Charalampos N. Moschopoulos

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system.

Applying evolutionary computation to the school timetabling problem: The Greek case

In this contribution, an adaptive algorithm based on evolutionary computation techniques is designed, developed and applied to the timetabling problem of educational organizations. Specifically, the proposed algorithm has been used in order to create feasible and efficient timetables for high schools in Greece. The algorithm has been tested exhaustively with real-world input data coming from many different high schools and has been compared with several other effective techniques in order to demonstrate its efficiency and superior performance. Simulation results showed that the algorithm is able to construct a feasible and very efficient timetable more quickly and easily compared to other techniques, thus preventing disagreements and arguments among teachers and assisting each school to operate with its full resources from the beginning of the academic year. Except from that, due to its inherent adaptive behavior it can be used each time satisfying different specific constraints, in order to lead to timetables, thus meeting the different needs that each school may have.

A genetic algorithm approach to school timetabling

An adaptive algorithm based on computational intelligence techniques is designed, developed and applied to the timetabling problem of educational organizations. The proposed genetic algorithm is used in order to create feasible and efficient timetables for high schools in Greece. In order to demonstrate the efficiency of the proposed genetic algorithm, exhaustive experiments with real-world input data coming from many different high schools in the city of Patras have been conducted. As well as that, in order to demonstrate the superior performance of the proposed algorithm, we compare its experimental results with the results obtained by another effective algorithm applied to the same problem. Simulation results showed that the proposed algorithm outperforms other existing attempts. However, the most significant contribution of the paper is that the proposed algorithm allows for criteria adaptation, thus producing different timetables for different constraints priorities. So, the proposed approach, due to its inherent adaptive capabilities, can be used, each time satisfying different specific constraints, in order to lead to different timetables, thus meeting the different needs that each school may have.

Which clustering algorithm is better for predicting protein complexes

BackgroundProtein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.ResultsIn this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.ConclusionsWhile results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm

jClust: a clustering and visualization toolbox

jClust is a user-friendly application which provides access to a set of widely used clustering and clique finding algorithms. The toolbox allows a range of filtering procedures to be applied and is combined with an advanced implementation of the Medusa interactive visualization module. These implemented algorithms are k-Means, Affinity propagation, Bron–Kerbosch, MULIC, Restricted neighborhood search cluster algorithm, Markov clustering and Spectral clustering, while the supported filtering procedures are haircut, outside–inside, best neighbors and density control operations. The combination of a simple input file format, a set of clustering and filtering algorithms linked together with the visualization tool provides a powerful tool for data analysis and information extraction. Availability: http://jclust.embl.de/ Contact: pavlopou@embl.de; rschneid@embl.de; skossida@bioacademy.gr Supplementary information: Supplementary data are available at Bioinformatics online.

Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme

BackgroundRecent technological advances applied to biology such as yeast-two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of protein interaction networks. These interaction networks represent a rich, yet noisy, source of data that could be used to extract meaningful information, such as protein complexes. Several interaction network weighting schemes have been proposed so far in the literature in order to eliminate the noise inherent in interactome data. In this paper, we propose a novel weighting scheme and apply it to the S. cerevisiae interactome. Complex prediction rates are improved by up to 39%, depending on the clustering algorithm applied.ResultsWe adopt a two step procedure. During the first step, by applying both novel and well established protein-protein interaction (PPI) weighting methods, weights are introduced to the original interactome graph based on the confidence level that a given interaction is a true-positive one. The second step applies clustering using established algorithms in the field of graph theory, as well as two variations of Spectral clustering. The clustered interactome networks are also cross-validated against the confirmed protein complexes present in the MIPS database.ConclusionsThe results of our experimental work demonstrate that interactome graph weighting methods clearly improve the clustering results of several clustering algorithms. Moreover, our proposed weighting scheme outperforms other approaches of PPI graph weighting.

An enhanced Markov clustering method for detecting protein complexes

With the recent high-throughput methods, large datasets of experimentally detected pairwise protein-protein interactions are generated. However, these data suffer from noise, reducing the quality of the information they bring (identification of protein complexes). This paper introduces a novel methodology for detecting protein complexes in a protein-protein interaction graph. Our method initially uses the Markov clustering algorithm and then filters the derived results in order to obtain the best set of clusters that represent protein complexes. The efficiency of our method is shown in experimental results derived from 7 different yeast protein interaction datasets. Moreover, comparisons with 4 other algorithms are performed proving that our method predicts known protein complexes, recorded in the MIPS database, more accurately.

Analyzing Protein-Protein Interaction Networks with Web Tools

Protein-protein interactions (PPIs) have been marked as the main actors for all of the processes taking place in a cell and therefore great efforts have been made towards the understanding of their biological function. Today, new high- throughput technologies generate vast amounts of interaction data even with a limited number of experiments. The analysis of these data can lead to valuable conclusions about the cell organization such as protein complex detection, characterization of protein function, identification of protein pathways etc. Various techniques have been applied to analyze PPI data based on different strategies. Web interfaces that have been developed to host these methods consist of valuable tools which are often available to all users. In this review, we describe a collection of such web tools to analyze PPI data and more that are applicable to a wider range of problems. The functionality of each web tool is described as well as their compatibility with other resources. An overview of the technologies that are supported by such tools for their activities is also provided.

GAPPI: IDENTIFYING IMPORTANT PROTEIN MODULES THROUGH PROTEIN-PROTEIN INTERACTION GRAPHS

In this paper a new Genetic Algorithm is proposed, called GAppi, which performs clustering in protein-protein interaction networks to identify protein complexes. The algorithm has been tested exhau...

Dealing with Large Datasets Using an Artificial Intelligence Clustering Tool

Nowadays, clustering on very large datasets is a very common task. In many scientific and research areas such as bioinformatics and/or economics, clustering on very big datasets has to be performed by people that are not familiar with computerized methods. In this contribution, an artificial intelligence clustering tool is presented which is user friendly and includes various powerful clustering algorithms that are able to cope with very large datasets that vary in nature. Moreover, the tool, presented in this contribution, allows the combination of various artificial intelligence algorithms in order to achieve better results. Experimental results show that the proposed artificial intelligence clustering tool is very flexible and has significant computational power, a fact that makes it suitable for clustering applications of very large datasets.