Integrative analysis of large-scale biological data sets

We present two novel web-applications for microarray and gene/protein set analysis, ArrayMining.net and TopoGSA. These bioinformatics tools use integrative analysis methods, including ensemble and consensus machine learning techniques, as well as modular combinations of different analysis types, to extract new biological insights from experimental transcriptomics and proteomics data. They enable researchers to combine related algorithms and datasets to increase the robustness and accuracy of statistical analyses and exploit synergies of different computational methods, ranging from statistical learning to optimization and topological network analysis

PathExpand: Extending biological pathways using molecular interaction networks

We present a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners. The added proteins display distinctive network topological features and molecular function annotations, and can be proposed as putative new components, and/or as regulators of the communication between the different cellular processes. Finally, these extended pathways and processes are used to analyze their enrichment in cancer mutated genes. Significant associations between mutated genes and certain processes are identified, enabling an analysis of the influence of previously non-annotated cancer mutated genes

vrmlgen: An R Package for 3D Data Visualization on the Web

The 3-dimensional representation and inspection of complex data is a frequently used strategy in many data analysis domains. Existing data mining software often lacks functionality that would enable users to explore 3D data interactively, especially if one wishes to make dynamic graphical representations directly viewable on the web. In this paper we present vrmlgen, a software package for the statistical programming language R to create 3D data visualizations in web formats like the Virtual Reality Markup Language (VRML) and LiveGraphics3D. vrmlgen can be used to generate 3D charts and bar plots, scatter plots with density estimation contour surfaces, and visualizations of height maps, 3D object models and parametric functions. For greater flexibility, the user can also access low-level plotting methods through a unified interface and freely group different function calls together to create new higher-level plotting methods. Additionally, we present a web tool allowing users to visualize 3D data online and test some of vrmlgen's features without the need to install any software on their computer.

A tutorial for competent memetic algorithms: Model, taxonomy and design issues

The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. Additionally, MAs are inspired by Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement (Dawkins, 1976). In the case of MA's, "memes" refer to the strategies (e.g., local refinement, perturbation, or constructive methods, etc.) that are employed to improve individuals. In this paper, we review some works on the application of MAs to well-known combinatorial optimization problems, and place them in a framework defined by a general syntactic model. This model provides us with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research. Also, by having an abstract model for this class of metaheuristics, it is possible to explore their design space and better understand their behavior from a theoretical standpoint. We illustrate the theoretical and practical relevance of this model and taxonomy for MAs in the context of a discussion of important design issues that must be addressed to produce effective and efficient MAs

BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning

This technical report briefly describes our recent work in the iterative rule learning approach (IRL) of evolutionary learning/genetics-based machine learning. This approach was initiated by the SIA system. A more recent example is HIDER. Our approach integrates some of the main characteristics of GAssist, a system belonging to the Pittsburgh approach of Evolutionary Learning, into the general framework of IRL. Our aims in developing this system are use all the good characteristics of GAssist but at the same time overcome some of the scalability limitations that it presents

An Approach to the Engineering of Cellular Models Based on P Systems

Living cells assembled into colonies or tissues communicate using complex systems. These systems consist in the interaction between many molecular species distributed over many compartments. Among the different cellular processes used by cells to monitor their environment and respond accordingly, gene regulatory networks, rather than individual genes, are responsible for the information processing and orchestration of the appropriate response [16]. In this respect, synthetic biology has emerged recently as a novel discipline aiming at unravelling the design principles in gene regulatory systems by synthetically engineering transcriptional networks which perform a specific and prefixed task [2]. Formal modelling and analysis are key methodologies used in the field to engineer, assess and compare different genetic designs or devices. In order to model cellular systems in colonies or tissues one requires a formalism able to represent the following relevant features: – Single cells should be described as the elementary units in the system. Nevertheless, they cannot be represented as homogeneous points as they exhibit complex structures containing different compartments where specific molecular species interact according to particular reactions. – The molecular interactions taking place in cellular systems are inherently discrete and stochastic processes. This is a key feature of cellular systems that needs to be taken into account when describing their dynamics [9]. – It has been postulated that gene regulatory networks are organised in a modular manner in such a way that cellular processes arise from the orchestrated interactions between different genetic transcriptional units that can be considered separable modules [1]. – Spatial and geometric information must be represented in the system in order to describe processes involving pattern formation. In this work we review recent advances in the use of the computational paradigm membrane computing or P systems as a formal methodology in synthetic biology for the specification and analysis on cellular system models according to the previously presented points

Towards the Design of Heuristics by Means of Self-Assembly

The current investigations on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for the problem at hand. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem at hand. Some approaches like genetic programming have been proposed for this. In this paper, we explore an elegant nature-inspired alternative based on self-assembly construction processes, in which structures emerge out of local interactions between autonomous components. This idea arises from previous works in which computational models of self-assembly were subject to evolutionary design in order to perform the automatic construction of user-defined structures. Then, the aim of this paper is to present a novel methodology for the automated design of heuristics by means of self-assembly