One of the most important, but often ignored, parts of any clustering and classification algorithm is the computation of the similarity matrix. This is especially important when integrating high throughput biological data sources because of the high noise rates and the many missing values. In this paper we present a new method to compute such similarities for the task of classifying pairs of proteins as interacting or not. Our method uses direct and indirect information about interaction pairs to constructs a random forest (a collection of decision tress) from a training set. The resulting forest is used to determine the similarity between protein pairs and this similarity is used by a classification algorithm (a modified kNN) to classify protein pairs. Testing the algorithm on yeast data indicates that it is able to improve coverage to 20 % of interacting pairs with a false positive rate of 50%. These results compare favorably with all previously suggested methods for this task indicating the importance of robust similarity estimates. 1

In this paper, we present a survey of the use of graph theoretical techniques in Biology. In particular, we discuss recent work on identifying and modelling the structure of bio-molecular networks, as well as the application of centrality measures to interaction networks and research on the hierarchical structure of such networks and network motifs. Work on the link between structural network properties and dynamics is also described, with emphasis on synchronization and disease propagation. 1

This report summarizes ten levels of abstraction that together span the continuum from the most elementary to the most general levels when modeling biological systems. It is shown how the neocybernetic principles can be seen as the key to reaching a holistic view of complex processes in general. Preface Concrete examples help to understand complex systems. In this report, the key point is to illustrate the basic mechanisms and properties of neocybernetic system models. Good visualizations are certainly needed. It is biological systems, or living systems, that are perhaps the most characteristic examples of cybernetic systems. This intuition is extended here to natural systems in general — indeed, it is all other than man-made ones that seem to be cybernetic. The word “biological ” in the title should be interpreted as “bio-logical ” — referring to general studies of any living systems, independent of the phenosphere. Starting from the concrete examples, connections to more abstract systems are found, and the discussions become more and more all-embracing in this text. However, the neocybernetic model framework still makes it possible to conceptually master the complexity. There is more information about neocybernetics available in Internet — also this report is available there in electronic form:

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Genomic mining for complex disease traits with ‘‘random chemistry’’

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The lethality of a gene is a fundamental and representative measure for understanding the function of a gene and its associated bio-systems. Recently, many research groups have started focusing on the concept of synthetic lethality. The synthetic lethality between genes is defined by the combination of mutations in two genes causing cell death. Here, we confirm that synthetic lethality and cellular location have close relationships among the Saccharomyces cerevisiae genes. Furthermore, we attempt the prediction of candidate gene pairs with synthetic lethality. The prediction is based on the hierarchical aspect model (HAM) which learns from a data set of cellular location to estimate a likelihood value indicating the synthetic lethality between genes.

This chapter provides an overview of three crucial aspects of systems biology: constructing biological networks, analyzing and modeling the structure of biological networks, and modeling the dynamics of biological networks. We describe the types of intracellular networks most often studied, and the “omic ” information available to synthesize these networks, with a special focus on plant biology. We review the computational methods used to construct or infer (reverse engineer) intracellular networks. We present the graph theoretical measures most useful for understanding the organization of biological networks, from the single node level to the global properties of the whole network. A representative sample of biological network models is provided, ranging from static models to dynamic models that incorporate how the status of the nodes changes in time. Throughout the chapter we focus on the biological predictions possible by combining experimental, theoretical and computational methods.