Incorporating label dependency into the binary relevance framework for multi-label classification

In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multi-label learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. Aiming to accurately predict label combinations, in this paper we propose a simple approach that enables the binary classifiers to discover existing label dependency by themselves. An experimental study using decision trees, a kernel method as well as Naive Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.

Decreasing the number of false positives in sequence classification

Abstract Background A large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation. Results For all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results. Conclusions Of the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.

Association between muscle strength and the cardiopulmonary status of individuals living with HIV/AIDS

OBJECTIVE: The purpose of this study was to compare aerobic function [anaerobic threshold (%_VVO2-AT), respiratory compensation point (%_VVO2-RCP) and peak oxygen uptake (_VVO2peak)] between physically active patients with HIV/AIDS and matched controls and to examine associations between disease status, poor muscle strength, depression (as estimated by the profile of mood states questionnaire) and the aerobic performance of patients. METHODS: Progressive treadmill test data for %_VVO2-AT (V-slope method), RCP and (_VVO2peak) were compared between 39 male patients with HIV/AIDS (age 40.6¡1.4 years) and 28 male controls (age 44.4¡2.1 years) drawn from the same community and matched for habitual physical activity. Within-patient data were also examined in relation to CD4+ counts (nadir and current data) and peak isokinetic knee torque. RESULTS: AT, RCP and (_VVO2peak) values were generally similar for patients and controls.Within the patient sample, binary classification suggested that AT, RCP and (_VVO2peak) values were not associated with either the nadir or current CD4+ count, but treadmill test variables were positively associated with peak isokinetic knee torque. CONCLUSION: The aerobic performance of physically active patients with HIV/AIDS is generally well conserved. Nevertheless, poor muscle strength is observed in some HIV/AIDS patients, which is associated with lower anaerobic power and (_VVO2peak), suggesting the possibility of enhancing the aerobic performance of patients with weak muscles through appropriate muscle-strengthening activities.

Binary and ternary inclusion complexes of dapsone in cyclodextrins and polymers: preparation, characterization and evaluation

Dapsone (DAP) is a synthetic sulfone drug with bacteriostatic activity, mainly against Mycobacterium leprae. In this study we have investigated the interactions of DAP with cyclodextrins, 2-hydroxypropyl-beta-cyclodextrin (HP beta CD) and beta-cyclodextrin (beta CD), in the presence and absence of water-soluble polymers, in order to improve its solubility and bioavailability. Solid systems DAP/HP beta CD and DAP/beta CD, in the presence or absence of polyvinylpyrrolidone (PVP K30) or hydroxypropyl methylcellulose (HPMC), were prepared. The binary and ternary systems were evaluated and characterized by SEM, DSC, XRD and NMR analysis as well as phase solubility assays, in order to investigate the interactions between DAP and the excipients in aqueous solution. This study revealed that inclusion complexes of DAP and cyclodextrins (HP beta CD and beta CD) can be produced in order to improve DAP solubility and bioavailability in the presence or absence of polymers (PVP K30 and HPMC). The more stable inclusion complex was obtained with HP beta CD, and consequently HP beta CD was more efficient in improving DAP solubility than beta CD, and the addition of polymers had no influence on DAP solubility or on the stability of the DAP/CDs complexes.

Combining fractal and deterministic walkers for texture analysis and classification

In this paper,we present a novel texture analysis method based on deterministic partially self-avoiding walks and fractal dimension theory. After finding the attractors of the image (set of pixels) using deterministic partially self-avoiding walks, they are dilated in direction to the whole image by adding pixels according to their relevance. The relevance of each pixel is calculated as the shortest path between the pixel and the pixels that belongs to the attractors. The proposed texture analysis method is demonstrated to outperform popular and state-of-the-art methods (e.g. Fourier descriptors, occurrence matrix, Gabor filter and local binary patterns) as well as deterministic tourist walk method and recent fractal methods using well-known texture image datasets.