Abstract
High-throughput studies of biological systems are rapidly accumulating a wealth of 'omics'-scale data. Visualization is a key aspect of both the analysis and understanding of these data, and users now have many visualization methods and tools to choose from. The challenge is to create clear, meaningful and integrated visualizations that give biological insight, without being overwhelmed by the intrinsic complexity of the data. In this review, we discuss how visualization tools are being used to help interpret protein interaction, gene expression and metabolic profile data, and we highlight emerging new directions.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Michal, G. Biochemical Pathways: An Atlas of Biochemistry and Molecular Biology (Wiley, New York, 1998).
Nishizuka, Y. The role of protein kinase C in cell surface signal transduction and tumour promotion. Nature 308, 693–698 (1984).
Levine, M. & Davidson, E.H. Gene regulatory networks for development. Proc. Natl. Acad. Sci. USA 102, 4936–4942 (2005).
Bader, G.D., Cary, M.P. & Sander, C. Pathguide: a pathway resource list. Nucleic Acids Res. 34 Database issue, D504–D506 (2006).
Saraiya, P., North, C. & Duca, K. Visualizing biological pathways: requirements analysis, systems evaluation, and research agenda. Inf. Vis. 4, 191–205 (2005). This paper represents one of the first attempts to critically evaluate the requirements for visualization software used in biology.
Suderman, M. & Hallett, M. Tools for visually exploring biological networks. Bioinformatics 23, 2651–2659 (2007).
Pavlopoulos, G.A.G., Wegener, A.L.A. & Schneider, R.R. A survey of visualization tools for biological network analysis. BioData Min. 1, 12 (2008).
Charbonnier, S., Gallego, O. & Gavin, A.C. The social network of a cell: recent advances in interactome mapping. Biotechnol. Annu. Rev. 14, 1–28 (2008).
Yu, H. et al. High-quality binary protein interaction map of the yeast interactome network. Science 322, 104–110 (2008).
Gavin, A.C. et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006).
Mathivanan, S. et al. An evaluation of human protein-protein interaction data in the public domain. BMC Bioinformatics 7 (suppl. 5), S19 (2006).
Ma'ayan, A. Network integration and graph analysis in mammalian molecular systems biology. IET Syst. Biol. 2, 206–221 (2008).
Salwinski, L. et al. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 32 (database issue), D449–D451 (2004).
Prasad, T.S., Kandasamy, K. & Pandey, A. Human Protein Reference Database and Human Proteinpedia as discovery tools for systems biology. Methods Mol. Biol. 577, 67–79 (2009).
Aranda, B. et al. The IntAct molecular interaction database in 2010. Nucleic Acids Res. 38 (database issue), D525–D531 (2010).
von Mering, C. et al. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417, 399–403 (2002).
Fruchterman, T.M.J. & Reingold, E.M. Graph drawing by force-directed placement. Software Pract. Exper. 21, 1129–1164 (1991).
Bader, G.D. & Hogue, C.W. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4, 2 (2003).
Kuhner, S. et al. Proteome organization in a genome-reduced bacterium. Science 326, 1235–1240 (2009).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003). This paper describes the core Cytoscape software, which has become one of the most popular tools to visualize and analyze biological networks. This is partly due to the modular design of the software that allows developers to create plug-ins to address virtually any network analysis problem.
Junker, B.H., Klukas, C. & Schreiber, F. VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics 7, 109 (2006).
Hu, Z. et al. VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic Acids Res. 37 (web server issue), W115–W121 (2009).
McGuffin, M.J. & Jurisica, I. Interaction techniques for selecting and manipulating subgraphs in network visualizations. IEEE Trans. Vis. Comput. Graph. 15, 937–944 (2009).
Prinz, S. et al. Control of yeast filamentous-form growth by modules in an integrated molecular network. Genome Res. 14, 380–390 (2004).
Hu, Z. et al. Towards zoomable multidimensional maps of the cell. Nat. Biotechnol. 25, 547–554 (2007).
Barsky, A., Munzner, T., Gardy, J. & Kincaid, R. Cerebral: visualizing multiple experimental conditions on a graph with biological context. IEEE Trans. Vis. Comput. Graph. 14, 1253–1260 (2008).
Genc, B. and Dogrusoz, U. A layout algorithm for signaling pathways. Inf. Sci. 176, 135–149 (2006)
de Lichtenberg, U., Jensen, L.J., Brunak, S. & Bork, P. Dynamic complex formation during the yeast cell cycle. Science 307, 724–727 (2005).
Kanehisa, M. et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36 (database issue), D480–D484 (2008).
Matthews, L. et al. Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res. 37 (database issue), D619–D622 (2009).
Kuhn, M. et al. STITCH 2: an interaction network database for small molecules and proteins. Nucleic Acids Res. 38 (database issue), D552–D556 (2010).
Quackenbush, J. Computational analysis of microarray data. Nat. Rev. Genet. 2, 418–427 (2001).
Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).
Bantscheff, M., Schirle, M., Sweetman, G., Rick, J. & Kuster, B. Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem. 389, 1017–1031 (2007).
Gstaiger, M. & Aebersold, R. Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nat. Rev. Genet. 10, 617–627 (2009).
Westenberg, M.A., van Hijum, S.A.F.T., Kuipers, O.P. & Roerdink, J.B.T.M. Visualizing genome expression and regulatory network dynamics in genomic and metabolic context. Comput. Graph. Forum 27, 887–894 (2008).
Freeman, T.C. et al. Construction, visualisation, and clustering of transcription networks from microarray expression data. PLOS Comput. Biol. 3, e206 (2007).
Saraiya, P., Lee, P. & North, C. in IEEE Symp. Information Visualization (InfoVis 2005) 225–232 (2005).
Tufte, E.R. The Visual Display of Quantitative Information 2nd edn. (Graphics Press, Cheshire, Connecticut, USA, 2001).
Salomonis, N. et al. GenMAPP 2: new features and resources for pathway analysis. BMC Bioinformatics 8, 217 (2007).
Neuweger, H. et al. Visualizing post genomics data-sets on customized pathway maps by ProMeTra – aeration-dependent gene expression and metabolism of Corynebacterium glutamicum as an example. BMC Syst. Biol. 3, 82 (2009).
Kincaid, R., Kuchinsky, A. & Creech, M. VistaClara: an expression browser plug-in for Cytoscape. Bioinformatics 24, 2112–2114 (2008).
Lee, H.K., Hsu, A.K., Sajdak, J., Qin, J. & Pavlidis, P. Coexpression analysis of human genes across many microarray data sets. Genome Res. 14, 1085–1094 (2004).
Dunn, W.B. & Ellis, D.I. Metabolomics: current analytical platforms and methodologies. TrAC Trends Anal. Chem. 24, 285–294 (2005).
Scholz, M. & Fiehn, O. Setup X – a public study design database for metabolomic projects. Pac. Symp. Biocomput. 169–180, doi: 10.1142/9789812772435_0017 (2007).
Thimm, O. et al. MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 37, 914–939 (2004).
Paley, S.M. & Karp, P.D. The Pathway Tools cellular overview diagram and omics viewer. Nucleic Acids Res. 34, 3771–3778 (2006).
Tokimatsu, T. et al. KaPPA-view: a web-based analysis tool for integration of transcript and metabolite data on plant metabolic pathway maps. Plant Physiol. 138, 1289–1300 (2005).
Mlecnik, B. et al. PathwayExplorer: web service for visualizing high-throughput expression data on biological pathways. Nucleic Acids Res. 33 (web server issue), W633–W637 (2005)
Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 36 (database issue), D623–D631 (2008).
Funahashi, A. et al. CellDesigner 3.5: a versatile modeling tool for biochemical networks. Proc. IEEE 96, 1254–1265 (2008).
Sauro, H.M. et al. Next generation simulation tools: the Systems Biology Workbench and BioSPICE integration. OMICS 7, 355–372 (2003).
Demir, E. et al. PATIKA: an integrated visual environment for collaborative construction and analysis of cellular pathways. Bioinformatics 18, 996–1003 (2002).
Schreiber, F., Dwyer, T., Marriott, K. & Wybrow, M. A generic algorithm for layout of biological networks. BMC Bioinformatics 10, 375 (2009).
Thomas, J.J. & Cook, K.A. Illuminating the Path: The Research and Development Agenda for Visual Analytics. (National Visual Analytics Center & IEEE, Richland, Washington, USA, 2005).
Saraiya, P., North, C. & Duca, K. An Insight-based methodology for evaluating bioinformatics visualizations. IEEE Trans. Vis. Comput. Graph. 11, 443–456 (2005).
Dwyer, T., Koren, Y. & Marriott, K. IPSEP-COLA: an incremental procedure for separation constraint layout of graphs. IEEE Trans. Vis. Comput. Graph. 12, 821–828 (2006).
Dwyer, T. et al. Exploration of networks using overview+detail with constraint-based cooperative layout. IEEE Trans. Vis. Comput. Graph. 14, 1293–1300 (2008).
Viégas, F.B., Wattenberg, M., van Ham, F., Kriss, J. & McKeon, M. ManyEyes: a site for visualization at internet scale. IEEE Trans. Vis. Comput. Graph. 13, 1121–1128 (2007).
Heer, J., Viégas, F.B. & Wattenberg, M. Voyagers and voyeurs: supporting asynchronous collaborative information visualization. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI'07) 1029–1038 (ACM, New York, 2007).
Heer, J. & Agrawala, M. Design considerations for collaborative visual analytics. Inf. Vis. 7, 49–62 (2008).
Pico, A.R. et al. WikiPathways: pathway editing for the people. PLoS Biol. 6, e184 (2008).
Pavlopoulos, G.A. et al. Arena3D: visualization of biological networks in 3D. BMC Syst. Biol. 2, 104 (2008).
Ball, R. & North, C. Realizing embodied interaction for visual analytics through large displays. Comput. Graph. 31, 380–400 (2007).
Berman, H., Henrick, K. & Nakamura, H. Announcing the worldwide Protein Data Bank. Nat. Struct. Biol. 10, 980 (2003).
Hermjakob, H. et al. The HUPO PSI's molecular interaction format–a community standard for the representation of protein interaction data. Nat. Biotechnol. 22, 177–183 (2004).
Hucka, M. et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531 (2003).
Lloyd, C.M., Halstead, M.D. & Nielsen, P.F. CellML: its future, present and past. Prog. Biophys. Mol. Biol. 85, 433–450 (2004).
Le Novère, N. et al. The Systems Biology Graphical Notation. Nat. Biotechnol. 27, 735–741 (2009). This publication marks the first serious attempt to create a community standard for a graphical notation to represent networks in systems biology.
Walter, T. et al. Visualization of image data from cells to organisms. Nat. Methods 7, S26–S40 (2010).
O'Donoghue, S.I. et al. Visualization of macromolecular structures. Nat. Methods 7, S42–S55 (2010).
Nielsen, C.B., Cantor, M., Dubchak, I., Gordon, D. & Wang, T. Visualizing genomes: techniques and challenges. Nat. Methods 7, S5–S15 (2010).
Procter, J.B. et al. Visualization of multiple alignments, phylogenies and gene family evolution. Nat. Methods 7, S16–S25 (2010).
Burrage, K., Hood, L. & Ragan, M.A. Advanced computing for systems biology. Brief. Bioinform. 7, 390–398 (2006).
Awad, I.A., Rees, C.A., Hernandez-Boussard, T., Ball, C.A. & Sherlock, G. Caryoscope: an open source Java application for viewing microarray data in a genomic context. BMC Bioinformatics 5, 151 (2004).
Lau, C. et al. Exploration and visualization of gene expression with neuroanatomy in the adult mouse brain. BMC Bioinformatics 9, 153 (2008).
Weber, G.H. et al. Visual exploration of three-dimensional gene expression using physical views and linked abstract views. IEEE/ACM Trans. Comput. Biol. Bioinform. 6, 296–309 (2009).
Barsky, A., Gardy, J.L., Hancock, R.E. & Munzner, T. Cerebral: a Cytoscape plugin for layout of and interaction with biological networks using subcellular localization annotation. Bioinformatics 23, 1040–1042 (2007).
Mookherjee, N. et al. Modulation of the TLR-mediated inflammatory response by the endogenous human host defense peptide LL-37. J. Immunol. 176, 2455–2464 (2006).
Spellman, P.T. et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273–3297 (1998).
Kuntzer, J. et al. BNDB – the Biochemical Network Database. BMC Bioinformatics 8, 367 (2007).
Baitaluk, M., Sedova, M., Ray, A. & Gupta, A. BiologicalNetworks: visualization and analysis tool for systems biology. Nucleic Acids Res. 34 (web server issue), W466–W471 (2006).
Cline, M.S. et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2, 2366–2382 (2007).
Hooper, S.D. & Bork, P. Medusa: a simple tool for interaction graph analysis. Bioinformatics 21, 4432–4433 (2005).
Kao, H.L. & Gunsalus, K.C. Browsing multidimensional molecular networks with the generic network browser (N-Browse). Curr. Protoc. Bioinformatics 9, 9.11.1–9.11.21 (2008).
Brown, K.R. et al. NAViGaTOR: Network Analysis, Visualization and Graphing Toronto. Bioinformatics 25, 3327–3329 (2009).
Kohler, J. et al. Graph-based analysis and visualization of experimental results with ONDEX. Bioinformatics 22, 1383–1390 (2006).
Breitkreutz, B.J., Stark, C. & Tyers, M. Osprey: a network visualization system. Genome Biol. 4, R22 (2003).
Batagelj, V. & Mrvar, A. Pajek – Program for large network analysis. Connections 21, 47–57 (1998).
Forman, J.J., Clemons, P.A., Schreiber, S.L. & Haggarty, S.J. SpectralNET–an application for spectral graph analysis and visualization. BMC Bioinformatics 6, 260 (2005).
Auber, D. A huge graph visualization framework. in Graph Drawing Software (eds. Mutzel, P. & Jünger, M.) 105–126 (Springer, Heidelberg, Germany, 2004).
Zinovyev, A., Viara, E., Calzone, L. & Barillot, E. BiNoM: a Cytoscape plugin for manipulating and analyzing biological networks. Bioinformatics 24, 876–877 (2008).
Huttenhower, C., Mehmood, S.O. & Troyanskaya, O.G. Graphle: interactive exploration of large, dense graphs. BMC Bioinformatics 10, 417 (2009).
Longabaugh, W.J., Davidson, E.H. & Bolouri, H. Visualization, documentation, analysis, and communication of large-scale gene regulatory networks. Biochim. Biophys. Acta 1789, 363–374 (2009).
Streit, M., Lex, A., Kalkusch, M., Zatloukal, K. & Schmalstieg, D. Caleydo: connecting pathways and gene expression. Bioinformatics 25, 2760–2761 (2009).
Okuda, S. et al. KEGG atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res. 36 (web server issue), W423–W426 (2008).
van Iersel, M.P. et al. Presenting and exploring biological pathways with PathVisio. BMC Bioinformatics 9, 399 (2008).
Holford, M., Li, N., Nadkarni, P. & Zhao, H. VitaPad: visualization tools for the analysis of pathway data. Bioinformatics 21, 1596–1602 (2005).
Chung, H. J., Kim, M., Park, C. H., Kim, J., and Kim, J. H. ArrayXPath: mapping and visualizing microarray gene-expression data with integrated biological pathway resources using scalable vector graphics. Nucleic Acids Res. 32 (web server issue), W460–W464 (2004).
Weniger, M., Engelmann, J.C. & Schultz, J. Genome Expression Pathway Analysis Tool–analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context. BMC Bioinformatics 8, 179 (2007).
Letunic, I., Yamada, T., Kanehisa, M. & Bork, P. iPath: interactive exploration of biochemical pathways and networks. Trends Biochem. Sci. 33, 101–103 (2008).
Karp, P.D., Paley, S. & Romero, P. The Pathway Tools software. Bioinformatics 18 (suppl. 1), S225–S232 (2002).
Dogrusoz, U. et al. PATIKAweb: a Web interface for analyzing biological pathways through advanced querying and visualization. Bioinformatics 22, 374–375 (2006).
Santamaria, R., Theron, R. & Quintales, L. BicOverlapper: a tool for bicluster visualization. Bioinformatics 24, 1212–1213 (2008).
Goncalves, J.P., Madeira, S.C. & Oliveira, A.L. BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data. BMC Res Notes 2, 124 (2009).
Shamir, R. et al. EXPANDER–an integrative program suite for microarray data analysis. BMC Bioinformatics 6, 232 (2005).
Sturn, A., Quackenbush, J. & Trajanoski, Z. Genesis: cluster analysis of microarray data. Bioinformatics 18, 207–208 (2002).
Hibbs, M.A., Dirksen, N.C., Li, K. & Troyanskaya, O.G. Visualization methods for statistical analysis of microarray clusters. BMC Bioinformatics 6, 115 (2005).
Seo, J. & Shneiderman, B. Interactively exploring hierarchical clustering results. Computer 35, 80–86 (2002).
Saldanha, A.J. Java Treeview–extensible visualization of microarray data. Bioinformatics 20, 3246–3248 (2004).
Dietzsch, J., Gehlenborg, N. & Nieselt, K. Mayday – a microarray data analysis workbench. Bioinformatics 22, 1010–1012 (2006).
Gehlenborg, N., Dietzsch, J. & Nieselt, K. A framework for visualization of microarray data and integrated meta information. Inf. Vis. 4, 164–175 (2005).
Saeed, A.I. et al. TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34, 374–378 (2003). One of the first and still one of the most commonly used applications for the management, analysis and visualization of microarray data.
Hochheiser, H., Baehrecke, E.H., Mount, S.M. & Shneiderman, B. Dynamic querying for pattern identification in microarray and genomic data. Proc. IEEE Multimedia and Expo Int. Conf. 3, 453–456 (2003).
Kapushesky, M. et al. Expression Profiler: next generation–an online platform for analysis of microarray data. Nucleic Acids Res. 32 (web server issue), W465–W470 (2004).
Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500–501 (2006).
Quackenbush, J. Microarray data normalization and transformation. Nat. Genet. 32 (suppl.), 496–501 (2002).
Brettschneider, J. . Collin, F., Bolstad, B.M. & Speed, T.P. Quality assessment for short oligonucleotide microarray data. Technometrics 50, 241–264 (2008).
Kauffmann, A., Gentleman, R. & Huber, W. ArrayQualityMetrics–a bioconductor package for quality assessment of microarray data. Bioinformatics 25, 415–416 (2009).
Morgan, M. et al. ShortRead: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 25, 2607–2608 (2009).
Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Smyth, G.K., Yang, Y.H. & Speed, T. Statistical issues in cDNA microarray data analysis. Methods Mol. Biol. 224, 111–136 (2003).
Nesvizhskii, A.I., Vitek, O. & Aebersold, R. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 4, 787–797 (2007).
Perkins, D.N., Pappin, D.J., Creasy, D.M. & Cottrell, J.S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999).
Li, X.J. et al. A tool to visualize and evaluate data obtained by liquid chromatography-electrospray ionization-mass spectrometry. Anal. Chem. 76, 3856–3860 (2004).
Sturm, M. & Kohlbacher, O. TOPPView: an open-source viewer for mass spectrometry data. J. Proteome Res. 8, 3760–3763 (2009).
Kopka, J. Current challenges and developments in GC-MS based metabolite profiling technology. J. Biotechnol. 124, 312–322 (2006).
Broeckling, C.D. et al. Metabolic profiling of Medicago truncatula cell cultures reveals the effects of biotic and abiotic elicitors on metabolism. J. Exp. Bot. 56, 323–336 (2005).
Beckonert, O. et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2, 2692–2703 (2007).
Lindon, J.C. & Nicholson, J.K. Spectroscopic and statistical techniques for information recovery in metabonomics and metabolomics. Annu. Rev. Anal. Chem. 1, 45–69 (2008).
Xia, J., Bjorndahl, T.C., Tang, P. & Wishart, D.S. MetaboMiner–semi-automated identification of metabolites from 2D NMR spectra of complex biofluids. BMC Bioinformatics 9, 507 (2008).
Hotelling, H. Analysis of complex statistical variables into principal components. J. Educ. Psychol. 24, 417–441 (1933).
Kruskal, J. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29, 1–26 (1964).
Venna, J. & Kaski, S. Comparison of visualization methods for an atlas of gene expression data sets. Inf. Vis. 6, 139–154 (2007).
Inselberg, A. The plane with parallel coordinates. Vis. Comput. 1, 69–91 (1985).
Eisen, M.B., Spellman, P.T., Brown, P.O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998). Milestone publication that introduced the heat map visualization to the field of transcriptomics and has been cited several thousand times.
Wilkinson, L. & Friendly, M. The history of the cluster heat map. Am. Stat. 63, 179–184 (2009).
Weinstein, J.N. Biochemistry. a postgenomic visual icon. Science 319, 1772–1773 (2008).
Acknowledgements
The authors would like to acknowledge S. Kühner for providing the data for Figure 1 and Â. Gonçalves for comments on parts of the manuscript. This work was partly supported by the European Union Framework Programme 6 grant 'TAMAHUD' (LSHC-CT-2007-037472).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1-3 and Supplementary Table 1 (PDF 982 kb)
Rights and permissions
About this article
Cite this article
Gehlenborg, N., O'Donoghue, S., Baliga, N. et al. Visualization of omics data for systems biology. Nat Methods 7 (Suppl 3), S56–S68 (2010). https://doi.org/10.1038/nmeth.1436
Published:
Issue Date:
DOI: https://doi.org/10.1038/nmeth.1436
This article is cited by
-
Transomics2cytoscape: an automated software for interpretable 2.5-dimensional visualization of trans-omic networks
npj Systems Biology and Applications (2024)
-
Non-targeted screening and multivariate analysis of waste stream biomass conversion products
Biomass Conversion and Biorefinery (2022)
-
Relationship between gene regulation network structure and prediction accuracy in high dimensional regression
Scientific Reports (2021)
-
A network approach to elucidate and prioritize microbial dark matter in microbial communities
The ISME Journal (2021)
-
Visualizing metabolic network dynamics through time-series metabolomic data
BMC Bioinformatics (2020)