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Large scale data handling

Xem 1-8 trên 8 kết quả Large scale data handling
  • The reconstruction of gene regulatory network (GRN) from gene expression data can discover regulatory relationships among genes and gain deep insights into the complicated regulation mechanism of life. However, it is still a great challenge in systems biology and bioinformatics. During the past years, numerous computational approaches have been developed for this goal, and Bayesian network (BN) methods draw most of attention among these methods because of its inherent probability characteristics.

    pdf14p vilarryellison 29-10-2021 11 0   Download

  • The development of high-throughput experimental technologies, such as next-generation sequencing, have led to new challenges for handling, analyzing and integrating the resulting large and diverse datasets. Bioinformatical analysis of these data commonly requires a number of mutually dependent steps applied to numerous samples for multiple conditions and replicates.

    pdf13p viconnecticut2711 28-10-2020 10 0   Download

  • Feature screening plays a critical role in handling ultrahigh dimensional data analyses when the number of features exponentially exceeds the number of observations. It is increasingly common in biomedical research to have case-control (binary) response and an extremely large-scale categorical features.

    pdf15p vicolorado2711 22-10-2020 8 1   Download

  • A study was conducted to know the mortality pattern in wild animals received from rescue teams in Non Protected Areas of Territorial Forest Division Jabalpur, Madhya Pradesh. A total of 104 deaths were recorded in seventeen different species of wild animals during the period from April 2015 - March 2017. The cause specific mortality was determined on the basis of data available from primary and secondary resources. The history and pathoanatomical changes observed at necropsy were recorded among animals brought at the School of wildlife Forensic and Health, NDVSU, Jabalpur, MP, India.

    pdf7p nguaconbaynhay1 04-12-2019 9 1   Download

  • Ready to go to the next level with Rails? From examining the parts of Ruby that make this framework possible to deploying large Rails applications, Advanced Rails offers you an in-depth look at techniques for dealing with databases, security, performance, web services and much more. Chapters in this book help you understand not only the tricks and techniques used within the Rails framework itself, but also how to make use of ideas borrowed from other programming paradigms.

    pdf359p trac2_123 16-04-2013 66 9   Download

  • This paper provides evidence that the use of more unlabeled data in semi-supervised learning can improve the performance of Natural Language Processing (NLP) tasks, such as part-of-speech tagging, syntactic chunking, and named entity recognition. We first propose a simple yet powerful semi-supervised discriminative model appropriate for handling large scale unlabeled data. Then, we describe experiments performed on widely used test collections, namely, PTB III data, CoNLL’00 and ’03 shared task data for the above three NLP tasks, respectively. ...

    pdf9p hongphan_1 15-04-2013 55 1   Download

  • powerful and widely-used method for analyzing the performance behavior of parallel programs is event tracing. When an application is traced, performancerelevant events, such as entering functions or sending messages, are recorded at runtime and analyzed post-mortem to identify and potentially remove performance problems. While event tracing enables the detection of performance problems at a high level of detail, growing trace-file size often constrains its scalability on large-scale systems and complicates management, analysis, and visualization of trace data.

    pdf10p giamdocamnhac 06-04-2013 52 4   Download

  • Data Handling in Biology--the application of computational and analytical methods to biological problems--is a rapidly evolving scientific discipline. Written in a clear, engaging style, Large Scale Data Handling in Biology is for scientists and students who are learning computational approaches to biology. The book covers the data storage system, computational approaches to biological problems, an introduction to workflow systems, data mining, data visualization, and tips for tailoring existing data analysis software to individual research needs....

    pdf53p tuanloc_do 04-12-2012 59 2   Download

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