Comparison of tissue disease specific integrated networks using directed graphlet signatures

Can, Tolga
We present a novel framework for counting small sub-graph patterns in integrated genome-scale networks. An integrated network was built using the physical, regulatory, and metabolic interactions between H. sapiens proteins from the Pathway Commons database. The network was filtered for tissue/disease specific proteins by using a large-scale human transcriptional profiling study, resulting in several tissue and disease specific sub-networks. In this study, we apply and extend the idea of graphlet counting in undirected protein-protein interaction (PPI) networks to directed multi-labeled networks and represent each network as a vector of graphlet counts. Graphlet counts are assessed for statistical significance by comparison against a set of randomized networks. We present our results on analysis of differential graphlets between different conditions. Our results show that graphlets can be used for identification of systems level differences between disease states.


Comparison of tissue/disease specific integrated networks using directed graphlet signatures
Sönmez, Arzu Burçak; Can, Tolga (2017-03-22)
Background: Analysis of integrated genome-scale networks is a challenging problem due to heterogeneity of high-throughput data. There are several topological measures, such as graphlet counts, for characterization of biological networks.
Comparison of two inference approaches in Gaussian graphical models
Purutçuoğlu Gazi, Vilda; Wit, Ernst (Walter de Gruyter GmbH, 2017-04-01)
Introduction: The Gaussian Graphical Model (GGM) is one of the well-known probabilistic models which is based on the conditional independency of nodes in the biological system. Here, we compare the estimates of the GGM parameters by the graphical lasso (glasso) method and the threshold gradient descent (TGD) algorithm.
Analysis and network representation of hotspots in protein interfaces using minimum cut trees
Tunçbağ, Nurcan; Keskin, Ozlem; GÜRSOY, Attila (2010-08-01)
We propose a novel approach to analyze and visualize residue contact networks of protein interfaces by graph-based algorithms using a minimum cut tree (mincut tree). Edges in the network are weighted according to an energy function derived from knowledge-based potentials. The mincut tree, which is constructed from the weighted residue network, simplifies and summarizes the complex structure of the contact network by an efficient and informative representation. This representation offers a comprehensible vie...
A Formal Methods Approach to Pattern Recognition and Synthesis in Reaction Diffusion Networks
Bartocci, Ezio; Aydın Göl, Ebru; Haghighi, Iman; Belta, Calin (2018-03-01)
We introduce a formal framework for specifying, detecting, and generating spatial patterns in reaction diffusion networks. Our approach is based on a novel spatial superposition logic, whose semantics is defined over the quad-tree representation of a partitioned image. We demonstrate how to use rule-based classifiers to efficiently learn spatial superposition logic formulas for several types of patterns from positive and negative examples. We implement pattern detection as a model-checking algorithm and we ...
Representing temporal knowledge in connectionist expert systems
Alpaslan, Ferda Nur (1996-09-27)
This paper introduces a new temporal neural networks model which can be used in connectionist expert systems. Also, a Variation of backpropagation algorithm, called the temporal feedforward backpropagation algorithm is introduced as a method for training the neural network. The algorithm was tested using training examples extracted from a medical expert system. A series of experiments were carried out using the temporal model and the temporal backpropagation algorithm. The experiments indicated that the alg...
Citation Formats
A. B. SÖNMEZ and T. Can, “Comparison of tissue disease specific integrated networks using directed graphlet signatures,” 2016, Accessed: 00, 2020. [Online]. Available: