Roman Eisner

17.2k total citations · 4 hit papers
21 papers, 5.5k citations indexed

About

Roman Eisner is a scholar working on Molecular Biology, Pathology and Forensic Medicine and Oncology. According to data from OpenAlex, Roman Eisner has authored 21 papers receiving a total of 5.5k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 3 papers in Pathology and Forensic Medicine and 3 papers in Oncology. Recurrent topics in Roman Eisner's work include Metabolomics and Mass Spectrometry Studies (9 papers), Machine Learning in Bioinformatics (4 papers) and Tea Polyphenols and Effects (3 papers). Roman Eisner is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (9 papers), Machine Learning in Bioinformatics (4 papers) and Tea Polyphenols and Effects (3 papers). Roman Eisner collaborates with scholars based in Canada, France and Spain. Roman Eisner's co-authors include David S. Wishart, Craig Knox, Vanessa Neveu, Russell Greiner, Yannick Djoumbou-Feunang, Augustin Scalbert, Jara Pérez‐Jiménez, J. Cruz, P. Liu and C. Mak and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and PLoS ONE.

In The Last Decade

Roman Eisner

21 papers receiving 5.4k citations

Hit Papers

DrugBank 3.0: a comprehensive resource for 'Omics' resear... 2010 2026 2015 2020 2010 2010 2016 2013 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Roman Eisner Canada 17 3.0k 1.1k 895 476 422 21 5.5k
Vanessa Neveu France 15 3.1k 1.1× 1.7k 1.5× 1.4k 1.6× 666 1.4× 578 1.4× 18 6.3k
Justin J. J. van der Hooft Netherlands 41 3.6k 1.2× 498 0.5× 567 0.6× 294 0.6× 451 1.1× 98 5.2k
Craig Knox Canada 17 6.3k 2.1× 3.3k 3.0× 578 0.6× 525 1.1× 345 0.8× 20 9.9k
Wei Xiao China 44 3.6k 1.2× 487 0.4× 363 0.4× 235 0.5× 328 0.8× 433 7.9k
Gerard Pujadas Spain 34 1.8k 0.6× 1.1k 1.0× 705 0.8× 107 0.2× 173 0.4× 70 3.9k
Virapong Prachayasittikul Thailand 49 3.0k 1.0× 1.3k 1.2× 197 0.2× 187 0.4× 100 0.2× 278 7.8k
Richard D. Beger United States 40 3.2k 1.1× 408 0.4× 159 0.2× 211 0.4× 551 1.3× 163 5.8k
Giulio Rastelli Italy 36 4.2k 1.4× 2.1k 1.9× 226 0.3× 292 0.6× 176 0.4× 142 7.5k

Countries citing papers authored by Roman Eisner

Since Specialization
Citations

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

Fields of papers citing papers by Roman Eisner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Roman Eisner

This figure shows the co-authorship network connecting the top 25 collaborators of Roman Eisner. A scholar is included among the top collaborators of Roman Eisner 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 Roman Eisner. Roman Eisner 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.
Dykstra, Mark, Noah J. Switzer, Roman Eisner, et al.. (2017). Urine metabolomics as a predictor of patient tolerance and response to adjuvant chemotherapy in colorectal cancer. Molecular and Clinical Oncology. 7(5). 767–770. 13 indexed citations
3.
Djoumbou-Feunang, Yannick, Roman Eisner, Craig Knox, et al.. (2016). ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. Journal of Cheminformatics. 8(1). 61–61. 1002 indexed citations breakdown →
4.
Ravanbakhsh, Siamak, Philip L.‐F. Liu, Rupasri Mandal, et al.. (2015). Accurate, Fully-Automated NMR Spectral Profiling for Metabolomics. PLoS ONE. 10(5). e0124219–e0124219. 217 indexed citations
5.
Rothwell, Joseph A., Jara Pérez‐Jiménez, Vanessa Neveu, et al.. (2013). Phenol-Explorer 3.0: a major update of the Phenol-Explorer database to incorporate data on the effects of food processing on polyphenol content. Database. 2013(0). bat070–bat070. 650 indexed citations breakdown →
6.
Stretch, Cynthia, Nasimeh Asgarian, Roman Eisner, et al.. (2013). Effects of Sample Size on Differential Gene Expression, Rank Order and Prediction Accuracy of a Gene Signature. PLoS ONE. 8(6). e65380–e65380. 46 indexed citations
7.
Eisner, Roman, et al.. (2013). A Machine-Learned Predictor of Colonic Polyps Based on Urinary Metabolomics. BioMed Research International. 2013. 1–11. 33 indexed citations
8.
Rothwell, Joseph A., Mireia Urpí-Sardà, María Boto‐Ordóñez, et al.. (2012). Phenol-Explorer 2.0: a major update of the Phenol-Explorer database integrating data on polyphenol metabolism and pharmacokinetics in humans and experimental animals. Database. 2012(0). bas031–bas031. 137 indexed citations
9.
Stretch, Cynthia, Rupasri Mandal, Roman Eisner, et al.. (2011). Prediction of Skeletal Muscle and Fat Mass in Patients with Advanced Cancer Using a Metabolomic Approach. Journal of Nutrition. 142(1). 14–21. 26 indexed citations
10.
Neveu, Vanessa, Jara Pérez‐Jiménez, Vanessa Crespy, et al.. (2010). Phenol-Explorer: an online comprehensive database on polyphenol contents in foods. Database. 2010(0). bap024–bap024. 1043 indexed citations breakdown →
11.
Knox, Craig, Vivian Law, Timothy Jewison, et al.. (2010). DrugBank 3.0: a comprehensive resource for 'Omics' research on drugs. Nucleic Acids Research. 39(Database). D1035–D1041. 1475 indexed citations breakdown →
12.
Eisner, Roman, Cynthia Stretch, Jianguo Xia, et al.. (2010). Learning to predict cancer-associated skeletal muscle wasting from 1H-NMR profiles of urinary metabolites. Metabolomics. 7(1). 25–34. 49 indexed citations
13.
Wishart, David S., Michael Lewis, Roman Eisner, et al.. (2008). The human cerebrospinal fluid metabolome. Journal of Chromatography B. 871(2). 164–173. 263 indexed citations
14.
Poulin, Brett, Roman Eisner, Duane Szafron, et al.. (2006). Visual explanation of evidence in additive classifiers. Innovative Applications of Artificial Intelligence. 1822–1829. 57 indexed citations
15.
Knox, Craig, Savita Shrivastava, Paul Stothard, Roman Eisner, & David S. Wishart. (2006). BIOSPIDER: A WEB SERVER FOR AUTOMATING METABOLOME ANNOTATIONS. PubMed. 145–156. 18 indexed citations
16.
Eisner, Roman, Brett Poulin, Duane Szafron, P. Lu, & Russell Greiner. (2005). Improving Protein Function Prediction using the Hierarchical Structure of the Gene Ontology. 1–10. 77 indexed citations
17.
Szafron, Duane, P. Lu, Russell Greiner, et al.. (2004). Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations. Nucleic Acids Research. 32(Web Server). W365–W371. 88 indexed citations
18.
Lu, Zhonghua, Duane Szafron, Russell Greiner, et al.. (2004). Predicting subcellular localization of proteins using machine-learned classifiers. Bioinformatics. 20(4). 547–556. 276 indexed citations
19.
Lu, Zhiyong, Roman Eisner, Paul Lu, et al.. (2003). Proteome Analyst - Transparent High-throughput Protein Annotation: Function, Localization and Custom Predictors. University of Alberta Library. 6 indexed citations
20.
Böszörményi, László, et al.. (1999). Adding distribution to a workflow management system. 17–21. 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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