Catálogo de publicaciones - libros

Life logs include people’s experiences collected from various sources. It is used to support user’s memory. There are many studies that collect and store life log for personal memory. In this paper, we collect log data from smart phone, derive contexts from the log, and then identify which is meaningful context by using a method based on KeyGraph. To evaluate the proposed method, we show an example of the meaningful places by using contexts and GPS logs collected from two users.

In the information age, online collaboration and social networks are of increasing importance and quickly becoming an integral part of our lifestyle. In business, social networking can be a powerful tool to expand a customer network to which a company can sell products and services, or find new partners / employees in a more trustworthy and targeted manner. Identifying new friends or partners, on social networking websites, is usually done via a keyword search, browsing a directory of topics (e.g. interests, geography, or employer) or a chain of social ties (e.g. links to other friends on a user’s profile). However there are limitations to these three approaches. Keyword search typically produces a list of ranked results, where traversing pages of ranked results can be tedious and time consuming to explore. A directory of groups / networks is generally created manually, requires significant ongoing maintenance and cannot keep up with rapid changes. Social chains require the initial users to specify metadata in their profile settings and again may no be up to date. In this paper we propose to use the topological tree method to dynamically identify similar groups based on metadata and content. The topological tree method is used to automatically organise social networking groups. The retrieved results, organised using an online version of the topological tree method, are discussed against to the returned results of a social network search. A discussion is made on the criterions of representing social relationships, and the advantages of presenting underlying topics and providing a clear view of the connections between topics. The topological tree has been found to be a superior representation and well suited for organising social networking content.

Microarray technology produces large amounts of information to be manipulated by analysis methods, such as biclustering algorithms, to extract new knowledge. All-purpose multivariate data visualization tools are usually not enough for studying microarray experiments. Additionally, clustering tools do not provide means of simultaneous visualization of all the biclusters obtained.

We present an interactive tool that integrates traditional visualization techniques with others related to bioinformatics, such as transcription regulatory networks and microarray heatmaps, to provide enhanced understanding of the biclustering results. Our aim is to gain insight about the structure of biological data and the behavior of different biclustering algorithms.

There are lots of validation indexes and techniques to study clustering results. Biclustering algorithms have been applied in Systems Biology, principally in DNA Microarray analysis, for the last years, with great success. Nowadays, there is a big set of biclustering algorithms each one based in different concepts, but there are few intercomparisons that measure their performance. We review and present here some numerical measures, new and evolved from traditional clustering validation techniques, to allow comparisons and validation of biclustering algorithms.

Classification of micro-array data has been studied extensively but only a small amount of research work has been done on classification of micro-array data involving more than two classes. This paper proposes a learning strategy that deals with building a multi-target classifier and takes advantage from well known data mining techniques. To address the intrinsic difficulty of selecting features in order to promote the classification accuracy, the paper considers the use of a set of binary classifiers each of ones is devoted to predict a single class of the multi-classification problem. These classifiers are similar to local experts whose knowledge (about the features that are most correlated to each class value) is taken into account by the learning strategy for selecting an optimal set of features. Results of the experiments performed on a publicly available dataset demonstrate the feasibility of the proposed approach.

Dimensionality reduction can often improve the performance of the k-nearest neighbor classifier (kNN) for high-dimensional data sets, such as microarrays. The effect of the choice of dimensionality reduction method on the predictive performance of kNN for classifying microarray data is an open issue, and four common dimensionality reduction methods, Principal Component Analysis (PCA), Random Projection (RP), Partial Least Squares (PLS) and Information Gain(IG), are compared on eight microarray data sets. It is observed that all dimensionality reduction methods result in more accurate classifiers than what is obtained from using the raw attributes. Furthermore, it is observed that both PCA and PLS reach their best accuracies with fewer components than the other two methods, and that RP needs far more components than the others to outperform kNN on the non-reduced dataset. None of the dimensionality reduction methods can be concluded to generally outperform the others, although PLS is shown to be superior on all four binary classification tasks, but the main conclusion from the study is that the choice of dimensionality reduction method can be of major importance when classifying microarrays using kNN.

Many proteins are composed of two or more subunits, each associated with different polypeptide chains. The number and the arrangement of subunits forming a protein are referred to as . The quaternary structure of a protein is important, since it characterizes the biological function of the protein when it is involved in specific biological processes. Unfortunately, quaternary structures are not trivially deducible from protein amino acid sequences. In this work, we propose a protein quaternary structure classification method exploiting the functional domain composition of proteins. It is based on a nearest neighbor condensation technique in order to reduce both the portion of dataset to be stored and the number of comparisons to carry out. Our approach seems to be promising, in that it guarantees an high classification accuracy, even though it does not require the entire dataset to be analyzed. Indeed, experimental evaluations show that the method here proposed selects a small dataset portion for the classification (of the order of the 6.43%) and that it is very accurate (97.74%).

A novel technique to search for functional modules in a protein-protein interaction network is presented. The network is represented by the adjacency matrix associated with the undirected graph modelling it. The algorithm introduces the concept of of a sub-matrix of the adjacency matrix, and applies a greedy search technique for finding local optimal solutions made of dense sub-matrices containing the maximum number of ones. An initial random solution, constituted by a single protein, is evolved to search for a locally optimal solution by adding/removing connected proteins that best contribute to improve the function. Experimental evaluations carried out on proteins show that the algorithm is able to efficiently isolate groups of biologically meaningful proteins corresponding to the most compact sets of interactions.

The biclustering techniques have the purpose of finding subsets of genes that show similar activity patterns under a subset of conditions. In this paper we characterize a specific type of pattern, that we have called –pattern, and present an approach that consists in a new biclustering algorithm specifically designed to find –patterns, in which the gene expression values evolve across the experimental conditions showing a similar behavior inside a band that ranges from 0 up to a pre–defined threshold called . The value guarantees the co–expression among genes. We have tested our method on the dataset and compared the results to the biclustering algorithms of Cheng & Church (2000) and Aguilar & Divina (2005). Results show that the algorithm finds interesting biclusters, grouping genes with similar behaviors and maintaining a very low mean squared residue.

Microarray techniques have motivated the develop of different methods to extract useful information from a biological point of view. Biclustering algorithms obtain a set of genes with the same behaviour over a group of experimental conditions from gene expression data. In order to evaluate the quality of a bicluster, it is useful to identify specific tendencies represented by patterns on data. These patterns describe the behaviour of a bicluster obtained previously by an adequate biclustering technique from gene expression data. In this paper a new measure for evaluating biclusters is proposed. This measure captures a special kind of patterns with scaling trends which represents quality patterns. They are not contemplated with the previous evaluating measure accepted in the literature. This work is a first step to investigate methods that search biclusters based on the concept of shift and scale invariance. Experimental results based on the yeast cell cycle and the human B-cell lymphoma datasets are reported. Finally, the performance of the proposed technique is compared with an optimization method based on the Nelder-Mead Simplex search algorithm.