A Parallel Implementation of the K Nearest Neighbours Classifier in Three Levels: Threads MPI Processes and the Grid

The work described in this paper tackles the problem of data mining and classification of large amounts of data using the K nearest neighbours classifier (KNN) [1]. The large computing demand of this process is solved with a parallel computing implementation specially designed to work in Grid environments of multiprocessor computer farms. The different parallel computing approaches (intra-node, inter-node and inter-organisations) are not sufficient by themselves to face the computing demand of such a big problem. Instead of using parallel techniques separately, we propose to combine the three of them considering the parallelism grain of the different parts of the problem. The main purpose is to complete a 1 month-CPU job in a few hours. The technologies that are being used are the EGEE Grid Computing Infrastructure running the Large Hadron Collider Computing Grid (LCG 2.6) middleware [3], MPI [4] [5] and POSIX [6] threads. Finally, we compare the results obtained with the most popular and used tools to understand the importance of this strategy.Aparicio Pla, G.; Blanquer Espert, I.; Hernández García, V. (2007). A Parallel Implementation of the K Nearest Neighbours Classifier in Three Levels: Threads MPI Processes and the Grid. En High Performance Computing for Computational Science - VECPAR 2006. Springer Verlag (Germany). 225-235. doi:10.1007/978-3-540-71351-7_18S225235Cover, T.M., Hart, P.E.: Nearest neighbour pattern recognition. IEEE Trans. on Information Theory 13(1), 2127 (1967)Foster, I., Kesselman, C., Tuecke, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International J. Supercomputer Applications 15(3) (2001), http://www.globus.org/research/papers/anatomy.pdfLCG: World Wide Web Computing Grid. Distributed Production Environment of Physics Data Processing. http://lcg.web.cern.ch/LCGMessage Passing Interface Forum: MPI: A message-passing interface standard (2003), http://www.mpi-forum.org/Gropp, W., et al.: MPI: The Complete Reference. MIT Press, Cambridge (1998)Drepper, U., Molnar, I.: The Native POSIX Thread Library for Linux (2003), http://people.redhat.com/drepper/nptl-design.pdfFrank, E., Hall, M., L.T.: Weka 3: Data Mining Software in Java (2005), http://www.cs.waikato.ac.nz/ml/wek

First anatomical network analysis of fore- and hindlimb musculoskeletal modularity in bonobos, common chimpanzees, and humans

Studies of morphological integration and modularity, and of anatomical complexity in human evolution typically focus on skeletal tissues. Here we provide the first network analysis of the musculoskeletal anatomy of both the fore- and hindlimbs of the two species of chimpanzee and humans. Contra long-accepted ideas, network analysis reveals that the hindlimb displays a pattern opposite to that of the forelimb: Pan big toe is typically seen as more independently mobile, but humans are actually the ones that have a separate module exclusively related to its movements. Different fore- vs hindlimb patterns are also seen for anatomical network complexity (i.e., complexity in the arrangement of bones and muscles). For instance, the human hindlimb is as complex as that of chimpanzees but the human forelimb is less complex than in Pan. Importantly, in contrast to the analysis of morphological integration using morphometric approaches, network analyses do not support the prediction that forelimb and hindlimb are more dissimilar in species with functionally divergent limbs such as bipedal humans

The development of non-coding RNA ontology

Identification of non-coding RNAs (ncRNAs) has been significantly improved over the past decade. On the other hand, semantic annotation of ncRNA data is facing critical challenges due to the lack of a comprehensive ontology to serve as common data elements and data exchange standards in the field. We developed the Non-Coding RNA Ontology (NCRO) to handle this situation. By providing a formally defined ncRNA controlled vocabulary, the NCRO aims to fill a specific and highly needed niche in semantic annotation of large amounts of ncRNA biological and clinical data