Publications

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Scientific publications

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Journal articles

[130] F. Emmert-Streib, B. Haibe-Kains, S. Moutari, J. McCann, P. Mullan, G. Glazko, M. Dehmer, and C. Campbell. Can genome-scale gene regulatory networks be experimentally validated? Differences between biological and clinical validations. 2017, submitted. [ bib ]
[129] F. Emmert-Streib, S. Das, S. Tripathi, O. Yli-Harja, Y. Shi, and M. Dehmer. Understanding the world economy in terms of networks: A survey of data-based network science approaches on economic networks. 2017, submitted. [ bib ]
[128] K. Baltakys, J. Kanniainen, and F. Emmert-Streib. Multilayer Aggregation of Investor Trading Networks. Scientific Reports, 2017, accepted. [ bib ]
[127] F. Emmert-Streib, A. Musa, S. Tripathi, O. Yli-Harja, K. Baltakys, J. Kanniainen, and M. Dehmer. Computational Analysis of structural properties of Economic Networks. Journal Of Network Theory In Finance, 2017, accepted. [ bib ]
[126] Matthias Dehmer, Zengqiang Chen, Frank Emmert-Streib, Yongtang Shi, Shailesh Tripathi, Aliyu Musa, and Abbe Mowshowitz. Properties of graph distance measures by means of discrete inequalities. Applied Mathematical Modelling, 59:739 -- 749, 2018. [ bib | DOI | http ]
[125] Laszlo Barna Iantovics, Matthias Dehmer, and Frank Emmert-Streib. Metrintsimil—an accurate and robust metric for comparison of similarity in intelligence of any number of cooperative multiagent systems. Symmetry, 10(2):48, 2018. [ bib | DOI | http ]
[124] R. de Matos Simoes, F. Emmert-Streib, B. Duggan, M.W. Ruddock, D. O'Rourke, H.F. O'Kane, C.H. Reid, and K.E. Williamson. Smoking and increased age in hematuria patients significantly confound the accuracy of classifiers for the diagnosis of urothelial cancer. 2017, submitted. [ bib ]
[123] Ricardo de Matos Simoes, F. Emmert-Streib, Mark W. Ruddock, Declan O’Rourke, Brian Duggan, Hugh F. O’Kane, Funso Abogunrin, Cherith N. Reid, and Kate E. Williamson. Biomarker classifiers for detection of bladder cancer in patients with haematuria are confounded by smoking and increased age. Urologic Oncology: Seminars and Original Investigations, 35(10):621, 2017. [ bib | DOI | http ]
[122] M. Dehmer, F. Emmert-Streib, and Y. Shi. Quantitative graph theory: A new branch of graph theory and network science. Information Sciences, 418:575--580, 2017. [ bib ]
[121] S. Tripathi, J. Lloyd-Price, A. Ribeiro, O. Yli-Harja, M. Dehmer, and F. Emmert-Streib. sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters. BMC bioinformatics, 18(1):325, 2017. [ bib ]
[120] L. B. Iantovics, F. Emmert-Streib, and S. Arik. Metrintmeas a novel metric for measuring the intelligence of a swarm of cooperating agents. Cognitive Systems Research, 45:17--29, 2017. [ bib ]
[119] Y. Rahmatallah, B. Zybailov, F. Emmert-Streib, and G. Glazko. GSAR: Bioconductor package for gene set analysis in R. BMC Bioinformatics, 18(1):61, 2017. [ bib | DOI | http ]
[118] M. Dehmer, F. Emmert-Streib, B. Hu, Y. Shi, M. Stefu, and S. Tripathi. Highly unique network descriptors based on the roots of the permanental polynomial. Information Sciences, 408:176 -- 181, 2017. [ bib | DOI | http ]
[117] Z. Chen, M. Dehmer, F. Emmert-Streib, A. Mowshowitz, and Y. Shi. Toward measuring network aesthetics based on symmetry. Axioms, 6(2):12, 2017. [ bib | DOI | http ]
[116] A. Musa, L. Ghoraie, S-D. Zhang, G. Glazko, O. Yli-Harja, M. Dehmer, B. Haibe-Kains, and F. Emmert-Streib. A review of connectivity mapping and computational approaches in pharmacogenomics. Briefings in Bioinformatics, page 112, 2017. [ bib ]
[115] F. Emmert-Streib, M. Dehmer, and O. Yli-Harja. Against dataism and for data sharing of big biomedical and clinical data with research parasites. Frontiers in Genetics, 7:154, 2016. [ bib | DOI | http ]
[114] S. Tripathi, S. Moutari, M. Dehmer, and F. Emmert-Streib. Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules. BMC Bioinformatics, 17(1):1--18, 2016. [ bib | DOI | http ]
[113] F. Emmert-Streib, L. Tuomisto, and O. Yli-Harja. The Need for Formally Defining 'Modern Medicine' by Means of Experimental Design. Frontiers in Genetics, 7:60, 2016. [ bib | DOI | http ]
[112] A. Stupnikov, S. Tripathi, R. de Matos Simoes, D. McArt, M. Salto-Tellez, G. Glazko, and F. Emmert-Streib. samExploreR: Exploring reproducibility and robustness of RNA-seq results based on SAM files. Bioinformatics, page 475, 2016. [ bib ]
[111] N. Buckley, P. Haddock, R. De Matos Simoes, E. Parkes, G. Irwin, F. Emmert-Streib, S. McQuaid, R. Kennedy, and P. Mullan. A BRCA1 deficient, NFκB driven immune signal predicts good outcome in triple negative breast cancer. Oncotarget, 7(15):19884--96, 2016. [ bib ]
[110] F. Emmert-Streib, M. Dehmer, and Y. Shi. Fifty Years of Graph Matching, Network Alignment and Network Comparison. Information Sciences, 346-347:180--197, 2016. [ bib ]
[109] F. Emmert-Streib, S. Moutari, and M. Dehmer. The process of analyzing data is the emergent feature of data science. Frontiers in Genetics, 7:12, 2016. [ bib ]
[108] Y. Rahmatallah, F. Emmert-Streib, and G. Glazko. Gene set analysis approaches for RNA-seq data: performance evaluation and application guideline. Briefings in Bioinformatics, 17(3):393--407, 2016. [ bib | DOI ]
[107] MW Ruddock, R de Matos Simoes, DO Rourke, B Duggan, and et al. Stevenson, M. Urinary thrombomodulin levels were significantly higher following occupational exposure to chemicals, in the presence of dipstick protein, but not in the presence of dipstick blood. Biology and Medicine, 7(5):1000260, 2015. [ bib ]
[106] F. Emmert-Streib and M. Dehmer. Biological networks: The microscope of the 21st century? Frontiers in Genetics, 6(307), 2015. [ bib ]
[105] M. Dehmer, F. Emmert-Streib, and Y. Shi. Graph distance measures based on topological indices revisited. Applied Mathematics and Computation, 266:623 -- 633, 2015. [ bib | DOI | http ]
[104] M. Alvi, D. McArt, P. Kelly, M-A Fuchs, M. Alderdice, C. McCabe, V. Bingham, C. McGready, S. Tripathi, F. Emmert-Streib, M. Loughrey, S. McQaid, P. Maxwell, P. Hamilton, J. James, R. Wilson, and M. Salto-Tellez. Comprehensive molecular pathology analysis of small bowel adenocarcinoma reveals novel targets with potential clinical utility. Oncotarget, 6(25):20863--74, 2015. [ bib ]
[103] S. de Matos Simoes, Dalleau, K.E. Williamson, and F. Emmert-Streib. Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data. BMC Systems Biology, 9:21, 2015. [ bib ]
[102] Z. Chen, M. Dehmer, F. Emmert-Streib, and Y. Shi. Entropy of weighted graphs with Randić weights. Entropy, 17:3710--3723, 2015. [ bib ]
[101] Y. Shi, F. Emmert-Streib, and M. Dehmer. Graph Distance Mesures Based on Topological Indices Revisited. Applied Mathematics and Computation, 266:623--633, 2015. [ bib ]
[100] M. Dehmer, Z. Kurt, F. Emmert-Streib, C. Them, E. Schulc, and S. Hofer. Structural Analysis of Treatment Cycles Representing Transitions Between Nursing Organizational Units Inferred from Diabetes. PLoS ONE, 10(6):e0127152, 2015. [ bib ]
[99] C. Olsen, K. Fleming, N. Prendergast, R. Rubio, F. Emmert-Streib, G. Bontempi, J. Quackenbush, and B. Haibe-Kains. Using shRNA experiments to validate gene regulatory networks. Genomics Data, 4(0):123--126, 2015. [ bib ]
[98] A. Stupnikov, G. Glazko, and F. Emmert-Streib. Effects of subsampling on characteristics of RNA-seq data from triple negative breast cancer patients. Chinese Journal of Cancer, 34:36, 2015. [ bib ]
[97] M. Dehmer, F. Emmert-Streib, Y. Shi, M. Stefu, and S. Tripathi. Discrimination power of polynomial-based descriptors for graphs by using functional matrices. PLoS ONE, 10(10):1--10, 10 2015. [ bib | DOI | http ]
[96] Y. Rahmatallah, F. Emmert-Streib, and G. Glazko. Comparative evaluation of gene set analysis approaches for RNA-Seq data. BMC Bioinformatics, 15:397, 2014. [ bib ]
[95] F. Emmert-Streib, M. Dehmer, and B. Haibe-Kains. Untangling statistical and biological models to understand network inference: The need for a genomics network ontology. Front. Genet., 5:299, 2014. [ bib ]
[94] V. Kraus, M. Dehmer, and F. Emmert-Streib. Probabilistic inequalities for evaluating structural network measures. Information Sciences, 288:220--245, 2014. [ bib ]
[93] F. Emmert-Streib, M. Dehmer, and B. Haibe-Kains. Gene regulatory networks and their applications: Understanding biological and medical problems in terms of networks. Front. Cell Dev. Biol., 2:38, 2014. [ bib ]
[92] B. Haibe-Kains and F. Emmert-Streib. Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics. Front. Genet., 5:221, 2014. [ bib ]
[91] S. Tripathi, M. Dehmer, and F. Emmert-Streib. NetBioV: An R package for visualizing large-scale data in Network Biology. Bioinformatics, page 384, 2014. [ bib ]
[90] C. Olsen, G. Bontempi, F. Emmert-Streib, J. Quackenbush, and B. Haibe-Kains. Relevance of different prior knowledge sources for inferring gene interaction networks. Front. Genet., 5:177, 2014. [ bib ]
[89] M. Dehmer, F. Emmert-Streib, and Y. Shi. Interrelations of graph distance measures based on topological indices. PLoS One, 9(4):e94985, 2014. [ bib ]
[88] Z. Chen, M. Dehmer, F. Emmert-Streib, and Y. Shi. Entropy bounds for dendrimers. Applied Mathematics and Computation, 242:462--472, 2014. [ bib ]
[87] F. Emmert-Streib. Enhancing our understanding of ways to analyze metagenomes. Front. Genet., 5:108, 2014. [ bib ]
[86] F. Emmert-Streib, R. de Matos Simoes, G Glazko, S. McDade, B. Haibe-Kains, A. Holzinger, M. Dehmer, and F. Campbell. Functional and genetic analysis of the colon cancer network. BMC Bioinformatics, 15:6, 2014. [ bib ]
[85] F. Emmert-Streib, S-D Zhang, and P. Hamilton. Computational Cancer Biology: Education is a key to many locks. BMC Cancer, 15:7, 2015. [ bib ]
[84] F. Emmert-Streib, S-D Zhang, and P. Hamilton. Dry computational approaches for wet medical problems. Journal of Translational Medicine, 12:36, 2014. [ bib ]
[83] F. Emmert-Streib, R. de Matos Simoes, P. Mullan, B. Haibe-Kains, and M. Dehmer. The gene regulatory network for breast cancer: Integrated regulatory landscape of cancer hallmarks. Front. Genet., 5:15, 2014. [ bib ]
[82] F. Emmert-Streib, S-D Zhang, and P. Hamilton. Report from the 2nd Summer School in Computational Biology organized by the Queen's University of Belfast. Genomics Data, 2:37--39, 2014. [ bib ]
[81] C Olsen, K. Fleming, N. Prendergast, R. Rubio, F Emmert-Streib, G. Bontempi, B. Haibe-Kains, and J. Quackenbush. Inference and validation of predictive gene networks from biomedical literature and gene expression data. Genomics, 103(5-6):329--36, 2014. [ bib ]
[80] A. Gokmen, Z. Kurt, M. Dehmer, and F. Emmert-Streib. Netmes: Assessing gene network inference algorithms by ensemble network-based measures. Evolutionary Bioinformatics, 10:1--9, 2014. [ bib ]
[79] M. Dehmer, F. Emmert-Streib, and M. Grabner. A computational approach to construct a multivariate complete graph invariant. Information Sciences, 260:200--208, 2014. [ bib ]
[78] Y. Rahmatallah, F. Emmert-Streib, and G. Glazko. Gene Sets Net Correlations Analysis (GSNCA): A multivariate differential coexpression test for gene sets. Bioinformatics, 30(3):360--368, 2014. [ bib ]
[77] R. de Matos Simoes, M. Dehmer, and F. Emmert-Streib. B-cell lymphoma gene regulatory networks: Biological consistency among inference methods. Front. Genet., 4:281, 2013. [ bib ]
[76] M. Dehmer, F. Emmert-Streib, and S. Tripathi. Large-scale evaluation of molecular descriptors by means of clustering. PLoS ONE, 8(12):e83956, 2013. [ bib ]
[75] M. Dehmer, L.A.J. Mueller, and F. Emmert-Streib. Quantitative network measures as biomarkers for classifying prostate cancer disease states: A systems approach to diagnostic biomarkers. PLoS ONE, 8(11):e77602, 2013. [ bib ]
[74] A. Dander, L.A.J. Mueller, R. Gallasch, S. Pabinger, F. Emmert-Streib, A. Graber, and M. Dehmer. [COMMODE] A Large-Scale Database of Molecular Descriptors using compounds from PubChem. Source Code for Biology and Medicine, 8:22, 2013. [ bib ]
[73] F. Emmert-Streib and M. Dehmer. Enhancing systems medicine beyond genotype data by dynamic patient signatures: Having information and using it too. Front. Genet., 4:241, 2013. [ bib ]
[72] M. Dehmer, M. Grabner, A. Mowshowitz, and F. Emmert-Streib. An efficient heuristic approach to detecting graph isomorphism based on combinations of highly discriminating invariants. Advances in Computational Mathematics, 39(2):311--325, 2013. [ bib ]
[71] R. de Matos Simoes, M. Dehmer, and F. Emmert-Streib. Interfacing cellular networks of S. cerevisiae and E. coli: Connecting dynamic and genetic information. BMC Genomics, 14:324, 2013. [ bib ]
[70] M. Dehmer, W.O. Hackl, F. Emmert-Streib, W. Schulc, and C. Them. Network nursing: Connections between nursing and complex network science. International Journal of Nursing Knowledge, 24(3):150--6, 2013. [ bib ]
[69] F. Emmert-Streib, M. Dehmer, and F. Lyardet. Learning systems biology: Conceptual considerations toward a web-based learning platform. Education Sciences, 3(2):158--171, 2013. [ bib ]
[68] S. Tripathi, G.V. Glazko, and F. Emmert-Streib. Ensuring the statistical soundness of competitive gene set approaches: gene filtering and genome-scale coverage are essential. Nucleic Acids Research, 6(12):e53354, 2013. [ bib ]
[67] F. Emmert-Streib. Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: Environmental factors. PeerJ, 1:e10, 2013. [ bib ]
[66] F. Emmert-Streib, F. Abogunrin, R. de Matos Simoes, B. Duggan, M.W. Ruddock, C.N. Reid, O. Roddy, L. White, H.F. O'Kane, N.H. Anderson, D. O'Rourke, T. Nambirajan, and K.E. Williamson. Collectives of diagnostic biomarkers identify high-risk subpopulations of hematuria patients: exploiting heterogeneity in large-scale biomarker data . BMC Medicine, 11(1):12, 2013. [ bib ]
[65] F. Emmert-Streib and G.V. Glazko. The evolution of modularity in protein networks: A second-order gene duplication model. 2015, submitted. [ bib ]
[64] F. Emmert-Streib. Personalized medicine: Has it started yet? A reconstruction of the early history. Front Genet, 3:313, 2013. [ bib ]
[63] F. Emmert-Streib. Structural properties and complexity of a new network class: Collatz step graphs. PLoS ONE, 8(2):e56461, 2013. [ bib ]
[62] F. Emmert-Streib, S. Tripathi, R. de Matos Simoes, A.F. Hawwa, and M. Dehmer. The human disease network: Opportunities for classification, diagnosis and prediction of disorders and disease genes. Systems Biomedicine, 1(1):1--8, 2013. [ bib ]
[61] F. Emmert-Streib, S. Tripathi, and R. de Matos Simoes. Harnessing the complexity of gene expression data from cancer: From single gene to structural pathway methods. Biology Direct, 7:44, 2012. [ bib ]
[60] Y. Rahmatallah, F. Emmert-Streib, and G. Glazko. Gene set analysis for self-contained tests: complex null and specific alternative hypotheses. Bioinformatics, 28(23):3073--3080, 2012. [ bib ]
[59] R. de Matos Simoes, S. Tripathi, and F. Emmert-Streib. Organizational structure of the peripheral gene regulatory network in B-cell lymphoma. BMC Systems Biology, 6:38, 2012. [ bib ]
[58] M. Dehmer, M. Grabner, A. Mowshowitz, and F. Emmert-Streib. An efficient heuristic approach to detecting graph isomorphism based on combinations of highly discriminating invariants. Advances in Computational Mathematics, pages 1--15, 2012. [ bib ]
[57] F. Emmert-Streib, R. de Matos Simoes, S. Tripathi, G.V. Glazko, and M. Dehmer. A Bayesian analysis of the chromosome architecture of human disorders by integrating reductionist data. Scientific Reports, 2:513, 2012. [ bib ]
[56] S. Tripathi and F. Emmert-Streib. Assessment Method for a Power Analysis to Identify Differentially Expressed Pathways. PLoS ONE, 7(5):e37510, 05 2012. [ bib ]
[55] Reid C.N., M. Stevenson, Abogunrin F., Ruddock M.W., Emmert-Streib F., Lamont J.V., and Williamson K.E. Standardisation of diagnostic biomarker concentrations in urine; the hematuria caveat. PLoS ONE, 7(12):e53354, 2012. [ bib ]
[54] F. Emmert-Streib, A. Häkkinen, and A.S. Ribeiro. Detecting sequence dependent transcriptional pauses from RNA and protein number time series. BMC Bioinformatics, 13(152), 2012. [ bib ]
[53] R. de Matos Simoes and F. Emmert-Streib. Bagging statistical network inference from large-scale gene expression data. PLoS ONE, 7(3):e33624, 2012. [ bib ]
[52] F. Emmert-Streib. Universal construction mechanism for networks from one-dimensional symbol sequences. Applied Mathematics and Computation, 219(3):1020--1030, 2012. [ bib ]
[51] F. Emmert-Streib. Limitations of the gene duplication model: Evolution of modules in protein interaction networks. PLoS ONE, 7(4):e35531, 2012. [ bib ]
[50] F. Emmert-Streib and M. Dehmer. Exploring statistical and population aspects of network complexity. PLoS ONE, 7(5):e34523, 2012. [ bib ]
[49] F. Emmert-Streib, G.V. Glazko, Gökmen Altay, and Ricardo de Matos Simoes. Statistical inference and reverse engineering of gene regulatory networks from observational expression data. Frontiers in Genetics, 3:8, 2012. [ bib ]
[48] F. Emmert-Streib. Evolutionary dynamics of the spatial Prisoner's Dilemma with self-inhibition. Applied Mathematics and Computation, 218(11):6482--6488, 2012. [ bib ]
[47] L. Mueller, K. Kugler, A. Graber, F. Emmert-Streib, and M. Dehmer. Structural Measures for Network Biology Using QuACN. BMC Bioinformatics, 12(1):492, 2011. [ bib ]
[46] R. de Matos Simoes and F. Emmert-Streib. Influence of Statistical Estimators of Mutual Information and Data Heterogeneity on the Inference of Gene Regulatory Networks. PLoS ONE, 6(12):e29279, 2011. [ bib ]
[45] F. Emmert-Streib. Parametric construction of episode networks from pseudoperiodic time series based on mutual information. PLoS ONE, 6(12):e27733, 2011. [ bib ]
[44] G. Altay and F. Emmert-Streib. Structural Influence of gene networks on their inference: Analysis of C3NET. Biology Direct, 6:31, 2011. [ bib ]
[43] F. Emmert-Streib and G.V. Glazko. Network Biology: A direct approach to study biological function. Wiley Interdiscip Rev Syst Biol Med, 3(4):379--391, 2011. [ bib ]
[42] F. Emmert-Streib and M. Dehmer. Networks for Systems Biology: Conceptual Connection of Data and Function. IET Systems Biology, 5(3):185, 2011. [ bib ]
[41] F. Emmert-Streib and G.V. Glazko. Pathway analysis of expression data: deciphering functional building blocks of complex diseases. PLoS Computational Biology, 7(5):e1002053, 2011. [ bib ]
[40] M. Dehmer, A. Mowshowitz, and F. Emmert-Streib. Connections between Classical and Parametric Network Entropies. PLoS ONE, 6(1):e15733, 2011. [ bib ]
[39] F. Emmert-Streib and M. Dehmer. Influence of the Time Scale on the Construction of Financial Networks. PLoS ONE, 5(9):e12884, 2010. [ bib ]
[38] G. Altay and F. Emmert-Streib. Inferring the conservative causal core of gene regulatory networks. BMC Systems Biology, 4:132, 2010. [ bib ]
[37] F. Emmert-Streib. Statistic Complexity: Combining Kolmogorov Complexity with an Ensemble Approach. PLoS ONE, 5(8):e12256, 2010. [ bib ]
[36] G. Altay and F. Emmert-Streib. Revealing differences in gene network inference algorithms on the network-level by ensemble methods. Bioinformatics, 26(14):1738--44, 2010. [ bib ]
[35] F. Emmert-Streib and G. Altay. Local network-based measures to assess the inferability of different regulatory networks. IET Systems Biology, 4(4):277--288, 2010. [ bib ]
[34] F. Emmert-Streib and M. Dehmer. Identifying Critical Financial Networks of the DJIA: Towards a Network-based Index. Complexity, 16(1):24--33, 2010. [ bib ]
[33] M. Dehmer, F. Emmert-Streib, Y.R. Tsoy, and K. Varmuza. Novel Information Measure for the Analysis of Chemical Graphs. Bulletin of the Tomsk Polytechnic University, 316(5), 2010. [ bib ]
[32] F. Emmert-Streib. Exploratory analysis of spatiotemporal patterns of cellular automata by clustering compressibility. Physical Review E, 81(2):026103, 2010. [ bib ]
[31] G.V. Glazko and F. Emmert-Streib. Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets. Bioinformatics, 25(18):2348--54, 2009. [ bib ]
[30] F. Emmert-Streib and M. Dehmer. Predicting cell cycle regulated genes by causal interactions. Plos One, 4(8):e6633, 2009. [ bib ]
[29] F. Emmert-Streib and M. Dehmer. Hierarchical coordination of periodic genes in the cell cycle of saccharomyces cerevisiae. BMC Systems Biology, 3:76, 2009. [ bib ]
[28] M. Dehmer, K. Varmuza, S. Borgert, and F. Emmert-Streib. On entropy-based molecular descriptors: Statistical analysis of real and synthetic chemical structures. Journal of Chemical Information and Modeling, 49(7):1655--63, 2009. [ bib ]
[27] F. Emmert-Streib and M. Dehmer. Information processing in the transcriptional regulatory network of yeast: Functional robustness. BMC Systems Biology, 3:35, 2009. [ bib ]
[26] F. Emmert-Streib and M. Dehmer. Fault tolerance of information processing in gene networks. Physica A: Statistical Mechanics and its Applications, 388(4):541--548, 2009. [ bib ]
[25] M. Dehmer and F. Emmert-Streib. The structural information content of chemical networks. Zeitschrift für Naturforschung A, 63a:155--158, 2008. [ bib ]
[24] M. Dehmer, S. Borgert, and F. Emmert-Streib. Entropy bounds for hierarchical molecular networks. PLoS ONE, 3(8):e3079, 2008. [ bib ]
[23] F. Emmert-Streib and M. Dehmer. Robustness in scale-free networks: Comparing directed and undirected networks. International Journal of Modern Physics C, 19(5):717--726, 2008. [ bib ]
[22] M. Dehmer and F. Emmert-Streib. Structural information content of networks: Graph entropy based on local vertex functionals. Computational Biology and Chemistry, 32(2):131--138, 2008. [ bib ]
[21] M. Dehmer, F. Emmert-Streib, and T. Gesell. A comparative analysis of multidimensional features of objects resembling sets of graphs. Applied Mathematics and Computation, 196(1):221--235, 2008. [ bib ]
[20] F. Emmert-Streib and M. Dehmer. Nonlinear time series prediction based on a power-law noise model. International Journal of Modern Physics C, 18(12):1839 -- 1852, 2007. [ bib ]
[19] L Chen, F. Emmert-Streib, and J.D. Storey. Harnessing naturally randomized transcription to infer regulatory relationships among genes. Genome Biology, 8(10):R219, 2007. [ bib ]
[18] F. Emmert-Streib and A. Mushegian. A topological algorithm for identification of structural domains of proteins. BMC Bioinformatics, 8:237, 2007. [ bib ]
[17] F. Emmert-Streib. The chronic fatigue syndrome: A comparative pathway analysis. Journal of Computational Biology, 14(7):961--972, 2007. [ bib ]
[16] M. Dehmer and F. Emmert-Streib. Structural similarity of directed universal hierarchical graphs: A low computational complexity approach. Applied Mathematics and Computation, 194(1), 2007. [ bib ]
[15] F. Emmert-Streib and M. Dehmer. Information theoretic measures of UHG graphs with low computational complexity. Applied Mathematics and Computation, 190(2):1783--1794, 2007. [ bib ]
[14] M. Dehmer and F. Emmert-Streib. Comparing large graphs efficiently by margines of feature vectors. Applied Mathematics and Computation, 188(2):1699--1710, 2007. Remark: Both authors contributed equally to this work. [ bib ]
[13] F. Emmert-Streib and M. Dehmer. Topolocial mappings between graphs, trees and generalized trees. Applied Mathematics and Computation, 186(2):1326--1333, 2007. [ bib ]
[12] F. Emmert-Streib. A heterosynaptic learning rule for neural networks. International Journal of Modern Physics C, 17(10):1501--1520, 2006. [ bib ]
[11] M. Dehmer, F. Emmert-Streib, and O. Wolkenhauer. Perspectives of graph mining techniques. Rostocker Informatik Berichte, 30(2):47--56, 2006. [ bib ]
[10] M. Dehmer, F. Emmert-Streib, and J. Kilian. A similarity measure for graphs with low computational complexity. Applied Mathematics and Computation, 182(1):447--459, 2006. [ bib ]
[9] F. Emmert-Streib. Algorithmic computation of knot polynomials of secondary structure elemtents of proteins. Journal of Computational Biology, 13(8):1503--1512, 2006. [ bib ]
[8] F. Emmert-Streib. Influence of the neural network topology on the learning dynamics. Neurocomputing, 69(10-12):1179--1182, 2006. [ bib ]
[7] M. Dehmer, F. Emmert-Streib, A. Mehler, and J. Kilian. Measuring the structural similarity of web-based documents: A novel approach. International Journal of Computational Intelligence, 3(1):1--7, 2006. [ bib ]
[6] F. Emmert-Streib, M. Dehmer, J. Liu, and M. Mühlhäuser. Ranking genes from dna microarray data of cervical cancer by a local tree comparison. International Journal of Biomedical Sciences, 1(1):17--22, 2006. [ bib ]
[5] F. Emmert-Streib. Active learning in recurrent neural networks facilitated by an hebb-like learning rule with memory. Neural Information Processing - Letters and Reviews, 9(2):31--40, 2005. [ bib ]
[4] F. Emmert-Streib. Stochastic Sznajd model in open community. International Journal of Modern Physics C, 16(11):1693--1700, 2005. [ bib ]
[3] F. Emmert-Streib. Self-organized annealing in laterally inhibited neural networks shows power law decay. Neural Information Processing - Letters and Reviews, 7(1):29--38, 2005. [ bib ]
[2] J.R. Otterpohl, F. Emmert-Streib, and K. Pawelzik. A constrained HMM-based approach to the estimation of perceptual switching dynamics in pigeons. Neurocomputing, 38-40:1495--1501, 2001. [ bib ]
[1] J.R. Otterpohl, J.D. Haynes, F. Emmert-Streib, G. Vetter, and K. Pawelzik. Extracting the dynamics of perceptual switching from noisy behaviour: An application of hidden markov modeling to pecking data from pigeons. Journal of Physiology (Paris), 94(5-6):555--567, 2000. [ bib ]

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