Distributed data mining in grid computing environments

The official published version of this article can be found at the link below.The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Distributed Data Mining (DDM), calls for the support of a powerful Grid with an effective scheduling framework. DDM often shares the computing paradigm of local processing and global synthesizing. It involves every phase of Data Mining (DM) processes, which makes the workflow of DDM very complex and can be modelled only by a Directed Acyclic Graph (DAG) with multiple data entries. Motivated by the need for a practical solution of the Grid scheduling problem for the DDM workflow, this paper proposes a novel two-phase scheduling framework, including External Scheduling and Internal Scheduling, on a two-level Grid architecture (InterGrid, IntraGrid). Currently a DM IntraGrid, named DMGCE (Data Mining Grid Computing Environment), has been developed with a dynamic scheduling framework for competitive DAGs in a heterogeneous computing environment. This system is implemented in an established Multi-Agent System (MAS) environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. Practical classification problems from oil well logging analysis are used to measure the system performance. The detailed experiment procedure and result analysis are also discussed in this paper

La memoria del Grand Tour. Un set di strumenti chirurgici ‘pompeiani’ in collezione privata a Padova

Il presente lavoro si inquadra in una ricerca condotta dal Dipartimento di Studi Umanistici e del Patrimonio Culturale dell’Università di Udine sulla produzione e il mercato dei falsi; il caso oggetto di studio è uno strumentario chirurgico pseudo-antico da una collezione privata vicentina, del quale si approfondisce l’analisi stilistica grazie al contributo degli esami effettuati nel laboratorio della Soprintendenza Archeologia del Veneto. Ciò costituisce la premessa per indagare i motivi ispiratori e gli obiettivi di tale produzione, nel tentativo di individuarne produttori e destinatari. L’ipotesi sottesa alla ricerca è che l’altissimo grado di verosimiglianza, ottenuto nella realizzazione del falso sia attribuibile al fatto che sia stato prodotto in un arco di tempo ben determinato, per un ambiente di collezionisti e studiosi, a scopo eminentemente scientifico. Per ulteriori conferme viene contestualmente analizzato il contenitore nel quale gli strumenti sono stati conservati: una miniatura laccata proveniente dalla Russia di inizio Novecento, produzione di un certo pregio artistico, piuttosto famosa e diffusa in tutta Europa e soprattutto nei paesi dell’ex blocco sovietico. La produzione del cofanetto, il suo allestimento per gli strumenti, la storia del suo arrivo nella collezione Rossi, sembrano riconducibili per molti dettagli al periodo storico e all’ambiente del collezionismo antiquario e scientifico per il quale potrebbero essere stati prodotti gli strumenti.This article belongs to a further research made by the Department of Humanistic Studies and Cultural Heritage of University of Udine about the production of fakes and their commerce and distribution in the antiques market. As case study it a surgical set from a private collection in Vicenza, which can be regarded as an imitation of ancient medical tools from Pompei: our hypothesis is that the high quality of the fake suggests that it might have been made in a specific time frame and destined to collectors of antiquities as well as scholars for scientific purposes. To prove more the contribution has focussed also on the box containing the medical tools: it is a Russian lacquered miniature, dated back to the beginning of 20th century. This item reveals a significant historical and artistic value, very popular and widespread all over Europe, mostly in the ex URSS countries. Many details about the production, the set-up, the history and the passage of the box in the Rossi collection seem to be ascribable to the same time span, the same realm which the surgical instruments might have been produced for. As a result the study suggests that a specific production of fakes might have occurred during the second half of 19th and 20th century across Europe, with wide target of collectors and scholars with interests in medical tools and surgery from the classical worl

DMET-Miner: Efficient discovery of association rules from pharmacogenomic data

AbstractMicroarray platforms enable the investigation of allelic variants that may be correlated to phenotypes. Among those, the Affymetrix DMET (Drug Metabolism Enzymes and Transporters) platform enables the simultaneous investigation of all the genes that are related to drug absorption, distribution, metabolism and excretion (ADME). Although recent studies demonstrated the effectiveness of the use of DMET data for studying drug response or toxicity in clinical studies, there is a lack of tools for the automatic analysis of DMET data. In a previous work we developed DMET-Analyzer, a methodology and a supporting platform able to automatize the statistical study of allelic variants, that has been validated in several clinical studies. Although DMET-Analyzer is able to correlate a single variant for each probe (related to a portion of a gene) through the use of the Fisher test, it is unable to discover multiple associations among allelic variants, due to its underlying statistic analysis strategy that focuses on a single variant for each time. To overcome those limitations, here we propose a new analysis methodology for DMET data based on Association Rules mining, and an efficient implementation of this methodology, named DMET-Miner. DMET-Miner extends the DMET-Analyzer tool with data mining capabilities and correlates the presence of a set of allelic variants with the conditions of patient’s samples by exploiting association rules. To face the high number of frequent itemsets generated when considering large clinical studies based on DMET data, DMET-Miner uses an efficient data structure and implements an optimized search strategy that reduces the search space and the execution time. Preliminary experiments on synthetic DMET datasets, show how DMET-Miner outperforms off-the-shelf data mining suites such as the FP-Growth algorithms available in Weka and RapidMiner. To demonstrate the biological relevance of the extracted association rules and the effectiveness of the proposed approach from a medical point of view, some preliminary studies on a real clinical dataset are currently under medical investigation

A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease

This work investigates the problem of feature selection in neuroimaging features from structural MRI brain images for the classification of subjects as healthy controls, suffering from Mild Cognitive Impairment or Alzheimer’s Disease. A Genetic Algorithm wrapper method for feature selection is adopted in conjunction with a Support Vector Machine classifier. In very large feature sets, feature selection is found to be redundant as the accuracy is often worsened when compared to an Support Vector Machine with no feature selection. However, when just the hippocampal subfields are used, feature selection shows a significant improvement of the classification accuracy. Three-class Support Vector Machines and two-class Support Vector Machines combined with weighted voting are also compared with the former and found more useful. The highest accuracy achieved at classifying the test data was 65.5% using a genetic algorithm for feature selection with a three-class Support Vector Machine classifier

Spotted fever from Rickettsia typhi in an older woman: a case report from a geographic area where it would not be expected

Summary We describe the case of a 75-year-old woman presenting with spotted fever followed by acute renal failure and septic shock. The infection was caused by Rickettsia typhi , not reported in Calabria district (southern Italy) since World War II. The diagnosis of murine typhus was made 3 days after admission and was based solely on clinical criteria when her worsening condition required a prompt move to the intensive care unit. Therapy with tigecycline was then started immediately and the patient improved dramatically. The diagnosis of murine typhus was confirmed 10 days after admission by immunofluorescence assay. Our case is an example of how the diagnosis of murine typhus is challenging. However, in the case of a disease lacking specific symptoms, clinicians should never forget that, even in geographic areas considered free of flea-borne diseases, the components of the enzootic cycle are present and the diagnosis should never be underestimated

MuLaN: a MultiLayer Networks Alignment Algorithm

A Multilayer Network (MN) is a system consisting of several topological levels (i.e., layers) representing the interactions between the system's objects and the related interdependency. Therefore, it may be represented as a set of layers that can be assimilated to a set of networks of its own objects, by means inter-layer edges (or inter-edges) linking the nodes of different layers; for instance, a biological MN may allow modeling of inter and intra interactions among diseases, genes, and drugs, only using its own structure. The analysis of MNs may reveal hidden knowledge, as demonstrated by several algorithms for the analysis. Recently, there is a growing interest in comparing two MNs by revealing local regions of similarity, as a counterpart of Network Alignment algorithms (NA) for simple networks. However, classical algorithms for NA such as Local NA (LNA) cannot be applied on multilayer networks, since they are not able to deal with inter-layer edges. Therefore, there is the need for the introduction of novel algorithms. In this paper, we present MuLaN, an algorithm for the local alignment of multilayer networks. We first show as proof of concept the performances of MuLaN on a set of synthetic multilayer networks. Then, we used as a case study a real multilayer network in the biomedical domain. Our results show that MuLaN is able to build high-quality alignments and can extract knowledge about the aligned multilayer networks. MuLaN is available at https://github.com/pietrocinaglia/mulan

Advanced feature selection methods in multinominal dementia classification from structural MRI data

Recent studies showed that features extracted from brain MRIs can well discriminate Alzheimer’s disease from Mild Cognitive Impairment. This study provides an algorithm that sequentially applies advanced feature selection methods for findings the best subset of features in terms of binary classification accuracy. The classifiers that provided the highest accuracies, have been then used for solving a multi-class problem by the one-versus-one strategy. Although several approaches based on Regions of Interest (ROIs) extraction exist, the prediction power of features has not yet investigated by comparing filter and wrapper techniques. The findings of this work suggest that (i) the IntraCranial Volume (ICV) normalization can lead to overfitting and worst the accuracy prediction of test set and (ii) the combined use of a Random Forest-based filter with a Support Vector Machines-based wrapper, improves accuracy of binary classification

10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2017

Agapito, G., Cannataro, M., Castelli, M., Dondi, R., & Zoppis, I. (2017). 10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2017. Procedia Computer Science, 108, 1113-1114. https://doi.org/10.1016/j.procs.2017.05.279We present the 10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2017, held in Zurich, 12 - 14 June 2017.publishersversionpublishe