**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**8

# Publications

##### 8 First Studies of the Influence of Single Gene Perturbations on the Inference of Genetic Networks

**Authors:**
Frank Emmert-Streib,
Matthias Dehmer

**Abstract:**

**Keywords:**
Dynamic Bayesian networks,
microarray data,
structure learning,
Markov chain Monte Carlo.

##### 7 Towards Clustering of Web-based Document Structures

**Authors:**
Matthias Dehmer,
Frank Emmert Streib,
Jürgen Kilian,
Andreas Zulauf

**Abstract:**

**Keywords:**
Clustering methods,
graph-based patterns,
graph similarity,
hypertext structures,
web structure mining

##### 6 Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series

**Authors:**
Frank Emmert Streib,
Matthias Dehmer,
Gökhan H. Bakır,
Max Mühlhauser

**Abstract:**

**Keywords:**
Dynamic Bayesian networks,
structure learning,
gene networks,
Markov chain Monte Carlo,
microarray data.

##### 5 Measuring the Structural Similarity of Web-based Documents: A Novel Approach

**Authors:**
Matthias Dehmer,
Frank Emmert Streib,
Alexander Mehler,
Jürgen Kilian

**Abstract:**

Most known methods for measuring the structural similarity of document structures are based on, e.g., tag measures, path metrics and tree measures in terms of their DOM-Trees. Other methods measures the similarity in the framework of the well known vector space model. In contrast to these we present a new approach to measuring the structural similarity of web-based documents represented by so called generalized trees which are more general than DOM-Trees which represent only directed rooted trees.We will design a new similarity measure for graphs representing web-based hypertext structures. Our similarity measure is mainly based on a novel representation of a graph as strings of linear integers, whose components represent structural properties of the graph. The similarity of two graphs is then defined as the optimal alignment of the underlying property strings. In this paper we apply the well known technique of sequence alignments to solve a novel and challenging problem: Measuring the structural similarity of generalized trees. More precisely, we first transform our graphs considered as high dimensional objects in linear structures. Then we derive similarity values from the alignments of the property strings in order to measure the structural similarity of generalized trees. Hence, we transform a graph similarity problem to a string similarity problem. We demonstrate that our similarity measure captures important structural information by applying it to two different test sets consisting of graphs representing web-based documents.

**Keywords:**
Graph similarity,
hierarchical and directed graphs,
hypertext,
generalized trees,
web structure mining.

##### 4 Protein Graph Partitioning by Mutually Maximization of cycle-distributions

**Authors:**
Frank Emmert Streib

**Abstract:**

**Keywords:**
Graph partitioning,
unweighted graph,
protein domains.

##### 3 Application of a Similarity Measure for Graphs to Web-based Document Structures

**Authors:**
Matthias Dehmer,
Frank Emmert Streib,
Alexander Mehler,
Jürgen Kilian,
Max Mühlhauser

**Abstract:**

**Keywords:**
Graph similarity,
hierarchical and directed graphs,
hypertext,
generalized trees,
web structure mining.

##### 2 A Systems Approach to Gene Ranking from DNA Microarray Data of Cervical Cancer

**Authors:**
Frank Emmert Streib,
Matthias Dehmer,
Jing Liu,
Max Mühlhauser

**Abstract:**

**Keywords:**
Graph similarity,
DNA microarray data,
cancer.

##### 1 Ranking Genes from DNA Microarray Data of Cervical Cancer by a local Tree Comparison

**Authors:**
Frank Emmert-Streib,
Matthias Dehmer,
Jing Liu,
Max Muhlhauser

**Abstract:**

The major objective of this paper is to introduce a new method to select genes from DNA microarray data. As criterion to select genes we suggest to measure the local changes in the correlation graph of each gene and to select those genes whose local changes are largest. More precisely, we calculate the correlation networks from DNA microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to tumor progression. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth. This indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.

**Keywords:**
Graph similarity,
generalized trees,
graph alignment,
DNA microarray data,
cervical cancer.