Frank Emmert-Streib

Harnessing naturally randomized transcription to infer regulatory relationships among genes

We develop an approach utilizing randomized genotypes to rigorously infer causal regulatory relationships among genes at the transcriptional level, based on experiments in which genotyping and expression profiling are performed. This approach can be used to build transcriptional regulatory networks and to identify putative regulators of genes. We apply the method to an experiment in yeast, in which genes known to be in the same processes and functions are recovered in the resulting transcriptional regulatory network.

Analysis of Complex Networks

The best way for understanding how things work is by understanding their structures [1]. Complex networks are not an exception [2]. In order to understand why some networks are more robust than others, or why the propagation of a disease in faster in one network than in another is necessary to understand how these networks are organized [3-5]. A complex network is a simplified representation of a complex system in which the entities of the system are represented by the nodes in the network and the interrelations between entities are represented by means of the links joining pairs of nodes [3-5]. In analyzing the architecture of a complex network we are concerned only with the topological organization of these nodes and links. That is to say, we are not taking care of any geometric characteristic of the systems we are representing by these networks but only on how the parts are organized or distributed to form the whole system.

Brief Communication: Structural information content of networks: Graph entropy based on local vertex functionals

In this paper we define the structural information content of graphs as their corresponding graph entropy. This definition is based on local vertex functionals obtained by calculating j-spheres via the algorithm of Dijkstra. We prove that the graph entropy and, hence, the local vertex functionals can be computed with polynomial time complexity enabling the application of our measure for large graphs. In this paper we present numerical results for the graph entropy of chemical graphs and discuss resulting properties.

Applied statistics for network biology : methods in systems biology

MODELING, SIMULATION AND MEANING OF GENE NETWORKS Network Analysis to Interpret Complex Phenotypes (Hong Yu, Jialiang Huang, Wei Zhang, and Jing-Dong J. Han) Stochastic Modelling of Regulatory Networks (Tianhai Tian) Modeling eQTL in Multiple Populations (Ching-Lin Hsiao and Cathy S.J. Fann) INFERENCE OF GENE NETWORKS Transcriptional Network Inference based on Information Theory (Patrick E. Meyer and Gianluca Bontempi) Elucidation of General and Condition-dependent Gene Pathways Using Mixture Models and Bayesian Networks (Sandra Rodriguez-Zas and Younhee Ko) Multi-scale Networks Reconstruction from Gene-expression Measurements: Correlations, Perturbations and a-priori Biological Knowledge (Daniel Remondini and Gastone Castellani) Gene Regulatory Network Inference: Combining a Genetic Programming and Hendless Filtering Approach (Lijun Qian, Haixin Wang, and Xiangfang Li) Computational Reconstruction of Protein Interaction Networks (Konrad Monks, Irmgard Muhlberger, Andreas Bernthaler, Raul Fechete, Paul Perco, Rudolf Freund, Arno Lukas, and Bernd Mayer) ANALYSIS OF GENE NETWORKS What if the Fit is Unfit? Criteria for Biological Systems Estimation Beyond Residual Errors (Eberhard O. Voit) Machine Learning Methods for Identifying Essential Genes and Proteins in Networks (Kitiporn Plaimas and Rainer Konig) Gene Co-expression Networks for the Analysis of DNA Microarray Data (Matthew Weirauch) Correlation Network Analysis and Knowledge Integration (Thomas N. Plasterer, Robert Stanley, and Erich Gombocz) Network Screening: A New Method to Identify Active Networks from an Ensemble of Known Networks (Shigeru Saito and Katsuhisa Horimoto) Community Detection in Biological Networks (Gautam S. Thakur) On Some Inverse Problems in Generating Probabilistic Boolean Networks (Xi Chen, Wai-Ki Ching and Nam-Kiu Tsing) Boolean analysis of gene-expression datasets (Debashis Sahoo) SYSTEMS APPROACH TO DISEASES Representing Cancer Cell Trajectories in a Phase-space Diagram: Switching Cellular States by Biological Phase Transitions (Mariano Bizzarri and Alessandro Giuliani) Protein Network Analysis for Disease Gene Identification and Prioritization (Jing Chen and Anil G. Jegga) Pathways and Networks as Functional Descriptors for Human Disease and Drug Response Endpoints (Y. Nikolsky and B. Bessarabova and E. Kirillov and Z. Dezso and W. Nikolskaya)

Analysis of Microarray Data

We give a brief overview over necessary steps in the analysis of microarray data. We cover quality control, preprocessing, statistical as well as enrichment analysis.

A topological algorithm for identification of structural domains of proteins

BackgroundIdentification of the structural domains of proteins is important for our understanding of the organizational principles and mechanisms of protein folding, and for insights into protein function and evolution. Algorithmic methods of dissecting protein of known structure into domains developed so far are based on an examination of multiple geometrical, physical and topological features. Successful as many of these approaches are, they employ a lot of heuristics, and it is not clear whether they illuminate any deep underlying principles of protein domain organization. Other well-performing domain dissection methods rely on comparative sequence analysis. These methods are applicable to sequences with known and unknown structure alike, and their success highlights a fundamental principle of protein modularity, but this does not directly improve our understanding of protein spatial structure.ResultsWe present a novel graph-theoretical algorithm for the identification of domains in proteins with known three-dimensional structure. We represent the protein structure as an undirected, unweighted and unlabeled graph whose nodes correspond to the secondary structure elements and edges represent physical proximity of at least one pair of alpha carbon atoms from two elements. Domains are identified as constrained partitions of the graph, corresponding to sets of vertices obtained by the maximization of the cycle distributions found in the graph. When a partition is found, the algorithm is iteratively applied to each of the resulting subgraphs. The decision to accept or reject a tentative cut position is based on a specific classifier. The algorithm is applied iteratively to each of the resulting subgraphs and terminates automatically if partitions are no longer accepted. The distribution of cycles is the only type of information on which the decision about protein dissection is based. Despite the barebone simplicity of the approach, our algorithm approaches the best heuristic algorithms in accuracy.ConclusionOur graph-theoretical algorithm uses only topological information present in the protein structure itself to find the domains and does not rely on any geometrical or physical information about protein molecule. Perhaps unexpectedly, these drastic constraints on resources, which result in a seemingly approximate description of protein structures and leave only a handful of parameters available for analysis, do not lead to any significant deterioration of algorithm accuracy. It appears that protein structures can be rigorously treated as topological rather than geometrical objects and that the majority of information about protein domains can be inferred from the coarse-grained measure of pairwise proximity between elements of secondary structure elements.

Advances in network complexity

Functional Complexity Based on Topology (Hildegard Meyer-Ortmanns) Connections between Artificial Intelligence, Computational Complexity and the Complexity of Graphs (Angel Garrido) Selection Based Estimates of Complexity Unravel Some Mechanisms and Selective Pressures Underlying the Evolution of Complexity in Artificial Networks (Herve Le Nagard, Olivier Tenaillon) Three Types of Network Complexity Pyramid (Fang Jin-Qing, Li Yong, Liu Qiang) Computational Complexity of Graphs (Stasys Jukna) The Linear Complexity of a Graph (David L. Neel, Michael E. Orrison) Kirchhoffs Matrix Tree Theorem revisited: Counting Spanning Trees with the Quantum Relative Entropy (Vittorio Giovannetti, Simone Severini) Dimension Measure for Complex Networks (O. Shanker) Information Based Complexity of Networks (Russell K. Standish) Thermodynamic Depth in Undirected and Directed Networks (Francisco Escolano, Edwin R. Hancock) Circumscribed Complexity in Ecological Networks (Robert E. Ulanowicz) Metros as Biological Systems: Complexity in Small Real-life Networks (Sybil Derrible)

Towards an Information Theory of Complex Networks

For over a decade, complex networks have steadily grown as an important tool across abroad array of academic disciplines, with applications ranging from physics to social media.A tightly organized collectionofcarefully-selected papers on the subject, Towards an Information Theory of Complex Networks: Statistical Methods and Applications presents theoretical and practical results about information-theoretic and statistical models of complex networks in the natural sciences and humanities.The booksmajor goal is to advocate and promote a combination of graph-theoretic, information-theoretic, and statistical methods as a way to better understand and characterize real-world networks. This volume is the first to present a self-contained, comprehensive overview of information-theoretic modelsof complex networks with an emphasis on applications. As such, itmarks a first step toward establishing advanced statistical information theory as a unified theoretical basis of complex networksfor allscientific disciplines andcan serve asa valuable resource foradiverse audience of advanced students and professional scientists.While it is primarilyintendedas a reference for research,the bookcould also be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others.

A comparative analysis of multidimensional features of objects resembling sets of graphs

In the present paper, we introduce a notion of a style representing abstract, complex objects having characteristics that can be represented as structured objects. Furthermore, we provide some mathematical properties of such styles. As a main result, we present a novel approach to perform a meaningful comparative analysis of such styles by defining and using graph-theoretic measures. We compare two styles by comparing the underlying feature sets representing sets of graph structurally. To determine the structural similarity between the underlying graphs, we use graph similarity measures that are computationally efficient. More precisely, in order to compare styles, we map each feature set to a so-called median graph and compare the resulting median graphs. As an application, we perform an experimental study to compare special styles representing sets of undirected graphs and present numerical results thereof.

Statistical Diagnostics for Cancer: Analyzing high-dimensional data

Control of Type I Error Rates for Oncology Biomarker Discovery with High-throughput Platforms (Jeffrey Miecznikowski, Dan Wang, Song Liu) Discovery of Expression Signatures in Chronic Myeloid Leukemia by Bayesian Model Averaging (Ka Yee Yeung) Bayesian Ranking and Selection Methods in Microarray Studies (Hisashi Noma, Shigeyuki Matsui) Multi-class Classification via Bayesian Variable Selection with Gene Expression Data (Yang Aijun, Song Xinyuan, Li Yunxian) Colorectal Cancer and its Molecular Subsystems: Construction, Interpretation, and Validation (Vishal N. Patel, Mark R. Chance) Semi-Supervised Methods for Analyzing High-Dimensional Genomic Data (Devin C. Koestler) Network Medicine: Disease Genes in Molecular Networks (Sreenivas Chavali, Kartiek Kanduri) Inference of Gene Regulatory Networks in Breast and Ovarian Cancer by Integrating Different Genomic Data (Binhua Tang, Fei Gu, Victor X. Jin) Network Module Based Approaches in Cancer Data Analysis (Guanming Wu, Lincoln D. Stein) Discriminant and Network Analysis to Study Origin Of Cancer (Yue Wang, Li Chen, Ye Tian, Guoqiang Yu, David J. Miller, Ie-Ming Shih) Intervention and Control of Gene Regulatory Net-Works: Theoretical Framework and Application to Human Melanoma Gene Regulation (Nidhal Bouaynaya, Roman Shterenberg, Dan Schonfeld, Hassan M. Fathallah-Shaykh) Identification of Recurrent DNA Copy Number Aberrations in Tumors (Vonn Walter, Andrew B. Nobel, D. Neil Hayes, Fred A. Wright) The Cancer Cell, its Entropy, and High-Dimensional Molecular Data (Wessel N. van Wieringen, Aad W. van der Vaart) Overview of Public Cancer Databases, Resources and Visualization Tools (Frank Emmert-Streib, Ricardo de Matos Simoes, Shailesh Tripathi, Matthias Dehmer)