Matthew Hibbs

4.5k total citations
33 papers, 2.6k citations indexed

About

Matthew Hibbs is a scholar working on Molecular Biology, Genetics and Atmospheric Science. According to data from OpenAlex, Matthew Hibbs has authored 33 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Molecular Biology, 4 papers in Genetics and 2 papers in Atmospheric Science. Recurrent topics in Matthew Hibbs's work include Bioinformatics and Genomic Networks (16 papers), Gene expression and cancer classification (12 papers) and Genomics and Phylogenetic Studies (5 papers). Matthew Hibbs is often cited by papers focused on Bioinformatics and Genomic Networks (16 papers), Gene expression and cancer classification (12 papers) and Genomics and Phylogenetic Studies (5 papers). Matthew Hibbs collaborates with scholars based in United States, Canada and Norway. Matthew Hibbs's co-authors include Olga G. Troyanskaya, Chad L. Myers, Curtis Huttenhower, David Hess, Kai Li, Alexander Goesmann, Anne‐Claude Gavin, Séan O’Donoghue, Oliver Kohlbacher and Reinhard Schneider and has published in prestigious journals such as Journal of Clinical Investigation, Bioinformatics and PLoS ONE.

In The Last Decade

Matthew Hibbs

33 papers receiving 2.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthew Hibbs United States 22 2.1k 278 259 154 139 33 2.6k
John O. Mason United Kingdom 32 2.6k 1.2× 124 0.4× 629 2.4× 59 0.4× 228 1.6× 87 3.6k
Jasmin Fisher United Kingdom 23 1.6k 0.8× 61 0.2× 101 0.4× 179 1.2× 474 3.4× 60 2.5k
Elahe Elahi Iran 27 1.0k 0.5× 481 1.7× 365 1.4× 405 2.6× 30 0.2× 108 2.3k
Martina Kutmon Netherlands 17 1.4k 0.7× 54 0.2× 218 0.8× 45 0.3× 176 1.3× 51 2.1k
Kristina Hanspers United States 19 2.3k 1.1× 38 0.1× 274 1.1× 62 0.4× 255 1.8× 25 3.1k
Annamaria Carissimo Italy 23 1.3k 0.6× 81 0.3× 108 0.4× 88 0.6× 46 0.3× 48 2.4k
José Dávila-Velderrain United States 20 1.5k 0.7× 103 0.4× 156 0.6× 1.0k 6.6× 57 0.4× 40 2.8k
Yoseph Barash United States 27 3.0k 1.4× 61 0.2× 386 1.5× 32 0.2× 47 0.3× 63 3.6k
Alexis Battle United States 27 2.0k 1.0× 101 0.4× 1.2k 4.6× 42 0.3× 30 0.2× 56 2.9k
Ahmed Mahfouz Netherlands 25 1.6k 0.8× 112 0.4× 223 0.9× 108 0.7× 13 0.1× 80 2.5k

Countries citing papers authored by Matthew Hibbs

Since Specialization
Citations

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

Fields of papers citing papers by Matthew Hibbs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Hibbs

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Hibbs. A scholar is included among the top collaborators of Matthew Hibbs 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 Hibbs. Matthew Hibbs 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.
MacKie, Emma, et al.. (2023). GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation. Geoscientific model development. 16(13). 3765–3783. 5 indexed citations
2.
Hibbs, Matthew, et al.. (2020). Maternal diabetes and obesity influence the fetal epigenome in a largely Hispanic population. Clinical Epigenetics. 12(1). 34–34. 26 indexed citations
3.
Lewis, Kaitlyn N., Ilya Soifer, Eugene Melamud, et al.. (2016). Unraveling the message: insights into comparative genomics of the naked mole-rat. Mammalian Genome. 27(7-8). 259–278. 40 indexed citations
4.
Ball, Robyn L., Yasuhiro Fujiwara, Fengyun Sun, et al.. (2016). Regulatory complexity revealed by integrated cytological and RNA-seq analyses of meiotic substages in mouse spermatocytes. BMC Genomics. 17(1). 628–628. 29 indexed citations
5.
Walker, Michael, Timothy Billings, Christopher L. Baker, et al.. (2015). Affinity-seq detects genome-wide PRDM9 binding sites and reveals the impact of prior chromatin modifications on mammalian recombination hotspot usage. Epigenetics & Chromatin. 8(1). 31–31. 55 indexed citations
6.
Gelfond, Jonathan, Joseph G. Ibrahim, Ming‐Hui Chen, et al.. (2015). Homology cluster differential expression analysis for interspecies mRNA-Seq experiments. Statistical Applications in Genetics and Molecular Biology. 14(6). 507–16. 1 indexed citations
7.
Ackert‐Bicknell, Cheryl L. & Matthew Hibbs. (2012). The need for mouse models in osteoporosis genetics research. BoneKEy Reports. 1(6). 98–98. 3 indexed citations
8.
Gu, Tongjun, et al.. (2012). Canonical A-to-I and C-to-U RNA Editing Is Enriched at 3′UTRs and microRNA Target Sites in Multiple Mouse Tissues. PLoS ONE. 7(3). e33720–e33720. 69 indexed citations
9.
Howell, Gareth R., Danilo G. Macalinao, Gregory L. Sousa, et al.. (2011). Molecular clustering identifies complement and endothelin induction as early events in a mouse model of glaucoma. Journal of Clinical Investigation. 121(4). 1429–1444. 364 indexed citations
10.
Baryshnikova, Anastasia, Michael Costanzo, Huiming Ding, et al.. (2010). Quantitative analysis of fitness and genetic interactions in yeast on a genome scale. Nature Methods. 7(12). 1017–1024. 255 indexed citations
11.
Guan, Yuanfang, et al.. (2010). Functional Genomics Complements Quantitative Genetics in Identifying Disease-Gene Associations. PLoS Computational Biology. 6(11). e1000991–e1000991. 48 indexed citations
12.
Gehlenborg, Nils, Séan O’Donoghue, Nitin S. Baliga, et al.. (2010). Visualization of omics data for systems biology. Nature Methods. 7(S3). S56–S68. 423 indexed citations
13.
Hess, David, Chad L. Myers, Curtis Huttenhower, et al.. (2009). Computationally Driven, Quantitative Experiments Discover Genes Required for Mitochondrial Biogenesis. PLoS Genetics. 5(3). e1000407–e1000407. 118 indexed citations
14.
Huttenhower, Curtis, et al.. (2009). Exploring the human genome with functional maps. Genome Research. 19(6). 1093–1106. 153 indexed citations
15.
Hibbs, Matthew. (2008). Interpreting the Basin Closure Law in Montana: The Permissibility of "Prestream Capture" -- Montana Trout Unlimited v. Montana Department of Natural Resources and Conservation. The Mathematics Enthusiast. 29(1). 12. 1 indexed citations
16.
Huttenhower, Curtis, Avi I. Flamholz, J. Landis, et al.. (2007). Nearest Neighbor Networks: clustering expression data based on gene neighborhoods. BMC Bioinformatics. 8(1). 250–250. 52 indexed citations
17.
Myers, Chad L., et al.. (2006). Finding function: evaluation methods for functional genomic data. BMC Genomics. 7(1). 187–187. 154 indexed citations
18.
Sealfon, Rachel, Matthew Hibbs, Curtis Huttenhower, Chad L. Myers, & Olga G. Troyanskaya. (2006). GOLEM: an interactive graph-based gene-ontology navigation and analysis tool. BMC Bioinformatics. 7(1). 443–443. 46 indexed citations
19.
Myers, Chad L., Drew N. Robson, Matthew Hibbs, et al.. (2005). Discovery of biological networks from diverse functional genomic data. Genome biology. 6(13). R114–R114. 161 indexed citations
20.
Hibbs, Matthew, et al.. (2005). Visualization methods for statistical analysis of microarray clusters. BMC Bioinformatics. 6(1). 115–115. 44 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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