Computational Biology and Machine Learning Lab

Selected Publications

Books (Editor)
[2] Matthias Dehmer, Frank Emmert-Streib, Armin Graber, and Armindo Salvador, editors.
Applied Statistics for Network Biology: Methods for Systems Biology. Wiley-Blackwell, 2011.
[3] Frank Emmert-Streib and Matthias Dehmer, editors. Medical Biostatistics for Complex
Diseases. Wiley-Blackwell, 2010.
[4] Matthias Dehmer and Frank Emmert-Streib, editors. Analysis of Complex Networks:
From Biology to Linguistics. Wiley-VCH, 2009.
[5] Frank Emmert-Streib and Matthias Dehmer, editors. Information Theory and Statistical
Learning. Springer, 2008.
[6] Frank Emmert-Streib and Matthias Dehmer, editors. Analysis of Microarray Data: A
Network Based Approach. Wiley-VCH, 2008.

Journal Articles
[7] Frank Emmert-Streib. Detecting modules in the transcriptional regulatory network of
saccharomyces cerevisiae. 2010, in preparation.
[8] Frank Emmert-Streib, Lin Chen, and John D. Storey. Functional annotation of genes in
saccharomyces cerevisiae based on joint betweenness. 2010, in preparation.
[9] Gökmen Altay and Frank Emmert-Streib. Inferential characteristics of the gene network
inference algorithm C3NET: Influence of structural properties. 2010, submitted.
[10] Frank Emmert-Streib and Galina Glazko. Network biology: A direct approach to study
biological function. Wiley Interdiscip Rev Syst Biol Med, 2010, accepted.
[11] Frank Emmert-Streib, Galina Glazko, and Gökmen Altay. Statistical inference of gene
regulatory networks from expression data. 2010, submitted.
[12] Matthias Dehmer, Abbe Mowshowitz, and Frank Emmert-Streib. Special Interrelations
Between Classical and Parametric Network Entropies. 2010, submitted.
[13] Frank Emmert-Streib and Matthias Dehmer. Influence of the Time Scale on the Con-
struction of Financial Networks. PLoS ONE, 2010.
[14] Frank Emmert-Streib. Statistic Complexity: Combining Kolmogorov Complexity with an
Ensemble Approach. PLoS ONE, 5(8):e12256, 2010.
[15] Frank Emmert-Streib and Matthias Dehmer. Networks for Systems Biology: Concep-
tual Connection of Data and Function. IET Systems Biology, 2010, accepted.
[16] Gökmen Altay and Frank Emmert-Streib. Inferring the conservative causal core of gene
regulatory networks. BMC Systems Biology, 4:132, 2010.
[17] Gökmen Altay and Frank Emmert-Streib.
Revealing differences in gene network
inference algorithms on the network-level by ensemble methods.
Bioinformatics,
26(14):173844, 2010.
[18] Frank Emmert-Streib and Gökmen Altay. Local network-based measures to assess the
inferability of different regulatory networks. IET Systems Biology, 4(4):277288, 2010.
[19] Frank Emmert-Streib and Matthias Dehmer. Identifying Critical Financial Networks of
the DJIA: Towards a Network-based Index. Complexity, 16(1):2433, 2010.
[20] 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.
[21] Frank Emmert-Streib. Exploratory analysis of spatiotemporal patterns of cellular au-
tomata by clustering compressibility. Physical Review E, 81(2):026103, 2010.
[22] Galina Glazko and Frank Emmert-Streib. Unite and conquer: univariate and mul-
tivariate approaches for finding differentially expressed gene sets.
Bioinformatics,
25(18):234854, 2009.
[23] Frank Emmert-Streib and Matthias Dehmer. Predicting cell cycle regulated genes by
causal interactions. Plos One, 4(8):e6633, 2009.
[24] Frank Emmert-Streib and Matthias Dehmer.
Hierarchical coordination of periodic
genes in the cell cycle of saccharomyces cerevisiae. BMC Systems Biology, 3:76,
2009.
[25] Matthias Dehmer, Kurt Varmuza, Stephan Borgert, and Frank Emmert-Streib. On
entropy-based molecular descriptors: Statistical analysis of real and synthetic chemical
structures. Journal of Chemical Information and Modeling, 49(7):165563, 2009.
[26] Frank Emmert-Streib and Matthias Dehmer. Information processing in the transcrip-
tional regulatory network of yeast: Functional robustness. BMC Systems Biology, 3:35,
2009.
[27] Frank Emmert-Streib and Matthias Dehmer. Fault tolerance of information processing
in gene networks. Physica A: Statistical Mechanics and its Applications, 388(4):541
548, 2009.
[28] Matthias Dehmer and Frank Emmert-Streib.
The structural information content of
chemical networks. Zeitschrift für Naturforschung A, 63a:155158, 2008.
[29] Matthias Dehmer, Stephan Borgert, and Frank Emmert-Streib. Entropy bounds for
hierarchical molecular networks. PLoS ONE, 3(8):e3079, 2008.
[30] Frank Emmert-Streib and Matthias Dehmer. Robustness in scale-free networks: Com-
paring directed and undirected networks. International Journal of Modern Physics C,
19(5):717726, 2008.
[31] Matthias Dehmer and Frank Emmert-Streib. Structural information content of networks:
Graph entropy based on local vertex functionals. Computational Biology and Chem-
istry, 32(2):131138, 2008.
[32] Matthias Dehmer, Frank Emmert-Streib, and Tanja Gesell. A comparative analysis of
multidimensional features of objects resembling sets of graphs. Applied Mathematics
and Computation, 196(1):221235, 2008.
[33] Frank Emmert-Streib and Matthias Dehmer. Nonlinear time series prediction based on
a power-law noise model. International Journal of Modern Physics C, 18(12):1839
1852, 2007.
[34] Lin Chen, Frank Emmert-Streib, and John D. Storey. Harnessing naturally random-
ized transcription to infer regulatory relationships among genes. Genome Biology,
8(10):R219, 2007.
[35] Frank Emmert-Streib and Arcady Mushegian. A topological algorithm for identification
of structural domains of proteins. BMC Bioinformatics, 8:237, 2007.
[36] Frank Emmert-Streib. The chronic fatigue syndrome: A comparative pathway analysis.
Journal of Computational Biology, 14(7):961972, 2007.
[37] Matthias Dehmer and Frank Emmert-Streib. Structural similarity of directed universal
hierarchical graphs: A low computational complexity approach. Applied Mathematics
and Computation, 194(1), 2007.
[38] Frank Emmert-Streib and Matthias Dehmer. Information theoretic measures of UHG
graphs with low computational complexity. Applied Mathematics and Computation,
190(2):17831794, 2007.
[39] Matthias Dehmer and Frank 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.
[40] Frank Emmert-Streib and Matthias Dehmer. Topolocial mappings between graphs,
trees and generalized trees. Applied Mathematics and Computation, 186(2):1326
1333, 2007.
[41] Frank Emmert-Streib. A heterosynaptic learning rule for neural networks. International
Journal of Modern Physics C, 17(10):15011520, 2006.
[42] Matthias Dehmer, Frank Emmert-Streib, and Olaf Wolkenhauer. Perspectives of graph
mining techniques. Rostocker Informatik Berichte, 30(2):4756, 2006.
[43] Matthias Dehmer, Frank Emmert-Streib, and Jürgen Kilian. A similarity measure for
graphs with low computational complexity. Applied Mathematics and Computation,
182(1):447459, 2006.
[44] Frank Emmert-Streib. Algorithmic computation of knot polynomials of secondary struc-
ture elemtents of proteins. Journal of Computational Biology, 13(8):15031512, 2006.
[45] Frank Emmert-Streib. Influence of the neural network topology on the learning dynam-
ics. Neurocomputing, 69(10-12):11791182, 2006.
[46] Matthias Dehmer, Frank Emmert-Streib, Alexander Mehler, and Jürgen Kilian. Measur-
ing the structural similarity of web-based documents: A novel approach. International
Journal of Computational Intelligence, 3(1):17, 2006.
[47] Frank Emmert-Streib, Matthias Dehmer, Jing Liu, and Max Mühlhäuser. Ranking genes
from dna microarray data of cervical cancer by a local tree comparison. International
Journal of Biomedical Sciences, 1(1):1722, 2006.
[48] Frank Emmert-Streib. Active learning in recurrent neural networks facilitated by an
hebb-like learning rule with memory. Neural Information Processing - Letters and Re-
views, 9(2):3140, 2005.
[49] Frank Emmert-Streib. Stochastic Sznajd model in open community. International Jour-
nal of Modern Physics C, 16(11):16931700, 2005.
[50] Frank 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.
[51] Jens R. Otterpohl, Frank Emmert-Streib, and Klaus Pawelzik. A constrained HMM-
based approach to the estimation of perceptual switching dynamics in pigeons. Neu-
rocomputing, 38-40:14951501, 2001.
[52] Jens R. Otterpohl, John D. Haynes, Frank Emmert-Streib, G. Vetter, and Klaus
Pawelzik. Extracting the dynamics of perceptual switching from noisy behaviour: An
application of hidden markov modeling to pecking data from pigeons. Journal of Phys-
iology (Paris), 94(5-6):555567, 2000.

Book Chapters
[53] Frank Emmert-Streib. Large-scale statistical inference of gene regulatory networks:
Local network-based measures. In S. Niiranen and A. Ribeiro, editors, Information
Processing and Biological Systems. Springer, 2011, in press.
[54] Frank Emmert-Streib and Matthias Dehmer.
Learning systems biology: Concep-
tual considerations toward a web-based learning platform. In A. Mehler, editor, NN.
Springer, 2011, in press.
[55] Galina Glazko and Frank Emmert-Streib. Statistical analysis of differentially expressed
gene sets: current status and future directions. In A.Y. Yakovlev, L. Klebanov, and
D. Gaile, editors, Statistical Methods for Microarray Data Analysis. Humana Press,
2011, in press.
[56] Frank Emmert-Streib. A brief introduction to complex networks and their analysis. In
M. Dehmer, editor, Structural Analysis of Networks. Birkhäuser Publishing, 2010, in
press.
[57] Matthias Dehmer and Frank Emmert-Streib. Mining graph patterns in web-based sys-
tems: A conceptual view. In A. Mehler, S. Sharoff, and M. Santini, editors, Genres
on the web: Computational models and empirical studies, pages 237253. Springer,
2010.
[58] Matthias Dehmer, Frank Emmert-Streib, Yury R. Tsoy, and Kurt Varmuza. Quantifying
structural complexity of graphs: Information measures in mathematical chemistry. In
M.V. Putz, editor, Quantum Frontiers of Atoms and Molecules. Nova Science Pubish-
ing, 2010. in press.
[59] Frank Emmert-Streib and Matthias Dehmer. Detecting pathological pathways of a
complex disease by a comparative analysis of networks. In F. Emmert-Streib and
M. Dehmer, editors, Analysis of Microarray Data: A Network Based Approach, pages
285305. Wiley VCH, 2008.
[60] Frank Emmert-Streib. Learning behavior of neural networks with small-world topology:
A systems approach. In Gerald B. Kang, editor, Progress in Neurocomputing Research,
pages 141164. Nova Science Publishers, Inc., 2008.

Reviewed Proceeding Articles
[61] Frank Emmert-Streib and Matthias Dehmer. Towards a partitioning of the input space
of boolean networks: Variable selection using bagging. In Jie Zhou, editor, Complex
Sciences, volume 4 of Lecture Notes of the Institute for Computer Sciences, Social In-
formatics and Telecommunications Engineering, pages 715723. Springer Berlin Hei-
delberg, 2009.
[62] Matthias Dehmer and Frank Emmert-Streib. Towards network complexity. In Jie Zhou,
editor, Complex Sciences, volume 4 of Lecture Notes of the Institute for Computer
Sciences, Social Informatics and Telecommunications Engineering, pages 707714.
Springer Berlin Heidelberg, 2009.
[63] Frank Emmert-Streib and Matthias Dehmer. Organizational structure of the transcrip-
tional regulatory network of yeast: Periodic genes. In Jie Zhou, editor, Complex Sci-
ences, volume 4 of Lecture Notes of the Institute for Computer Sciences, Social Infor-
matics and Telecommunications Engineering, pages 140148. Springer Berlin Heidel-
berg, 2009.
[64] Frank Emmert-Streib and Matthias Dehmer. Towards a channel capacity of communi-
cation networks. In 2008 Complexity and Intelligence of the Artificial and Natural Com-
plex Systems, Medical Applications of the Complex Systems, Biomedical Computing.
IEEE Computer Society Press, 2009, in press.
[65] Matthias Dehmer, Stephan Borgert, and Frank Emmert-Streib. Network classes and
graph complexity measures. In 2008 Complexity and Intelligence of the Artificial and
Natural Complex Systems, Medical Applications of the Complex Systems, Biomedical
Computing. IEEE Computer Society Press, 2009, in press.
[66] Frank Emmert-Streib, Eearl F. Glynn, Christopher Seidel, Christoph L. Bausch, and
Arcady Mushegian. Detecting pathological pathways of the chronic fatigue syndrome
by the comparison of networks. In P. McConnell, S.M. Lin, and A.J. Cuticcia, editors,
Methods of Microarray Data Analysis VI. CreateSpace, 2009. Conference proceedings
of Critical Assessment of Microarray Data Analysis (CAMDA) held in 2006.
[67] Frank Emmert-Streib and Matthias Dehmer. Quantifying communication capabilities of
networks. In B. Iantovics, C. Enachescu, and F. Filip, editors, Complexity in artificial
and natural systems, pages 115119. Petru Maior University Press, 2008.
[68] Matthias Dehmer, Stephan Borgert, and Frank Emmert-Streib. Investigating network
classes by measuring their complexity. In B. Iantovics, C. Enachescu, and F. Filip,
editors, Complexity in artificial and natural systems, pages 100107. Petru Maior Uni-
versity Press, 2008.
[69] Frank Emmert-Streib and Andre S. Ribeiro. Optimize observational time points to max-
imize the inferability of gene networks. In H.A. Arabnia, editor, BIOCOMP'08 - The
2008 International Conference on Bioinformatics & Computational Biology, pages 55
60, 2008. Acceptance rate 27%.
[70] Frank Emmert-Streib and Matthias Dehmer. Global information processing in gene
networks: Fault tolerance. In Proceedings of the workshop on Computing and Com-
munications from Biological Systems: Theory and Applications (CCBS), pages 2027,
2007.
[71] Frank Emmert-Streib and Matthias Dehmer. Optimization procedure for predicting non-
linear time series based on a non-gaussian noise model. In MICAI 2007: Advances
in Artificial Intelligence, Lecture Notes in Computes Science (LNCS), pages 540549,
2007.
[72] Matthias Dehmer and Frank Emmert-Streib. A graph mining technique for automatic
classification of web genre data. In MLMTA'07 - The 2007 International Conference
on Machine Learning: Models, Technologies & Applications, pages 1016. CSREA
Press, 2007.
[73] Matthias Dehmer, Alexander Mehler, and Frank Emmert-Streib.
Graph-theoretical
characterizations of generalized trees. In MLMTA'07 - The 2007 International Con-
ference on Machine Learning: Models, Technologies & Applications, pages 113117.
CSREA Press, 2007.
[74] Frank Emmert-Streib and Mathhias Dehmer. A systems biology approach for the clas-
sification of dna microarray data. In Jarek Meller and Wieslaw Nowak, editors, Machine
Learning Approaches in Bioinformatics, pages 3549. Peter Lang, 2007.
[75] Frank Emmert-Streib, Matthias Dehmer, and Chris Seidel. Influence of prior informa-
tion on the reconstruction of the yeast cell cycle from microarray data. In H.A. Arabnia
and H. Valafar, editors, BIOCOMP'06 - The 2006 International Conference on Bioinfor-
matics & Computational Biology, pages 477482. CSREA Press, 2006.
[76] Frank Emmert-Streib and Matthias Dehmer. Theoretical bounds for the number of
inferable edges in sparse random networks. In H.A. Arabnia and H. Valafar, editors,
BIOCOMP'06 - The 2006 International Conference on Bioinformatics & Computational
Biology, pages 472476. CSREA Press, 2006.
[77] Earl F. Glynn, Frank Emmert-Streib, and Arcady Mushegian. An attempt to categorize
the servity of the chronic fatigue syndrome disease using affective disorder pathways.
In Critical Assessment of Microarray Data Analysis (CAMDA), 2006.
[78] Frank Emmert-Streib. A novel stochastic learning rule for neural networks. In Jun
Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, and Hujun Yin, editors, Advances
in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks,
Lecture Notes in Computer Science (LNCS), pages 414423. Springer, 2006.
[79] Frank Emmert-Streib and Matthias Dehmer. First studies of the influence of single gene
perturbations on the inference of genetic networks. In VIII. International Conference on
Enformatika, Systems Sciences and Engineering, Krakow, volume 10, pages 6569.
Enformatika, 2005.
[80] Frank Emmert-Streib, Matthias Dehmer, Gökhan H. Bakir, and M. Mühlhäuser. Influ-
ence of noise on the inference of dynamic bayesian networks from short time series.
In VIII. International Conference on Enformatika, Systems Sciences and Engineering,
volume 10, pages 7074. Enformatika, 2005.
[81] Matthias Dehmer, Frank Emmert-Streib, Jürgen Kilian, and Andreas Zulauf. Towards
clustering of web-based document structures. In VIII. International Conference on
Enformatika, Systems Sciences and Engineering, volume 10, pages 304309. Enfor-
matika, 2005.
[82] Frank Emmert-Streib, Matthias Dehmer, Jing Liu, and Max Mühlhäuser. A systems
approach to gene ranking from dna microarray data of cervical cancer. In VI. Inter-
national Conference on Enformatika, Systems Sciences and Engineering, volume 8,
pages 8287. Enformatika, 2005.
[83] Frank Emmert-Streib. Protein graph partitioning by mutually maximization of cycle
distributions. In VI. International Conference on Enformatika, Systems Sciences and
Engineering, volume 8, pages 8892. Enformatika, 2005.
[84] Matthias Dehmer, Frank Emmert-Streib, Alexander Mehler, Jürgen Kilian, and Max
Mühlhäuser. Application of a similarity measure for graphs to web-based document
structures. In VI. International Conference on Enformatika, Systems Sciences and
Engineering, volume 8, pages 7781. Enformatika, 2005.
[85] Frank Emmert-Streib. A neurobiologically motivated model for self-organized learning.
In Alexander Gelbukh and Hugo Terashima, editors, MICAI 2005: Advances in Artificial
Intelligence, Lecture Notes in Computes Science (LNCS), Lecture Notes in Artificial
Intelligence, volume 3789, pages 415424. Springer, acceptance rate: 28 %, 2005.
[86] Frank Emmert-Streib.
Self-reinforcement learning: A neurobiologically motivated
model. In Annual Conference on Artificial & Computational Intelligence for Decision,
Control and Automation in Engineering and Industrial Applications - International Con-
ference on Machine Intelligence (ACIDCA-ICMI), pages 335342, 2005.
[87] Frank Emmert-Streib, Matthias Dehmer, and Jürgen Kilian.
Classification of large
graphs by a local tree decomposition. In H.R. Arabnia and A. Scime, editors, Proceed-
ings of the 2005 International Conference on Data Mining (DMIN'05), pages 200207,
2005.
[88] J. Michael Herrmann, Frank Emmert-Streib, and Klaus Pawelzik. Autonomous robots
and neuroethology: Emergence of behavior from a sense of curiosity. In F. Mondada
U. Rüückert A. Löffler, editor, Experiments with the Mini-Robot Khepera, Proceedings
of the 1st Int. Khepera Workshop, volume 64, pages 8998. HNI-Verlagsschriftenreihe,
1999.

Books (Series Editor)
[89] Jarek Meller Wieslaw Nowak (Editors). Machine Learning Approaches in Bioinformat-
ics. Frank Emmert-Streib and Matthias Dehmer (Series Editors). Peter Lang, 2007.

Proceedings (Editor)
[90] Matthias Dehmer, Michael Drmota, and Frank Emmert-Streib, editors. Proceedings
of the 2008 International Conference on Information Theory and Statistical Learning
(ITSL'08). CSREA Press, 2008.
[91] Hamid Arabnia, Matthias Dehmer, Frank Emmert-Streib, and Mary Qu Yang, editors.
Proceedings of the 2007 International Conference on Machine Learning: Models, Tech-
nologies and Applications (MLMTA'07). CSREA Press, 2007.

Proceedings (Associate Editor)
[92] Hamid Arabnia, Mary Qu Yang, and Jack Y. Yang (Editors). Proceedings of the 2007
International Conference on Bioinformatics and Computational Biology (BIOCOMP'07).
Frank Emmert-Streib (Associate Editor). CSREA Press, 2007.
[93] Hamid Arabnia, Mary Qu Yang, and Jack Y. Yang (Editors). Proceedings of the 2007
International Conference on Artificial Intelligence (ICAI'07). Frank Emmert-Streib (As-
sociate Editor). CSREA Press, 2007.
[94] Hamid Arabnia, Mary Qu Yang, and Jack Y. Yang (Editors). Proceedings of the 2007
International Conference on Scientific Computing (CSC'07). Frank Emmert-Streib (As-
sociate Editor). CSREA Press, 2007.
[95] Hamid Arabnia, Mary Qu Yang, and Jack Y. Yang (Editors).
Proceedings of the
2007 International Conference on Genetic and Evolutionary Methods (GEM'07). Frank
Emmert-Streib (Associate Editor). CSREA Press, 2007.
[96] Hamid R. Arabnia and Homayoun Valafar (Editors). Proceedings of the 2006 Interna-
tional Conference on Bioinformatics and Computational Biology (BIOCOMP'06). Frank
Emmert-Streib (Associate Editor). CSREA Press, 2006.

Unreviewed Manuscripts
[97] Frank Emmert-Streib and Matthias Dehmer. Preface. In Frank Emmert-Streib and
Matthias Dehmer, editors, Medical Biostatistics for Complex Diseases. Wiley-Blackwell,
2010.
[98] Matthias Dehmer and Frank Emmert-Streib. Preface. In Matthias Dehmer and Frank
Emmert-Streib, editors, Analysis of Complex Networks: From Biology to Linguistics.
Wiley-VCH, 2009.
[99] Frank Emmert-Streib and Matthias Dehmer. Preface. In Frank Emmert-Streib and
Matthias Dehmer, editors, Information Theory and Statistical Learning. Springer, 2008.
[100] Frank Emmert-Streib, Hamid R. Arabnia, and Mary Qu Yang. Preface. Applied Artificial
Intelligence, 22(7-8):617618, 2008. Special Issue with the title: 'Machine Learning:
Models and Applications'.
[101] Frank Emmert-Streib and Matthias Dehmer. Preface. In Frank Emmert-Streib and
Matthias Dehmer, editors, Analysis of Microarray Data: A Network-based approach.
Wiley-VCH, 2008.