Assessing similarity of feature selection techniques in high-dimensional domains

Recent research efforts attempt to combine multiple feature selection techniques instead of using a single one. However, this combination is often made on an “ad hoc” basis, depending on the specific problem at hand, without considering the degree of diversity/similarity of the involved methods. Moreover, though it is recognized that different techniques may return quite dissimilar outputs, especially in high dimensional/small sample size domains, few direct comparisons exist that quantify these differences and their implications on classification performance. This paper aims to provide a contribution in this direction by proposing a general methodology for assessing the similarity between the outputs of different feature selection methods in high dimensional classification problems. Using as benchmark the genomics domain, an empirical study has been conducted to compare some of the most popular feature selection methods, and useful insight has been obtained about their pattern of agreement

A model for term selection in text categorization problems

In the last ten years, automatic Text Categorization (TC) has been gaining an increasing interest from the research community, due to the need to organize a massive number of digital documents. Following a machine learning paradigm, this paper presents a model which regards TC as a classification task supported by a wrapper approach and combines the utilization of a Genetic Algorithm (GA) with a filter. First, a filter is used to weigh the relevance of terms in documents. Then, the top-ranked terms are grouped in several nested sets of relatively small size. These sets are explored by a GA which extracts the subset of terms that best categorize documents. Experimental results on the Reuters-21578 dataset state the effectiveness of the proposed model and its competitiveness with the learning approaches proposed in the TC literature

Characterization of the B-cell immune response elicited in BALB/c mice challenged with Neospora caninum tachyzoites

Activation of B cells occurring in hosts infected with protozoan parasites has been implicated either in protective or parasite-evasion immune-mediated mechanisms. Intraperitoneal inoculation of Neospora caninum tachyzoites into BALB/c mice induces an acute response characterized by a rapid increase in the numbers of CD69-expressing peritoneal and splenic B cells. This early B-cell stimulatory effect preceded an increase in the numbers of total and immunoglobulin-secreting splenic B cells and a rise in serum levels of N. caninum-specific immunoglobulins, predominantly of the immunoglobulin G2a (IgG2a) and IgM isotypes. Increased numbers of B cells expressing the costimulatory molecules CD80 and CD86 were also observed in the N. caninum-infected mice. The B-cell stimulatory effect observed in mice challenged with N. caninum tachyzoites was reduced in mice challenged with γ-irradiated parasites. Contrasting with the peripheral B-cell expansion, a depletion of B-lineage cells was observed in the bone-marrow of the N. caninum-infected mice. Intradermal immunization of BALB/c mice with diverse N. caninum antigenic preparations although inducing the production of parasite-specific antibodies nevertheless impaired interferon-γ (IFN-γ) mRNA expression and caused lethal susceptibility to infection in mice inoculated with a non-lethal parasitic inoculum. This increased susceptibility to N. caninum was not observed in naïve mice passively transferred with anti-N. caninum antibodies. Taken together, these results show that N. caninum induces in BALB/c mice a parasite-specific, non-polyclonal, B-cell response, reinforce previous observations made by others showing that immunization with N. caninum whole structural antigens increases susceptibility to murine neosporosis and further stress the role of IFN-γ in the host protective immune mechanisms against this parasite

Evaluating feature selection robustness on high-dimensional data

With the explosive growth of high-dimensional data, feature selection has become a crucial step of machine learning tasks. Though most of the available works focus on devising selection strategies that are effective in identifying small subsets of predictive features, recent research has also highlighted the importance of investigating the robustness of the selection process with respect to sample variation. In presence of a high number of features, indeed, the selection outcome can be very sensitive to any perturbations in the set of training records, which limits the interpretability of the results and their subsequent exploitation in real-world applications. This study aims to provide more insight about this critical issue by analysing the robustness of some state-of-the-art selection methods, for different levels of data perturbation and different cardinalities of the selected feature subsets. Furthermore, we explore the extent to which the adoption of an ensemble selection strategy can make these algorithms more robust, without compromising their predictive performance. The results on five high-dimensional datasets, which are representatives of different domains, are presented and discussed

Bombyx mori Nucleopolyhedrovirus Displaying Neospora caninum Antigens as a Vaccine Candidate Against N. caninum Infection in Mice

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Pisa syndrome in Parkinson disease. An observational multicenter Italian study

Objective: To estimate the prevalence of Pisa syndrome (PS) in patients with Parkinson disease (PD) and to assess the association between PS and demographic and clinical variables. Methods: In this multicenter cross-sectional study, consecutive outpatients with PD attending 21 movement disorders Italian tertiary centers were enrolled and underwent standardized clinical evaluation. PS was defined as trunk lateral deviation 10. Patients with PD were compared according to the presence of PS for several demographic and clinical variables. Results: Among 1,631 enrolled patients with PD, PS was detected in 143 patients (8.8%, 95% confidence interval 7.4%-10.3%). Patients with PS were older, had lower body mass index, longer disease duration, higher disease stages, and poorer quality of life. Falls were more frequent in the PS group as well as occurrence of "veering gait" (i.e., the progressive deviation toward one side when patient walked forward and backward with eyes closed). Patients with PS received higher daily levodopa equivalent daily dose and were more likely to be treated with combination of levodopa and dopamine agonists. Osteoporosis and arthrosis were significantly the most frequent associatedmedical conditions in patients with PS. Multiple explanatory variable logistic regression models confirmed the association of PSwith the following variables: Hoehn and Yahr stage, ongoing combined treatment with levodopa and dopamine agonist, associated medical conditions, and presence of veering gait. Conclusions: Our results suggest that PS is a relatively frequent and often disabling complication in PD, especially in the advanced disease stages. The association is dependent on a number of potentially relevant demographic and clinical variables