Unlabeled pattern management through Semi-Supervised classification techniques

l'obbiettivo di questo progetto consiste nell'analizzare le performance di alcuni algoritmi di semi-supervised learning proposti negli ultimi anni. In particolare si è usato un algoritmo di feature selection basato su Self-training per determinare l'insieme ottimo di features per ogni dataset. Poi sono stati applicati alcuni algoritmi di semi-supervised learning per classificare i dati. Questi algoritmi sono stati testati usando rispettivamente come classificatore di base SVM e SMC

Investigating Evaluation Measures in Ant Colony Algorithms for Learning Decision Tree Classifiers

Ant-Tree-Miner is a decision tree induction algorithm that is based on the Ant Colony Optimization (ACO) meta- heuristic. Ant-Tree-Miner-M is a recently introduced extension of Ant-Tree-Miner that learns multi-tree classification models. A multi-tree model consists of multiple decision trees, one for each class value, where each class-based decision tree is responsible for discriminating between its class value and all other values present in the class domain (one vs. all). In this paper, we investigate the use of 10 different classification quality evaluation measures in Ant-Tree-Miner-M, which are used for both candidate model evaluation and model pruning. Our experimental results, using 40 popular benchmark datasets, identify several quality functions that substantially improve on the simple Accuracy quality function that was previously used in Ant-Tree-Miner-M

Mirna Expression Profiles Identify Drivers in Colorectal and Pancreatic Cancers

Altered expression of microRNAs (miRNAs) hallmarks many cancer types. The study of the associations of miRNA expression profile and cancer phenotype could help identify the links between deregulation of miRNA expression and oncogenic pathways.Expression profiling of 866 human miRNAs in 19 colorectal and 17 pancreatic cancers and in matched adjacent normal tissues was investigated. Classical paired t-test and random forest analyses were applied to identify miRNAs associated with tissue-specific tumors. Network analysis based on a computational approach to mine associations between cancer types and miRNAs was performed. in pancreatic cancers.MiRNA expression profiles may identify cancer-specific signatures and potentially useful biomarkers for the diagnosis of tissue specific cancers. miRNA-network analysis help identify altered miRNA regulatory networks that could play a role in tumor pathogenesis

PLoS One

Background and AimAltered expression of microRNAs (miRNAs) hallmarks many cancer types. The study of the associations of miRNA expression profile and cancer phenotype could help identify the links between deregulation of miRNA expression and oncogenic pathways.MethodsExpression profiling of 866 human miRNAs in 19 colorectal and 17 pancreatic cancers and in matched adjacent normal tissues was investigated. Classical paired t-test and random forest analyses were applied to identify miRNAs associated with tissue-specific tumors. Network analysis based on a computational approach to mine associations between cancer types and miRNAs was performed.ResultsThe merge between the two statistical methods used to intersect the miRNAs differentially expressed in colon and pancreatic cancers allowed the identification of cancer-specific miRNA alterations. By miRNA-network analysis, tissue-specific patterns of miRNA deregulation were traced: the driving miRNAs were miR-195, miR-1280, miR-140-3p and miR-1246 in colorectal tumors, and miR-103, miR-23a and miR-15b in pancreatic cancers.ConclusionMiRNA expression profiles may identify cancer-specific signatures and potentially useful biomarkers for the diagnosis of tissue specific cancers. miRNA-network analysis help identify altered miRNA regulatory networks that could play a role in tumor pathogenesis.RC0903CH47/CH/OID CDC HHS/United StatesRC0903GA51/RC/CCR NIH HHS/United StatesRC1003BS14/BS/FDA HHS/United StatesRC1003CH50/CH/OID CDC HHS/United StatesRC1003GA52/RC/CCR NIH HHS/United StatesRC1103CH46/CH/OID CDC HHS/United StatesRC1103GA47/RC/CCR NIH HHS/United States22479426PMC331649

A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines

Typical dimensionality reduction (DR) methods are data-oriented, focusing on directly reducing the number of random variables (or features) while retaining the maximal variations in the high-dimensional data. Targeting unsupervised situations, this paper aims to address the problem from a novel perspective and considers model-oriented dimensionality reduction in parameter spaces of binary multivariate distributions. Specifically, we propose a general parameter reduction criterion, called Confident-Information-First (CIF) principle, to maximally preserve confident parameters and rule out less confident ones. Formally, the confidence of each parameter can be assessed by its contribution to the expected Fisher information distance within a geometric manifold over the neighbourhood of the underlying real distribution. Then we demonstrate two implementations of CIF in different scenarios. First, when there are no observed samples, we revisit the Boltzmann Machines (BM) from a model selection perspective and theoretically show that both the fully visible BM (VBM) and the BM with hidden units can be derived from the general binary multivariate distribution using the CIF principle. This finding would help us uncover and formalize the essential parts of the target density that BM aims to capture and the non-essential parts that BM should discard. Second, when there exist observed samples, we apply CIF to the model selection for BM, which is in turn made adaptive to the observed samples. The sample-specific CIF is a heuristic method to decide the priority order of parameters, which can improve the search efficiency without degrading the quality of model selection results as shown in a series of density estimation experiments

Corporate governance and corporate failure: evidence from listed UK firms

This study is motivated by the numerous reforms to strengthen the efficacy of corporate boards and their oversight committees, in the wake of high profile corporate failures. The empirical question, however, is whether recent proposals would enhance board and their committee effectiveness and in this way, reduce the likelihood of firm`s failure. This study examines whether the composition, structure and functions of corporate boards and their interactions are related to the probability of corporate failure. Prior studies employ agency and resource dependency theories in isolation as theoretical lenses. This study, however, employs these aforementioned theories as theoretical lenses and argues that the board control and resource function affects the relationship between corporate board attributes and corporate failure. This study examines a sample of 358 UK listed firms, consisting of 95 failed firms and 263 non-failed firms during the period 1999-2011. This study also uses a unique hand-collected data set that measures the corporate governance attributes and functions of these 358 firms over a period of five years preceding failure or otherwise, resulting in 1748 firm-years observations. This study reveals that the probability of failure is lower in firms with large board size, former government officials, independent remuneration committee chairman and greater proportion of outside directors as well as effective audit and remuneration committees. This study also finds that the prospect of corporate failure is higher in firms with less than three independent NEDs on both the audit and nomination committees, without audit committee and where audit committee has no one with financial expertise. The results, however, suggest that the possibility of corporate failure is higher in firms whose boards have a female director and where the nomination committee meets often or where its membership is exclusively preserved for independent NEDs. On the interaction effects, the results show that frequency of board meetings as well as its interactions with presence of female directors, audit and remuneration committees effectiveness are positively related to the probability of corporate failure. The results also indicate that a number of interactions between corporate board attributes and functions are unrelated to the likelihood of corporate failure. These include the interactions between board composition measures (i.e. proportion of outside directors, presence of female directors and board size) and the board resource proxy (i.e. former government official). These associations, especially remuneration committee effectiveness, remuneration committee chairman independence, firm size and profitability, are not only statistically and economically significant but also robust to different specifications. Further, the Receiver Operating Curves indicate that the impact of corporate governance measures after controlling for firm size, liquidity, profitability, age, industry effects, and leverage is more profound in two years preceding failure. The implication of this is that corporate governance mechanisms alone are insufficient to rescue the firm on the verge of collapse. The findings are consistent with the idea that failing firms decline in size, managerial performance, corporate board attributes as well as their board`s ability to discharge it`s monitoring and resource roles. This study adds to the debate on the impact of corporate governance on corporate failure by developing, analysing and testing a robust UK corporate failure prediction model which is underpinned by a multi-theoretical framework: agency and resource dependency theories. This study also offers several recommendations for policy makers and firm-level corporate governance strategies in the mix of the numerous corporate governance reforms worldwide, this in particular makes this study unique

Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets

<p>Abstract</p> <p>Background:</p> <p><it>Mycobacterium tuberculosis </it>continues to be a major pathogen in the third world, killing almost 2 million people a year by the most recent estimates. Even in industrialized countries, the emergence of multi-drug resistant (MDR) strains of tuberculosis hails the need to develop additional medications for treatment. Many of the drugs used for treatment of tuberculosis target metabolic enzymes. Genome-scale models can be used for analysis, discovery, and as hypothesis generating tools, which will hopefully assist the rational drug development process. These models need to be able to assimilate data from large datasets and analyze them.</p> <p>Results:</p> <p>We completed a bottom up reconstruction of the metabolic network of <it>Mycobacterium tuberculosis </it>H37Rv. This functional <it>in silico </it>bacterium, <it>iNJ</it>661, contains 661 genes and 939 reactions and can produce many of the complex compounds characteristic to tuberculosis, such as mycolic acids and mycocerosates. We grew this bacterium <it>in silico </it>on various media, analyzed the model in the context of multiple high-throughput data sets, and finally we analyzed the network in an 'unbiased' manner by calculating the Hard Coupled Reaction (HCR) sets, groups of reactions that are forced to operate in unison due to mass conservation and connectivity constraints.</p> <p>Conclusion:</p> <p>Although we observed growth rates comparable to experimental observations (doubling times ranging from about 12 to 24 hours) in different media, comparisons of gene essentiality with experimental data were less encouraging (generally about 55%). The reasons for the often conflicting results were multi-fold, including gene expression variability under different conditions and lack of complete biological knowledge. Some of the inconsistencies between <it>in vitro </it>and <it>in silico </it>or <it>in vivo </it>and <it>in silico </it>results highlight specific loci that are worth further experimental investigations. Finally, by considering the HCR sets in the context of known drug targets for tuberculosis treatment we proposed new alternative, but equivalent drug targets.</p