Mixture-based probabilistic graphical models for the partial label ranking problem

The Label Ranking problem consists in learning preference models from training datasets labeled with a ranking of class labels, and the goal is to predict a ranking for a given unlabeled instance. In this work, we focus on the particular case where both, the training dataset and the prediction given as output allow tied labels (i.e., there is no particular preference among them), known as the Partial Label Ranking problem. In particular, we propose probabilistic graphical models to solve this problem. As far as we know, there is no probability distribution to model rankings with ties, so we transform the rankings into discrete variables to represent the precedence relations (precedes, ties and succeeds) among pair of class labels (multinomial distribution). In this proposal, we use a Bayesian network with Naive Bayes structure and a hidden variable as root to collect the interactions among the different variables (predictive and target). The inference works as follows. First, we obtain the posterior-probability for each pair of class labels, and then we input these probabilities to the pair order matrix used to solve the corresponding rank aggregation problem. The experimental evaluation shows that our proposals are competitive (in accuracy) with the state-of-the-art Instance Based Partial Label Ranking (nearest neighbors paradigm) and Partial Label Ranking Trees (decision tree induction) algorithms

Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem

The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In particular, fine-tuned instance-based algorithms (e.g., k-nearest neighbors) and model-based algorithms (e.g., decision trees) have performed remarkably well in tackling the LR problem. Probabilistic Graphical Models (PGMs, e.g., Bayesian networks) have not been considered to deal with this problem because of the difficulty of modeling permutations in that framework. In this paper, we propose a Hidden Naive Bayes classifier (HNB) to cope with the LR problem. By introducing a hidden variable, we can design a hybrid Bayesian network in which several types of distributions can be combined: multinomial for discrete variables, Gaussian for numerical variables, and Mallows for permutations. We consider two kinds of probabilistic models: one based on a Naive Bayes graphical structure (where only univariate probability distributions are estimated for each state of the hidden variable) and another where we allow interactions among the predictive attributes (using a multivariate Gaussian distribution for the parameter estimation). The experimental evaluation shows that our proposals are competitive with the start-of-the-art algorithms in both accuracy and in CPU time requirements

How do care-provider and home exercise program characteristics affect patient adherence in chronic neck and back pain: a qualitative study

<p>Abstract</p> <p>Background</p> <p>The aim of this study is to explore perceptions of people with chronic neck or low back pain about how characteristics of home exercise programs and care-provider style during clinical encounters may affect adherence to exercises.</p> <p>Methods</p> <p>This is a qualitative study consisting of seven focus groups, with a total of 34 participants presenting chronic neck or low back pain. The subjects were included if they were receiving physiotherapy treatment and were prescribed home-based exercises.</p> <p>Results</p> <p>Two themes emerged: home-based exercise programme conditions and care provider's style. In the first theme, the participants described their positive and negative experiences regarding time consumption, complexity and effects of prescribed exercises. In the second theme, participants perceived more bonding to prescribed exercises when their care provider presented knowledge about the disease, promoted feedback and motivation during exercise instruction, gave them reminders to exercise, or monitored their results and adherence to exercises.</p> <p>Conclusions</p> <p>Our experiential findings indicate that patient's adherence to home-based exercise is more likely to happen when care providers' style and the content of exercise programme are positively experienced. These findings provide additional information to health care providers, by showing which issues should be considered when delivering health care to patients presenting chronic neck or back pain.</p

Susceptibility of Protein Methionine Oxidation in Response to Hydrogen Peroxide Treatment–Ex Vivo Versus In Vitro: A Computational Insight

Methionine oxidation plays a relevant role in cell signaling. Recently, we built a database containing thousands of proteins identified as sulfoxidation targets. Using this resource, we have now developed a computational approach aimed at characterizing the oxidation of human methionyl residues. We found that proteins oxidized in both cell-free preparations (in vitro) and inside living cells (ex vivo) were enriched in methionines and intrinsically disordered regions. However, proteins oxidized ex vivo tended to be larger and less abundant than those oxidized in vitro. Another distinctive feature was their subcellular localizations. Thus, nuclear and mitochondrial proteins were preferentially oxidized ex vivo but not in vitro. The nodes corresponding with ex vivo and in vitro oxidized proteins in a network based on gene ontology terms showed an assortative mixing suggesting that ex vivo oxidized proteins shared among them molecular functions and biological processes. This was further supported by the observation that proteins from the ex vivo set were co-regulated more often than expected by chance. We also investigated the sequence environment of oxidation sites. Glutamate and aspartate were overrepresented in these environments regardless the group. In contrast, tyrosine, tryptophan and histidine were clearly avoided but only in the environments of the ex vivo sites. A hypothetical mechanism of methionine oxidation accounts for these observations presented

Las sumas de las potencias de Bernouilli: un problema histórico

Aledo, JA.; Cortés, J. (2003). Las sumas de las potencias de Bernouilli: un problema histórico. Sociedad Puig Adam de profesores de matemáticas. 64:35-44. http://hdl.handle.net/10251/150657S35446