholars based in United States, Russia and Portugal. Matthew DeJongh's co-authors include Aaron A. Best, Christopher S. Henry, Rick Stevens, John Gould, Matthew Rycenga, Ross Overbeek, Veronika Vonstein, Nathan Tintle, Pavel S. Novichkov and Dmitry A. Rodionov and has published in prestigious journals such as Nature Biotechnology, Bioinformatics and Journal of Bacteriology.

In The Last Decade

Matthew DeJongh

15 papers receiving 1.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

High-throughput generation, optimization and analysis of genome-scale metabolic models

2010 788 citations Christopher S. Henry, Matthew DeJongh et al. Nature Biotechnology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Matthew DeJongh United States	7	1.0k	389	92	83	48	16	1.1k
Stephen Federowicz United States	7	837 0.8×	218 0.6×	80 0.9×	159 1.9×	37 0.8×	8	960
Colton J. Lloyd United States	17	1.4k 1.4×	578 1.5×	55 0.6×	200 2.4×	55 1.1×	22	1.5k
Patrick F. Suthers United States	16	1.4k 1.4×	605 1.6×	139 1.5×	126 1.5×	65 1.4×	26	1.5k
Meike Baumgart Germany	17	743 0.7×	245 0.6×	100 1.1×	190 2.3×	28 0.6×	42	880
Changdai Gu South Korea	5	573 0.6×	178 0.5×	50 0.5×	38 0.5×	25 0.5×	7	655
Benjamín J. Sánchez Denmark	14	1.4k 1.4×	570 1.5×	30 0.3×	45 0.5×	55 1.1×	26	1.5k
Sungho Jang South Korea	18	1.0k 1.0×	299 0.8×	53 0.6×	152 1.8×	13 0.3×	41	1.2k
S. P. Sineoky Russia	16	551 0.6×	316 0.8×	57 0.6×	79 1.0×	36 0.8×	65	668
Adison Wong Singapore	12	742 0.7×	220 0.6×	120 1.3×	157 1.9×	11 0.2×	21	917
Peter B. Otoupal United States	15	808 0.8×	445 1.1×	28 0.3×	80 1.0×	48 1.0×	22	922

Countries citing papers authored by Matthew DeJongh

Since Specialization

Citations

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

Fields of papers citing papers by Matthew DeJongh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew DeJongh

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

All Works

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16 of 16 papers shown

1.

Li, Kevin, Rachel Chen, W. C. Lindsey, et al.. (2018). Implementing and evaluating a Gaussian mixture framework for identifying gene function from TnSeq data. PubMed. 24. 172–183. 1 indexed citations

2.

Faria, José P., James J. Davis, Janaka N. Edirisinghe, et al.. (2016). Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation. Frontiers in Microbiology. 7. 1819–1819. 5 indexed citations

3.

Disselkoen, Craig, Kristin A. Koch, Joshua Cape, et al.. (2016). A Bayesian Framework for the Classification of Microbial Gene Activity States. Frontiers in Microbiology. 7. 1191–1191.

4.

Tintle, Nathan, et al.. (2016). IDENTIFICATION AND ANALYSIS OF BACTERIAL GENOMIC METABOLIC SIGNATURES. PubMed. 22. 3–14. 1 indexed citations

5.

Overbeek, Ross, et al.. (2013). Automated Genome Annotation and Metabolic Model Reconstruction in the SEED and Model SEED. Methods in molecular biology. 985. 17–45. 96 indexed citations

6.

Tintle, Nathan, et al.. (2012). Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data. BMC Bioinformatics. 13(1). 193–193. 6 indexed citations

7.

DeJongh, Matthew, et al.. (2011). CytoSEED: a Cytoscape plugin for viewing, manipulating and analyzing metabolic models created by the Model SEED. Bioinformatics. 28(6). 891–892. 10 indexed citations

8.

DeJongh, Matthew, et al.. (2011). Increasing engagement and enrollment in breadth-first introductory courses using authentic computing tasks. 429–434. 5 indexed citations

9.

Ravcheev, Dmitry A., Aaron A. Best, Nathan Tintle, et al.. (2011). Inference of the Transcriptional Regulatory Network in Staphylococcus aureus by Integration of Experimental and Genomics-Based Evidence. Journal of Bacteriology. 193(13). 3228–3240. 36 indexed citations

10.

Henry, Christopher S., et al.. (2010). High-throughput generation, optimization and analysis of genome-scale metabolic models. Nature Biotechnology. 28(9). 977–982. 788 indexed citations breakdown →

11.

Tintle, Nathan, Aaron A. Best, Matthew DeJongh, et al.. (2008). Gene set analyses for interpreting microarray experiments on prokaryotic organisms. BMC Bioinformatics. 9(1). 469–469. 11 indexed citations

12.

DeJongh, Matthew, et al.. (2007). Toward the automated generation of genome-scale metabolic networks in the SEED. BMC Bioinformatics. 8(1). 139–139. 104 indexed citations

13.

Best, Aaron A., et al.. (2007). Models of Interdisciplinary Research and Service Learning at Hope College. Scholarly Commons (Embry–Riddle Aeronautical University). 28(2). 18. 3 indexed citations

14.

DeJongh, Matthew, et al.. (2005). Linking molecular function and biological process terms in the ontology for gene expression data analysis. PubMed. 4. 2984–2986. 4 indexed citations

15.

Johnson, Todd R., et al.. (1992). Diagrammatic Reasoning of Tabular Data. 1 indexed citations

16.

Johnson, Kathryn A., et al.. (1991). RedSoar--a system for red blood cell antibody identification.. PubMed. 664–8. 13 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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