Publications

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Journal publications
  1. Emmert-Streib, F. (2023). What is the role of AI for digital twins? AI, 4(3), 721–728.
  2. Yang, Z., & Emmert-Streib, F. (2023). Threshold-learned CNN for multi-label text classification of electronic health records. IEEE Access.
  3. Emmert-Streib, F., Tripathi, S., & Dehmer, M. (2023). Human Team Behavior and Predictability in the Massively Multiplayer Online Game WOT Blitz. ACM Trans. Web, 18(1). doi:10.1145/3617509
  4. Emmert-Streib, F. (2023). Importance of critical thinking to understand ChatGPT. European Journal of Human Genetics, 1–2.
  5. Emmert-Streib, F. (2023). Can ChatGPT understand genetics? European Journal of Human Genetics, 1–2.
  6. Emmert-Streib, F. (2023). Defining a Digital Twin: A Data Science-Based Unification. Machine Learning and Knowledge Extraction, 5(3), 1036–1054.
  7. Emmert-Streib, F., Tripathi, S., & Dehmer, M. (2023). Analyzing the Scholarly Literature of Digital Twin Research: Trends, Topics and Structure. IEEE Access, 11, 69649–69666. doi:10.1109/ACCESS.2023.3290488
  8. Ghorbani, M., Alidehi-Ravandi, R., Dehmer, M., & Emmert-Streib, F. (2023). A Study of Roots of a Certain Class of Counting Polynomials. Mathematics, 11(13), 2876.
  9. Farea, A., Yang, Z., Duong, K., Perera, N., & Emmert-Streib, F. (2022). Evaluation of Question Answering Systems: Complexity of judging a natural language. arXiv Preprint arXiv:2209. 12617.
  10. Emmert-Streib, F., & Dehmer, M. (2022). Taxonomy of machine learning paradigms: A data-centric perspective. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(5), e1470.
  11. Emmert-Streib, F., & Yli-Harja, O. (2022). What Is a Digital Twin? Experimental Design for a Data-Centric Machine Learning Perspective in Health. International Journal of Molecular Sciences, 23(21), 13149.
  12. Emmert-Streib, F. (2022). Severe testing with high-dimensional omics data for enhancing biomedical scientific discovery. Npj Systems Biology and Applications, 8(1), 1–11.
  13. Dehmer, M., Emmert-Streib, F., Tratnik, N., & Pleteršek, P. Ž. (2022). Szeged-like entropies of graphs. Applied Mathematics and Computation, 431, 127325.Ma, Y., 
  14. Dehmer, M., Künzi, U.-M., Tripathi, S., Ghorbani, M., Tao, J., & Emmert-Streib, F. (2022). The usefulness of topological indices. Information Sciences, 606, 143–151.
  15. Yang, Z., Kanniainen, J., Krogerus, T., & Emmert-Streib, F. (2022). Prognostic modeling of predictive maintenance with survival analysis for mobile work equipment. Scientific Reports, 12(1), 1–20.
  16. Perera, N., Nguyen, T. T. L., Dehmer, M., & Emmert-Streib, F. (2022). Comparison of Text Mining Models for Food and Dietary Constituent Named-Entity Recognition. Machine Learning and Knowledge Extraction, 4(1), 254–275.
  17. Bashath, S., Perera, N., Tripathi, S., Manjang, K., Dehmer, M., & Emmert-Streib, F. (2022). A data-centric review of deep transfer learning with applications to text data. Information Sciences, 585, 498–528.
  18. Holzinger, A., Dehmer, M., Emmert-Streib, F., Cucchiara, R., Augenstein, I., Del Ser, J., … Díaz-Rodríguez, N. (2022). Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence. Information Fusion, 79, 263–278.
  19. Tripathi, S., Jodlbauer, H., Mittermayr, C., & Emmert-Streib, F. (2022). Identifying key interactions between process variables of different material categories using mutual information-based network inference method. Procedia Computer Science, 200, 1550–1564.
  20. Ma, Y., Dehmer, M., Künzi, U.-M., Mowshowitz, A., Tripathi, S., Ghorbani, M., & Emmert-Streib, F. (2021). Relationships between symmetry-based graph measures. Information Sciences, 581, 291–303.
  21. Emmert-Streib, F., Manjang, K., Dehmer, M., Yli-Harja, O., & Auvinen, A. (2021). Are There Limits in Explainability of Prognostic Biomarkers? Scrutinizing Biological Utility of Established Signatures. Cancers, 13(20), 5087.
  22. Hu, B., Dehmer, M., Emmert-Streib, F., & Zhang, B. (2021). Analysis of the real number of infected people by COVID-19: A system dynamics approach. Plos One, 16(3), e0245728.
  23. Stupnikov, A., McInerney, C. E., Savage, K. I., McIntosh, S. A., Emmert-Streib, F., Kennedy, R., … McArt, D. G. (2021). Robustness of differential gene expression analysis of RNA-seq. Computational and Structural Biotechnology Journal, 19, 3470–3481.
  24. Ghorbani, M., Dehmer, M., Lofti, A., Amraei, N., Mowshowitz, A., & Emmert-Streib, F. (2021). On the relationship between PageRank and automorphisms of a graph. Information Sciences, 579.
  25. Manjang, K., Yli-Harja, O., Dehmer, M., & Emmert-Streib, F. (2021). Limitations of explainability for established prognostic biomarkers of prostate cancer. Frontiers in Genetics, 12.de Matos Simoes, R., 
  26. Emmert-Streib, F., Duggan, B., Ruddock, M., O’rourke, D., O’kane, H., … Williamson, K. (08 2021). Confounding Effects of Heterogeneity and the Impact on the Accuracy of Urothelial Cancer Classifiers for Haematuria Patients. Medical Sciences, 5.
  27. Jalali-Rad, M., Ghorbani, M., Dehmer, M., & Emmert-Streib, F. (2021). Orbit Entropy and Symmetry Index Revisited. Mathematics, 9(10), 1086.
  28. Emmert-Streib, F., & Dehmer, M. (2021). Data-driven computational social network science: Predictive and inferential models for Web-enabled scientific discoveries. Frontiers in Big Data, 4.
  29. Emmert-Streib, F. (2021). From the Digital Data Revolution toward a Digital Society: Pervasiveness of Artificial Intelligence. Machine Learning and Knowledge Extraction, 3(1), 284–298.
  30. Tripathi, S., Muhr, D., Brunner, M., Jodlbauer, H., Dehmer, M., & Emmert-Streib, F. (2021). Ensuring the robustness and reliability of data-driven knowledge discovery models in production and manufacturing. Frontiers in Artificial Intelligence, 4, 22.
  31. Manjang, K., Tripathi, S., Yli-Harja, O., Dehmer, M., Glazko, G., & Emmert-Streib, F. (2021). Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning. Scientific Reports, 11(1), 1–18.
  32. Varmuza, K., Dehmer, M., Emmert-Streib, F., & Filzmoser, P. (2021). Automorphism Groups of Alkane Graphs. Croatica Chemica Acta, 94(1), 47–58.
  33. Cheng, T., Dehmer, M., Emmert-Streib, F., Li, Y., & Liu, W. (2021). Properties of Commuting Graphs over Semidihedral Groups. Symmetry, 13(1), 103.
  34. Emmert-Streib, F., Yli-Harja, O., & Dehmer, M. (2020a). Artificial Intelligence: A Clarification of Misconceptions, Myths and Desired Status. Front. Artif. Intell, 3, 524339.
  35. Manjang, K., Tripathi, S., Yli-Harja, O., Dehmer, M., & Emmert-Streib, F. (2020). Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance. Scientific Reports, 10(1), 1–16.
  36. Yin, Q., Wang, Z., Xia, C., Dehmer, M., Emmert-Streib, F., & Jin, Z. (2020). A novel epidemic model considering demographics and intercity commuting on complex dynamical networks. Applied Mathematics and Computation, 386, 125517.
  37. Wan, P., Chen, X., Tu, J., Dehmer, M., Zhang, S., & Emmert-Streib, F. (2020). On graph entropy measures based on the number of independent sets and matchings. Information Sciences, 516, 491–504. doi:10.1016/j.ins.2019.11.020
  38. Azemati, H., Jam, F., Ghorbani, M., Dehmer, M., Ebrahimpour, R., Ghanbaran, A., & Emmert-Streib, F. (2020). The Role of Symmetry in the Aesthetics of Residential Building Façades Using Cognitive Science Methods. Symmetry, 12(9), 1438.
  39. Perera, N., Dehmer, M., & Emmert-Streib, F. (2020). Named Entity Recognition and Relation Detection for Biomedical Information Extraction. Frontiers in Cell and Developmental Biology, 8, 673.
  40. Ghorbani, M., Dehmer, M., Cao, S., Feng, L., Tao, J., & Emmert-Streib, F. (2020). On the zeros of the partial Hosoya polynomial of graphs. Information Sciences, 524, 199–215. doi:10.1016/j.ins.2020.03.011
  41. Emmert-Streib, F., Yli-Harja, O., & Dehmer, M. (2020b). Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective. WIREs Data Mining and Knowledge Discovery, 10, e1368.
  42. Dehmer, M., Emmert-Streib, F., Mowshowitz, A., Ilić, A., Chen, Z., Yu, G., … Tao, J. (2020). Relations and bounds for the zeros of graph polynomials using vertex orbits. Applied Mathematics and Computation, 380, 125239.
  43. Ghorbani, M., Dehmer, M., Maimani, H., Maddah, S., Roozbayani, M., & Emmert-Streib, F. (2020). The watching system as a generalization of identifying code. Applied Mathematics and Computation, 380, 125302.
  44. Nadjafi-Arani, M. J., Mirzargar, M., Emmert-Streib, F., & Dehmer, M. (2020). Partition and Colored Distances in Graphs Induced to Subsets of Vertices and Some of Its Applications. Symmetry, 12(12), 2027.
  45. Ghorbani, M., Dehmer, M., & Emmert-Streib, F. (2020b). Properties of Entropy-Based Topological Measures of Fullerenes. Mathematics, 8(5), 740.
  46. Doan, P., Musa, A., Murugesan, A., Sipilä, V., Candeias, N. R., Emmert-Streib, F., … Kandhavelu, M. (2020). Glioblastoma Multiforme Stem Cell Cycle Arrest by Alkylaminophenol through the Modulation of EGFR and CSC Signaling Pathways. Cells, 9(3), 681.
  47. Dehmer, M., Chen, Z., Emmert-Streib, F., Mowshowitz, A., Varmuza, K., Feng, L., … Tao, J. (2020). The Orbit-Polynomial: A Novel Measure of Symmetry in Networks. IEEE Access, 8, 36100–36112.
  48. Gao, H., Tao, J., Dehmer, M., Emmert-Streib, F., Sun, Q., Chen, Z., … Zhou, Q. (2020). In-flight Wind Field Identification and Prediction of Parafoil Systems. Applied Sciences, 10(6), 1958.
  49. Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., & Dehmer, M. (2020). An introductory review of deep learning for prediction models with big data. Frontiers in Artificial Intelligence, 3, 4.
  50. Yang, Z., Dehmer, M., Yli-Harja, O., & Emmert-Streib, F. (2020). Combining deep learning with token selection for patient phenotyping from electronic health records. Scientific Reports.
  51. Xu, P., Li, W., Tao, J., Dehmer, M., Emmert-Streib, F., Xie, G., … Zhou, Q. (2020). Distributed Event-Triggered Circular Formation Control for Multiple Anonymous Mobile Robots With Order Preservation and Obstacle Avoidance. IEEE Access, 8, 167288–167299.
  52. Ghorbani, M., Dehmer, M., & Emmert-Streib, F. (2020a). On the Degeneracy of the Orbit Polynomial and Related Graph Polynomials. Symmetry, 12(10), 1643.
  53. Emmert-Streib, F., Yli-Harja, O., & Dehmer, M. (2019). Utilizing social media data for psychoanalysis to study human personality. Frontiers in Psychology, 10, 2596.
  54. Mowshowitz, A., Dehmer, M., & Emmert-Streib, F. (2019). A Note on Graphs with Prescribed Orbit Structure. Entropy, 21(11), 1118.
  55. Emmert-Streib, F., Dehmer, M., & Yli-Harja, O. (2019). Ensuring Quality Standards and Reproducible Research for Data Analysis Services in Oncology: A Cooperative Service Model. Frontiers in Cell and Developmental Biology, 7.
  56. Emmert-Streib, Frank, & Dehmer, M. (2019). Network science: From chemistry to digital society. Front. Young Minds, 7(49). doi:10.3389/frym.2019.00049
  57. Dehmer, M., Chen, Z., Emmert-Streib, F., Tripathi, S., Mowshowitz, A., Levitchi, A., … Tao, J. (2019). Measuring the complexity of directed graphs: A polynomial-based approach. PloS One, 14(11).
  58. Smolander, J., Stupnikov, A., Glazko, G., Dehmer, M., & Emmert-Streib, F. (2019). Comparing biological information contained in mRNA and non-coding RNAs for classification of lung cancer patients. BMC Cancer, 19(1), 1176.
  59. Musa, A., Tripathi, S., Dehmer, M., & Emmert-Streib, F. (2019). L1000 Viewer: A search engine and web interface for the LINCS data repository. Frontiers in Genetics, 10, 557.
  60. Emmert-Streib, F., & Dehmer, M. (2019d). Introduction to Survival Analysis in Practice. Machine Learning and Knowledge Extraction, 1(3), 1013–1038. doi:10.3390/make1030058
  61. Glazko, G., Zybailov, B., Emmert-Streib, F., Baranova, A., & Rahmatallah, Y. (2019). Proteome-transcriptome alignment of molecular portraits achieved by self-contained gene set analysis: Consensus colon cancer subtypes case study. PloS One, 14(8), e0221444.
  62. Emmert-Streib, F., & Dehmer, M. (2019f). Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference. Machine Learning and Knowledge Extraction, 1(3), 945–961. doi:10.3390/make1030054
  63. Yu, G., Dehmer, M., Emmert-Streib, F., & Jodlbauer, H. (2019). Hermitian normalized Laplacian matrix for directed networks. Information Sciences, 495, 175–184.
  64. Dehmer, M., Chen, Z., Mowshowitz, A., Jodlbauer, H., Emmert-Streib, F., Shi, Y., … Xia, C. (2019). On the degeneracy of the Randić entropy and related graph measures. Information Sciences, 501, 680–687.
  65. Ghorbani, M., Dehmer, M., Zangi, S., Mowshowitz, A., & Emmert-Streib, F. (2019). A Note on Distance-Based Entropy of Dendrimers. Axioms, 8(3). doi:10.3390/axioms8030098
  66. Liu, W., Ban, J., Feng, L., Cheng, T., Emmert-Streib, F., & Dehmer, M. (2019). The Maximum Hosoya Index of Unicyclic Graphs with Diameter at Most Four. Symmetry, 11(8). doi:10.3390/sym11081034
  67. Ghorbani, M., Dehmer, M., Rajabi-Parsa, M., Mowshowitz, A., & Emmert-Streib, F. (2019). On Properties of Distance-Based Entropies on Fullerene Graphs. Entropy, 21(5). doi:10.3390/e21050482
  68. Ghorbani, M., Dehmer, M., Mowshowitz, A., Tao, J., & Emmert-Streib, F. (2019). The Hosoya Entropy of Graphs Revisited. Symmetry, 11(8). doi:10.3390/sym11081013
  69. Dehmer, M., Chen, Z., Shi, Y., Zhang, Y., Tripathi, S., Ghorbani, M., … Emmert-Streib, F. (2019). On efficient network similarity measures. Applied Mathematics and Computation, 362, 124521.
  70. Wan, P., Tu, J., Dehmer, M., Zhang, S., & Emmert-Streib, F. (2019). Graph entropy based on the number of spanning forests of c-cyclic graphs. Applied Mathematics and Computation, 363, 124616.
  71. Musa, A., Tripathi, S., Dehmer, M., Yli-Harja, O., Kauffman, S. A., & Emmert-Streib, F. (2019). Systems pharmacogenomic Landscape of Drug similarities from LINCs data: Drug Association Networks. Scientific Reports, 9(1), 7849.
  72. Smolander, J., Dehmer, M., & Emmert-Streib, F. (2019). Comparing deep belief networks with support vector machines for classifying gene expression data from complex disorders. FEBS Open Bio, 9(7), 1232–1248.
  73. Emmert-Streib, F., & Dehmer, M. (2019e). Large-Scale Simultaneous Inference with Hypothesis Testing: Multiple Testing Procedures in Practice. Machine Learning and Knowledge Extraction, 1(2), 653–683. doi:10.3390/make1020039
  74. Moore, D., de Matos Simoes, R., Dehmer, M., & Emmert-Streib, F. (2019). Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data. Current Genomics, 20(1), 38–48. doi:10.2174/1389202919666181107122005
  75. Ghorbani, M., Dehmer, M., Taghvayi-Yazdelli, V., & Emmert-Streib, F. (2019). A Graph Theoretic Approach to Construct Desired Cryptographic Boolean Functions. Axioms, 8(2), 40.
  76. Doan, P., Musa, A., Candeias, N. R., Emmert-Streib, F., Yli-Harja, O. P., & Kandhavelu, M. (2019). Alkylaminophenol induces G1/S phase cell cycle arrest in glioblastoma cells through p53 and cyclin-dependent kinase signaling pathway. Frontiers in Pharmacology, 10, 330.
  77. Emmert-Streib, F., & Dehmer, M. (2019b). Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error. Machine Learning and Knowledge Extraction, 1(1), 521–551.
  78. Emmert-Streib, F., Tripathi, S., & Dehmer, M. (2019). Constrained covariance matrices with a biologically realistic structure: Comparison of methods for generating high-dimensional Gaussian graphical models. Front. Appl. Math. Stat., 5, 17.
  79. Emmert-Streib, F., Moutari, S., & Dehmer, M. (2019). A comprehensive survey of error measures for evaluating binary decision making in data science. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1303.
  80. Azam, M. F., Musa, A., Dehmer, M., Yli-Harja, O. P., & Emmert-Streib, F. (2019). Global Genetics Research in Prostate Cancer: A Text Mining and Computational Network Theory Approach. Frontiers in Genetics, 10, 70.
  81. Ghorbani, M., Dehmer, M., Rajabi-Parsa, M., Emmert-Streib, F., & Mowshowitz, A. (2019). Hosoya entropy of fullerene graphs. Applied Mathematics and Computation, 352, 88–98. doi:10.1016/j.amc.2019.01.024
  82. Emmert-Streib, F., & Dehmer, M. (2019c). High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection. Machine Learning and Knowledge Extraction, 1(1), 359–383. doi:10.3390/make1010021
  83. Viswanathan, A., Kute, D., Musa, A., Konda Mani, S., Sipila, V., Emmert-Streib, F., … Kandhavelu, M. (2019). 2-(2-(2,4-dioxopentan-3-ylidene)hydrazineyl)benzonitrile as novel inhibitor of receptor tyrosine kinase and PI3K/AKT/mTOR signaling pathway in glioblastoma. European Journal of Medicinal Chemistry, 166, 291–303. doi:10.1016/j.ejmech.2019.01.021
  84. Dehmer, M., Chen, Z., Emmert-Streib, F., Mowshowitz, A., Shi, Y., Tripathi, S., & Zhang, Y. (2019). Towards detecting structural branching and cyclicity in graphs: A polynomial-based approach. Information Sciences, 471, 19–28. doi:10.1016/j.ins.2018.08.043
  85. Emmert-Streib, F., & Dehmer, M. (2019a). Defining Data Science by a Data-Driven Quantification of the Community. Machine Learning and Knowledge Extraction, 1(1), 235–251.
  86. Musa, A., Dehmer, M., Yli-Harja, O., & Emmert-Streib, F. (2018). Exploiting Genomic Relations in Big Data Repositories by Graph-Based Search Methods. Mach. Learn. Knowl. Extr., 1(1), 205–210.
  87. Stupnikov, A., O’Reilly, P. G., McInerney, C. E., Roddy, A. C., Dunne, P. D., Gilmore, A., … McArt, D. (2018). Impact of Variable RNA-Sequencing Depth on Gene Expression Signatures and Target Compound Robustness: Case Study Examining Brain Tumor (Glioma) Disease Progression. JCO Precision Oncology, 2, 1–17.
  88. Musa, A., Tripathi, S., Kandhavelu, M., Dehmer, M., & Emmert-Streib, F. (2018). Harnessing the biological complexity of Big Data from LINCS gene expression signatures. PloS One, 13(8), e0201937.
  89. Emmert-Streib, F., & Dehmer. (2018a). A Machine Learning Perspective on Personalized Medicine: An Automatized, Comprehensive Knowledge Base with Ontology for Pattern Recognition. Mach. Learn. Knowl. Extr., 1(1), 149–156.
  90. Emmert-Streib, F., Yli-Harja, O., & Dehmer, M. (2018). Data analytics applications for streaming data from social media: What to predict? Frontiers in Big Data, 1, 1.
  91. Emmert-Streib, F., S., Tripathi, S., Yli-Harja, O., & Dehmer, M. (2018). Understanding the world economy in terms of networks: A survey of data-based network science approaches on economic networks. Front. Appl. Math. Stat., 4, 37.
  92. Musa, A., Ghoraie, L., Zhang, S.-D., Glazko, G., Yli-Harja, O., Dehmer, M., … Emmert-Streib, F. (2018). A Review of Connectivity Mapping and Computational Approaches in Pharmacogenomics. Briefings in Bioinformatics, 19(3), 506–523.
  93. Dehmer, M., Chen, Z., Emmert-Streib, F., Shi, Y., & Tripathi, S. (2018). Graph measures with high discrimination power revisited: A random polynomial approach. Information Sciences, 467, 407–414. doi:10.1016/j.ins.2018.07.072
  94. Iantovics, B., Niazi, M. A., Gligor, A., Szilagyi, S., Dehmer, M., Emmert-Streib, F., & Tokody, D. (2018). CoopRA Algorithm for Universal Characterization of the Experimental Evaluation Results of Cooperative Multiagent Systems. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(3), 37–49.
  95. Baltakys, K., Kanniainen, J., & Emmert-Streib, F. (2018). Multilayer Aggregation of Investor Trading Networks. Scientific Reports, 1, 8198.
  96. Emmert-Streib, F., Musa, A., Tripathi, S., Yli-Harja, O., Baltakys, K., Kanniainen, J., & Dehmer, M. (2018). Computational Analysis of structural properties of Economic Networks. Journal Of Network Theory In Finance, 4(3), 1–32.
  97. Emmert-Streib, F., & Dehmer. (2018b). Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations? Mach. Learn. Knowl. Extr., 1(1), 138–148.
  98. Dehmer, M., Chen, Z., Emmert-Streib, F., Shi, Y., Tripathi, S., Musa, A., & Mowshowitz, A. (2018). Properties of graph distance measures by means of discrete inequalities. Applied Mathematical Modelling, 59, 739–749. doi:10.1016/j.apm.2018.01.027
  99. Iantovics, L. B., Dehmer, M., & Emmert-Streib, F. (2018). MetrIntSimil-An Accurate and Robust Metric for Comparison of Similarity in Intelligence of Any Number of Cooperative Multiagent Systems. Symmetry, 10(2), 48. doi:10.3390/sym10020048
  100. Dehmer, M., & Emmert-Streib, F. (2018). Comments to ‘Quantification of network structural dissimilarities’ published by Schieber et al. Mathematical Methods in the Applied Sciences, 41(14), 5711–5713.
  101. de Matos Simoes, R., Emmert-Streib, F., Ruddock, M. W., O’Rourke, D., Duggan, B., O’Kane, H. F., … Williamson, K. E. (2017). 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. doi:10.1016/j.urolonc.2017.06.062
  102. Dehmer, M., Emmert-Streib, F., & Shi, Y. (2017). Quantitative Graph Theory: A new branch of graph theory and network science. Information Sciences, 418, 575–580.
  103. Tripathi, S., Lloyd-Price, J., Ribeiro, A., Yli-Harja, O., Dehmer, M., & Emmert-Streib, F. (2017). sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters. BMC Bioinformatics, 18(1), 325.
  104. Iantovics, L. B., Emmert-Streib, F., & Arik, S. (2017). MetrIntMeas a novel metric for measuring the intelligence of a swarm of cooperating agents. Cognitive Systems Research, 45, 17–29.
  105. Rahmatallah, Y., Zybailov, B., Emmert-Streib, F., & Glazko, G. (2017). GSAR: Bioconductor package for Gene Set analysis in R. BMC Bioinformatics, 18(1), 61. doi:10.1186/s12859-017-1482-6
  106. Dehmer, M., Emmert-Streib, F., Hu, B., Shi, Y., Stefu, M., & Tripathi, S. (2017). Highly unique network descriptors based on the roots of the permanental polynomial. Information Sciences, 408, 176–181. doi:10.1016/j.ins.2017.04.041
  107. Chen, Z., Dehmer, M., Emmert-Streib, F., Mowshowitz, A., & Shi, Y. (2017). Toward Measuring Network Aesthetics Based on Symmetry. Axioms, 6(2), 12. doi:10.3390/axioms6020012
  108. Emmert-Streib, F., Dehmer, M., & Yli-Harja, O. (2016). Against Dataism and for Data Sharing of Big Biomedical and Clinical Data with Research Parasites. Frontiers in Genetics, 7, 154. doi:10.3389/fgene.2016.00154
  109. Tripathi, S., Moutari, S., Dehmer, M., & Emmert-Streib, F. (2016). Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules. BMC Bioinformatics, 17(1), 1–18. doi:10.1186/s12859-016-0979-8
  110. Emmert-Streib, F., Tuomisto, L., & Yli-Harja, O. (2016). The Need for Formally Defining ‘Modern Medicine’ by Means of Experimental Design. Frontiers in Genetics, 7, 60. doi:10.3389/fgene.2016.00060
  111. Stupnikov, A., Tripathi, S., de Matos Simoes, R., McArt, D., Salto-Tellez, M., Glazko, G., & Emmert-Streib, F. (2016). samExploreR: Exploring reproducibility and robustness of RNA-seq results based on SAM files. Bioinformatics, 475.
  112. Buckley, N., Haddock, P., De Matos Simoes, R., Parkes, E., Irwin, G., Emmert-Streib, F., … Mullan, P. (2016). A BRCA1 deficient, NFκB driven immune signal predicts good outcome in Triple Negative breast cancer. Oncotarget, 7(15), 19884–19896.
  113. Emmert-Streib, F., Dehmer, M., & Shi, Y. (2016). Fifty Years of Graph Matching, Network Alignment and Network Comparison. Information Sciences, 346–347, 180–197.Emmert-Streib, F., Moutari, S., & Dehmer, M. (2016). The process of analyzing data is the emergent feature of data science. Frontiers in Genetics, 7, 12.
  114. Rahmatallah, Y., Emmert-Streib, F., & Glazko, G. (2016). Gene set analysis approaches for RNA-seq data: performance evaluation and application guideline. Briefings in Bioinformatics, 17(3), 393–407. doi:10.1093/bib/bbv069
  115. Ruddock, M. W., de Matos Simoes, R., Rourke, D. O., Duggan, B., & Stevenson, E. al, M. (2015). 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.
  116. Emmert-Streib, F., & Dehmer, M. (2015). Biological networks: The microscope of the 21st century? Frontiers in Genetics, 6(307).
  117. Dehmer, M., Emmert-Streib, F., & Shi, Y. (2015). Graph distance measures based on topological indices revisited. Applied Mathematics and Computation, 266, 623–633. doi:10.1016/j.amc.2015.05.072
  118. Alvi, M., McArt, D., Kelly, P., Fuchs, M.-A., Alderdice, M., McCabe, C., … Salto-Tellez, M. (2015). Comprehensive molecular pathology analysis of small bowel adenocarcinoma reveals novel targets with potential clinical utility. Oncotarget, 6(25), 20863–20874.
  119. de Matos Simoes, S., Dalleau, Williamson, K. E., & Emmert-Streib, F. (2015). Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data. BMC Systems Biology, 9, 21.
  120. Chen, Z., Dehmer, M., Emmert-Streib, F., & Shi, Y. (2015). Entropy of weighted graphs with Randić weights. Entropy, 17, 3710–3723.
  121. Shi, Y., Emmert-Streib, F., & Dehmer, M. (2015). Graph Distance Mesures Based on Topological Indices Revisited. Applied Mathematics and Computation, 266, 623–633.
  122. Dehmer, M., Kurt, Z., Emmert-Streib, F., Them, C., Schulc, E., & Hofer, S. (2015). Structural Analysis of Treatment Cycles Representing Transitions Between Nursing Organizational Units Inferred from Diabetes. PLoS ONE, 10(6), e0127152.
  123. Olsen, C., Fleming, K., Prendergast, N., Rubio, R., Emmert-Streib, F., Bontempi, G., … Haibe-Kains, B. (2015). Using shRNA experiments to validate gene regulatory networks. Genomics Data, 4(0), 123–126.
  124. Stupnikov, A., Glazko, G., & Emmert-Streib, F. (2015). Effects of subsampling on characteristics of RNA-seq data from triple negative breast cancer patients. Chinese Journal of Cancer, 34, 36.
  125. Dehmer, M., Emmert-Streib, F., Shi, Y., Stefu, M., & Tripathi, S. (10 2015). Discrimination Power of Polynomial-Based Descriptors for Graphs by Using Functional Matrices. PLoS ONE, 10(10), 1–10. doi:10.1371/journal.pone.0139265
  126. Rahmatallah, Y., Emmert-Streib, F., & Glazko, G. (2014a). Comparative evaluation of gene set analysis approaches for RNA-Seq data. BMC Bioinformatics, 15, 397.
  127. Emmert-Streib, F., Dehmer, M., & Haibe-Kains, B. (2014b). Untangling statistical and biological models to understand network inference: The need for a genomics network ontology. Front. Genet., 5, 299.
  128. Kraus, V., Dehmer, M., & Emmert-Streib, F. (2014). Probabilistic Inequalities for Evaluating Structural Network Measures. Information Sciences, 288, 220–245.
  129. Emmert-Streib, F., Dehmer, M., & Haibe-Kains, B. (2014a). Gene regulatory networks and their applications: Understanding biological and medical problems in terms of networks. Front. Cell Dev. Biol., 2, 38.
  130. Haibe-Kains, B., & Emmert-Streib, F. (2014). Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics. Front. Genet., 5, 221.
  131. Tripathi, S., Dehmer, M., & Emmert-Streib, F. (2014). NetBioV: An R package for visualizing large-scale data in Network Biology. Bioinformatics, 384.
  132. Olsen, C., Bontempi, G., Emmert-Streib, F., Quackenbush, J., & Haibe-Kains, B. (2014). Relevance of different prior knowledge sources for inferring gene interaction networks. Front. Genet., 5, 177.
  133. Dehmer, M., Emmert-Streib, F., & Shi, Y. (2014). Interrelations of Graph Distance Measures Based on Topological Indices. PLoS One, 9(4), e94985.
  134. Chen, Z., Dehmer, M., Emmert-Streib, F., & Shi, Y. (2014). Entropy bounds for dendrimers. Applied Mathematics and Computation, 242, 462–472.
  135. Emmert-Streib, F. (2014). Enhancing our understanding of ways to analyze metagenomes. Front. Genet., 5, 108.
  136. Emmert-Streib, F., de Matos Simoes, R., Glazko, G., McDade, S., Haibe-Kains, B., Holzinger, A., … Campbell, F. (2014a). Functional and genetic analysis of the colon cancer network. BMC Bioinformatics, 15, 6.
  137. Emmert-Streib, F., Zhang, S.-D., & Hamilton, P. (2015). Computational Cancer Biology: Education is a key to many locks. BMC Cancer, 15, 7.
  138. Emmert-Streib, F., Zhang, S.-D., & Hamilton, P. (2014c). Dry computational approaches for wet medical problems. Journal of Translational Medicine, 12, 36.
  139. Emmert-Streib, F., de Matos Simoes, R., Mullan, P., Haibe-Kains, B., & Dehmer, M. (2014b). The gene regulatory network for breast cancer: Integrated regulatory landscape of cancer hallmarks. Front. Genet., 5, 15.
  140. Emmert-Streib, F., Zhang, S.-D., & Hamilton, P. (2014d). Report from the 2nd Summer School in Computational Biology organized by the Queen’s University of Belfast. Genomics Data, 2, 37–39.
  141. Olsen, C., Fleming, K., Prendergast, N., Rubio, R., Emmert-Streib, F., Bontempi, G., … Quackenbush, J. (2014). Inference and validation of predictive gene networks from biomedical literature and gene expression data. Genomics, 103(5–6), 329–336.
  142. Gokmen, A., Kurt, Z., Dehmer, M., & Emmert-Streib, F. (2014). Netmes: Assessing gene network inference algorithms by ensemble network-based measures. Evolutionary Bioinformatics, 10, 1–9.
  143. Dehmer, M., Emmert-Streib, F., & Grabner, M. (2014). A Computational Approach to Construct a Multivariate Complete Graph Invariant. Information Sciences, 260, 200–208.
  144. Rahmatallah, Y., Emmert-Streib, F., & Glazko, G. (2014b). Gene Sets Net Correlations Analysis (GSNCA): A multivariate differential coexpression test for gene sets. Bioinformatics, 30(3), 360–368.
  145. de Matos Simoes, R., Dehmer, M., & Emmert-Streib, F. (2013a). B-cell lymphoma gene regulatory networks: Biological consistency among inference methods. Front. Genet., 4, 281.
  146. Dehmer, M., Emmert-Streib, F., & Tripathi, S. (2013). Large-Scale Evaluation of Molecular Descriptors by Means of Clustering. PLoS ONE, 8(12), e83956.
  147. Dehmer, M., Mueller, L. A. J., & Emmert-Streib, F. (2013). Quantitative Network Measures as Biomarkers for Classifying Prostate Cancer Disease States: A Systems Approach to Diagnostic Biomarkers. PLoS ONE, 8(11), e77602.
  148. Dander, A., Mueller, L. A. J., Gallasch, R., Pabinger, S., Emmert-Streib, F., Graber, A., & Dehmer, M. (2013). [COMMODE] A Large-Scale Database of Molecular Descriptors using compounds from PubChem. Source Code for Biology and Medicine, 8, 22.
  149. Emmert-Streib, F., & Dehmer, M. (2013). Enhancing systems medicine beyond genotype data by dynamic patient signatures: Having information and using it too. Front. Genet., 4, 241.
  150. Dehmer, M., Grabner, M., Mowshowitz, A., & Emmert-Streib, F. (2013). An efficient heuristic approach to detecting graph isomorphism based on combinations of highly discriminating invariants. Advances in Computational Mathematics, 39(2), 311–325.
  151. de Matos Simoes, R., Dehmer, M., & Emmert-Streib, F. (2013b). Interfacing cellular networks of \textitS. cerevisiae and \textitE. coli: Connecting dynamic and genetic information. BMC Genomics, 14, 324.
  152. Dehmer, M., Hackl, W. O., Emmert-Streib, F., Schulc, W., & Them, C. (2013). Network Nursing: Connections between Nursing and Complex Network Science. International Journal of Nursing Knowledge, 24(3), 150–156.
  153. Emmert-Streib, F., Dehmer, M., & Lyardet, F. (2013). Learning Systems Biology: Conceptual Considerations Toward a Web-based Learning Platform. Education Sciences, 3(2), 158–171.
  154. Tripathi, S., Glazko, G. V., & Emmert-Streib, F. (2013). Ensuring the statistical soundness of competitive gene set approaches: gene filtering and genome-scale coverage are essential. Nucleic Acids Research, 6(12), e53354.
  155. Emmert-Streib, F. (2013a). Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: Environmental factors. PeerJ, 1, e10.
  156. Emmert-Streib, F., Abogunrin, F., de Matos Simoes, R., Duggan, B., Ruddock, M. W., Reid, C. N., … Williamson, K. E. (2013). Collectives of diagnostic biomarkers identify high-risk subpopulations of hematuria patients: exploiting heterogeneity in large-scale biomarker data. BMC Medicine, 11(1), 12.
  157. Emmert-Streib, F. (2013b). Personalized medicine: Has it started yet? A reconstruction of the early history. Front Genet, 3, 313.
  158. Emmert-Streib, F. (2013c). Structural properties and complexity of a new network class: Collatz step graphs. PLoS ONE, 8(2), e56461.
  159. Emmert-Streib, F., Tripathi, S., de Matos Simoes, R., Hawwa, A. F., & Dehmer, M. (2013). The human disease network: Opportunities for classification, diagnosis and prediction of disorders and disease genes. Systems Biomedicine, 1(1), 1–8.
  160. Emmert-Streib, F., Tripathi, S., & de Matos Simoes, R. (2012). Harnessing the complexity of gene expression data from cancer: From single gene to structural pathway methods. Biology Direct, 7, 44.
  161. Rahmatallah, Y., Emmert-Streib, F., & Glazko, G. (2012). Gene set analysis for self-contained tests: complex null and specific alternative hypotheses. Bioinformatics, 28(23), 3073–3080.
  162. de Matos Simoes, R., Tripathi, S., & Emmert-Streib, F. (2012). Organizational structure of the peripheral gene regulatory network in B-cell lymphoma. BMC Systems Biology, 6, 38.
  163. Dehmer, M., Grabner, M., Mowshowitz, A., & Emmert-Streib, F. (2012). An efficient heuristic approach to detecting graph isomorphism based on combinations of highly discriminating invariants. Advances in Computational Mathematics, 1–15.
  164. Emmert-Streib, F., de Matos Simoes, R., Tripathi, S., Glazko, G. V., & Dehmer, M. (2012). A Bayesian analysis of the chromosome architecture of human disorders by integrating reductionist data. Scientific Reports, 2, 513.
  165. Tripathi, S., & Emmert-Streib, F. (05 2012). Assessment Method for a Power Analysis to Identify Differentially Expressed Pathways. PLoS ONE, 7(5), e37510.
  166. C.n., R., Stevenson, M., F., A., M.w., R., F., E.-S., J.v., L., & K.e., W. (2012). Standardisation of diagnostic biomarker concentrations in urine; the hematuria caveat. PLoS ONE, 7(12), e53354.
  167. Emmert-Streib, F., Häkkinen, A., & Ribeiro, A. S. (2012). Detecting sequence dependent transcriptional pauses from RNA and protein number time series. BMC Bioinformatics, 13(152).
  168. de Matos Simoes, R., & Emmert-Streib, F. (2012). Bagging statistical network inference from large-scale gene expression data. PLoS ONE, 7(3), e33624.
  169. Emmert-Streib, F. (2012c). Universal construction mechanism for networks from one-dimensional symbol sequences. Applied Mathematics and Computation, 219(3), 1020–1030.
  170. Emmert-Streib, F. (2012b). Limitations of the gene duplication model: Evolution of modules in protein interaction networks. PLoS ONE, 7(4), e35531.
  171. Emmert-Streib, F., & Dehmer, M. (2012). Exploring statistical and population aspects of network complexity. PLoS ONE, 7(5), e34523.
  172. Emmert-Streib, F., Glazko, G. V., Altay, G., & de Matos Simoes, R. (2012). Statistical inference and reverse engineering of gene regulatory networks from observational expression data. Frontiers in Genetics, 3, 8.
  173. Emmert-Streib, F. (2012a). Evolutionary dynamics of the spatial Prisoner’s Dilemma with self-inhibition. Applied Mathematics and Computation, 218(11), 6482–6488.
  174. Mueller, L., Kugler, K., Graber, A., Emmert-Streib, F., & Dehmer, M. (2011). Structural Measures for Network Biology Using QuACN. BMC Bioinformatics, 12(1), 492.
  175. de Matos Simoes, R., & Emmert-Streib, F. (2011). Influence of Statistical Estimators of Mutual Information and Data Heterogeneity on the Inference of Gene Regulatory Networks. PLoS ONE, 6(12), e29279.
  176. Emmert-Streib, F. (2011). Parametric construction of episode networks from pseudoperiodic time series based on mutual information. PLoS ONE, 6(12), e27733.
  177. Altay, G., & Emmert-Streib, F. (2011). Structural Influence of gene networks on their inference: Analysis of C3NET. Biology Direct, 6, 31.
  178. Emmert-Streib, F., & Glazko, G. V. (2011a). Network Biology: A direct approach to study biological function. Wiley Interdiscip Rev Syst Biol Med, 3(4), 379–391.
  179. Emmert-Streib, F., & Dehmer, M. (2011). Networks for Systems Biology: Conceptual Connection of Data and Function. IET Systems Biology, 5(3), 185.
  180. Emmert-Streib, F., & Glazko, G. V. (2011b). Pathway analysis of expression data: deciphering functional building blocks of complex diseases. PLoS Computational Biology, 7(5), e1002053.
  181. Dehmer, M., Mowshowitz, A., & Emmert-Streib, F. (2011). Connections between Classical and Parametric Network Entropies. PLoS ONE, 6(1), e15733.
  182. Emmert-Streib, F., & Dehmer, M. (2010b). Influence of the Time Scale on the Construction of Financial Networks. PLoS ONE, 5(9), e12884.
  183. Altay, G., & Emmert-Streib, F. (2010a). Inferring the conservative causal core of gene regulatory networks. BMC Systems Biology, 4, 132.
  184. Emmert-Streib, F. (2010b). Statistic Complexity: Combining Kolmogorov Complexity with an Ensemble Approach. PLoS ONE, 5(8), e12256.
  185. Altay, G., & Emmert-Streib, F. (2010b). Revealing differences in gene network inference algorithms on the network-level by ensemble methods. Bioinformatics, 26(14), 1738–1744.
  186. Emmert-Streib, F., & Altay, G. (2010). Local network-based measures to assess the inferability of different regulatory networks. IET Systems Biology, 4(4), 277–288.
  187. Emmert-Streib, F., & Dehmer, M. (2010a). Identifying Critical Financial Networks of the DJIA: Towards a Network-based Index. Complexity, 16(1), 24–33.
  188. Dehmer, M., Emmert-Streib, F., Tsoy, Y. R., & Varmuza, K. (2010). Novel Information Measure for the Analysis of Chemical Graphs. Bulletin of the Tomsk Polytechnic University, 316(5).
  189. Emmert-Streib, F. (2010a). Exploratory analysis of spatiotemporal patterns of cellular automata by clustering compressibility. Physical Review E, 81(2), 026103.
  190. Glazko, G. V., & Emmert-Streib, F. (2009). Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets. Bioinformatics, 25(18), 2348–2354.
  191. Emmert-Streib, F., & Dehmer, M. (2009d). Predicting cell cycle regulated genes by causal interactions. Plos One, 4(8), e6633.
  192. Emmert-Streib, F., & Dehmer, M. (2009b). Hierarchical coordination of periodic genes in the cell cycle of \it Saccharomyces cerevisiae. BMC Systems Biology, 3, 76.
  193. Dehmer, M., Varmuza, K., Borgert, S., & Emmert-Streib, F. (2009). On Entropy-based Molecular Descriptors: Statistical Analysis of Real and Synthetic Chemical Structures. Journal of Chemical Information and Modeling, 49(7), 1655–1663.
  194. Emmert-Streib, F., & Dehmer, M. (2009c). Information Processing in the Transcriptional Regulatory Network of Yeast: Functional Robustness. BMC Systems Biology, 3, 35.
  195. Emmert-Streib, F., & Dehmer, M. (2009a). Fault Tolerance of Information Processing in Gene Networks. Physica A: Statistical Mechanics and Its Applications, 388(4), 541–548.
  196. Dehmer, M., & Emmert-Streib, F. (2008b). The Structural Information Content of Chemical Networks. Zeitschrift Für Naturforschung A, 63a, 155–158.
  197. Dehmer, M., Borgert, S., & Emmert-Streib, F. (2008). Entropy Bounds for Hierarchical Molecular Networks. PLoS ONE , 3 (8), e3079.
  198. Emmert-Streib, F., & Dehmer, M. (2008). Robustness in Scale-free Networks: Comparing Directed and Undirected Networks. International Journal of Modern Physics C , 19 (5), 717–726.
  199. Dehmer, M., & Emmert-Streib, F. (2008a). Structural Information Content of Networks: Graph Entropy based on Local Vertex Functionals. Computational Biology and Chemistry , 32 (2), 131–138.
  200. Dehmer, M., Emmert-Streib, F., & Gesell, T. (2008). A Comparative Analysis of Multidimensional Features of Objects Resembling Sets of Graphs. Applied Mathematics and Computation , 196 (1), 221–235.
  201. Emmert-Streib, F., & Dehmer, M. (2007a). Nonlinear Time Series Prediction based on a Power-Law Noise Model. International Journal of Modern Physics C , 18 (12), 1839–1852.
  202. Chen, L., Emmert-Streib, F., & Storey, J. D. (2007). Harnessing naturally randomized transcription to infer regulatory relationships among genes. Genome Biology , 8 (10), R219.
  203. Emmert-Streib, F., & Mushegian, A. (2007). A Topological Algorithm for Identification of Structural Domains of Proteins. BMC Bioinformatics , 8 , 237.
  204. Emmert-Streib, F. (2007). The Chronic Fatigue Syndrome: A Comparative Pathway Analysis. Journal of Computational Biology , 14 (7), 961–972.
  205. Dehmer, M., & Emmert-Streib, F. (2007a). Structural Similarity of Directed Universal Hierarchical Graphs: A low Computational Complexity Approach. Applied Mathematics and Computation , 194 (1).
  206. Emmert-Streib, F., & Dehmer, M. (2007b). Information Theoretic Measures of UHG Graphs with Low Computational Complexity. Applied Mathematics and Computation , 190 (2), 1783–1794.
  207. Dehmer, M., & Emmert-Streib, F. (2007b). Comparing Large Graphs Efficiently by Margines of Feature Vectors. Applied Mathematics and Computation , 188 (2), 1699–1710.
  208. Emmert-Streib, F., & Dehmer, M. (2007c). Topolocial Mappings between Graphs, Trees and Generalized Trees. Applied Mathematics and Computation , 186 (2), 1326–1333.
  209. Emmert-Streib, F. (2006a). A Heterosynaptic Learning Rule for Neural Networks. International Journal of Modern Physics C , 17 (10), 1501–1520.
  210. Dehmer, M., Emmert-Streib, F., & Wolkenhauer, O. (2006). Perspectives of Graph Mining Techniques. Rostocker Informatik Berichte , 30 (2), 47–56.
  211. Dehmer, M., Emmert-Streib, F., & Kilian, J. (2006). A Similarity Measure for Graphs with Low Computational Complexity. Applied Mathematics and Computation , 182 (1), 447–459.
  212. Emmert-Streib, F. (2006b). Algorithmic Computation of Knot Polynomials of Secondary Structure Elemtents of Proteins. Journal of Computational Biology , 13 (8), 1503–1512.
  213. Emmert-Streib, F. (2006c). Influence of the neural network topology on the learning dynamics. Neurocomputing , 69 (10–12), 1179–1182.
  214. Dehmer, M., Emmert-Streib, F., Mehler, A., & Kilian, J. (2006). Measuring the Structural Similarity of Web-based Documents: A novel Approach. International Journal of Computational Intelligence , 3 (1), 1–7.
  215. Emmert-Streib, F., Dehmer, M., Liu, J., & Mühlhäuser, M. (2006). Ranking Genes from DNA Microarray Data of Cervical Cancer by a local Tree Comparison. International Journal of Biomedical Sciences , 1 (1), 17–22.
  216. Emmert-Streib, F. (2005a). 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.
  217. Emmert-Streib, F. (2005c). Stochastic Sznajd Model in Open Community. International Journal of Modern Physics C , 16 (11), 1693–1700.
  218. Emmert-Streib, F. (2005b). Self-organized annealing in laterally inhibited neural networks shows power law decay. Neural Information Processing - Letters and Reviews , 7 (1), 29–38.
  219. Otterpohl, J. R., Emmert-Streib, F., & Pawelzik, K. (2001). A constrained HMM-based approach to the estimation of perceptual switching dynamics in Pigeons. Neurocomputing , 38–40 , 1495–1501.
  220. Otterpohl, J. R., Haynes, J. D., Emmert-Streib, F., Vetter, G., & Pawelzik, K. (2000). 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.
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