Mining frequent patterns from large datasets is an important issue in data mining. Recently, complex and unstructured (or semi-structured) datasets have appeared as targets for major data mining applications, including text mining, web mining and bioinformatics. Our work focuses on labeled ordered trees, which are typically semi-structured datasets. In bioinformatics, carbohydrate sugar chains, or glycans, can be modeled as labeled ordered trees. Glycans are the third major class of biomolecules, having important roles in signaling and recognition. For mining labeled ordered trees, we propose a new probabilistic model and its efficient learning scheme which significantly improves the time and space complexity of an existing probabilistic model for labeled ordered trees. We evaluated the performance of the proposed model, comparing it with those of other probabilistic models, using synthetic as well as real datasets from glycobiology. Experimental results showed that the proposed model drastically reduced the computation time of the competing model, keeping the predictive power and avoiding overfitting to the training data. Finally, we assessed our results on real data from a variety of biological viewpoints, verifying known facts in glycobiology.

Mining frequent patterns is a general and important issue in data mining. Complex and unstructured (or semi-structured) datasets have appeared in major data mining applications, including text mining, web mining and bioinformatics. Mining patterns from these datasets is the focus of many of the current data mining approaches. We focus on labeled ordered trees, typical datasets of semi-structured data in data mining, and propose a new probabilistic model and its efficient learning scheme for mining labeled ordered trees. The proposed approach significantly improves the time and space complexity of an existing probabilistic modeling for labeled ordered trees, while maintaining its expressive power. We evaluated the performance of the proposed model, comparing it with that of the existing model, using synthetic as well as real datasets from the field of glycobiology. Experimental results showed that the proposed model drastically reduced the computation time of the competing model, keeping the predictive power and avoiding overfitting to the training data. Finally, we assessed our results using the proposed model on real data from a variety of biological viewpoints, verifying known facts in glycobiology.

Glycans, or carbohydrate sugar chains, which play a number of important roles in the development and functioning of multicellular organisms, can be regarded as labeled ordered trees. A recent increase in the documentation of glycan structures, especially in the form of database curation, has made mining glycans important for the understanding of living cells. We propose a probabilistic model for mining labeled ordered trees, and we further present an efficient learning algorithm for this model, based on an EM algorithm. The time and space complexities of this algorithm are rather favorable, falling within the practical limits set by a variety of existing probabilistic models, including stochastic context-free grammars. Experimental results have shown that, in a supervised problem setting, the proposed method outperformed five other competing methods by a statistically significant factor in all cases. We further applied the proposed method to aligning multiple glycan trees, and we detected biologically significant common subtrees in these alignments where the trees are automatically classified into subtypes already known in glycobiology.

Positive definite kernels between labeled graphs have recently been proposed. They enable the application of kernel methods, such as support vector machines, to the analysis and classification of graphs, for example, chemical compounds. These graph kernels are obtained by marginalizing a kernel between paths with respect to a random walk model on the graph vertices along the edges. We propose two extensions of these graph kernels, with the double goal to reduce their computation time and increase their relevance as measure of similarity between graphs. First, we propose to modify the label of each vertex by automatically adding information about its environment with the use of the Morgan algorithm. Second, we suggest a modification of the random walk model to prevent the walk from coming back to a vertex that was just visited. These extensions are then tested on benchmark experiments of chemical compounds classification, with promising results.

We propose a new probabilistic model for mining labeled ordered trees. A noteworthy feature of the proposed model is to consider ordered siblings by modeling the dependencies of a node in a tree on the elder sibling as well as the parent. This model is reasonably extended from a variety of existing probabilistic models for strings and trees. We further propose a new learning/mining method to estimate the parameters of this model, based on an EM algorithm. This is also an extension of those for various simpler probabilistic models, such as hidden Markov models and hidden tree Markov models. We evaluated the effectiveness of our proposed method using both synthetic and real-world data sets, comparing the results with those of several simpler probabilistic models. Experimental results have shown that our proposed method outperforms the other methods compared, being statistically significant in all cases tested. This result tells us that the proposed methodology is highly effective for mining labeled ordered trees, which have recently emerged as one of the typical data structures in numerous data mining domains, including the web, text mining and bioinformatics.

MOTIVATION: Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVMs) are currently the most effective methods for the problem of superfamily recognition in the Structural Classification Of Proteins (SCOP) database. The performance of SVMs depends critically on the kernel function used to quantify the similarity between sequences. RESULTS: We propose new kernels for strings adapted to biological sequences, which we call local alignment kernels. These kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. When tested in combination with SVM on their ability to recognize SCOP superfamilies on a benchmark dataset, the new kernels outperform state-of-the-art methods for remote homology detection. AVAILABILITY: Software and data available upon request.

BACKGROUND: Many real networks can be understood as two complementary networks with two kind of nodes. This is the case of metabolic networks where the first network has chemical compounds as nodes and the second one has nodes as reactions. In general, the second network may be related to the first one by a technique called line graph transformation (i.e., edges in an initial network are transformed into nodes). Recently, the main topological properties of the metabolic networks have been properly described by means of a hierarchical model. While the chemical compound network has been classified as hierarchical network, a detailed study of the chemical reaction network had not been carried out. RESULTS: We have applied the line graph transformation to a hierarchical network and the degree-dependent clustering coefficient C(k) is calculated for the transformed network. C(k) indicates the probability that two nearest neighbours of a vertex of degree k are connected to each other. While C(k) follows the scaling law C(k) approximately k(-1.1) for the initial hierarchical network, C(k) scales weakly as k0.08 for the transformed network. This theoretical prediction was compared with the experimental data of chemical reactions from the KEGG database finding a good agreement. CONCLUSIONS: The weak scaling found for the transformed network indicates that the reaction network can be identified as a degree-independent clustering network. By using this result, the hierarchical classification of the reaction network is discussed.