Tags: Life Sciences

All Categories (1-4 of 4)

  1. BACE gas exchange 2011

    2016-09-07 15:08:51 | Datasets | Contributor(s): Nicholas G. Smith | doi:10.4231/R7PC30B8

    BACE gas exchange data for 2011 season

    https://purr.purdue.edu/publications/2213

  2. PRICLE community composition and soil moisture 2012-2013

    2015-11-30 16:46:31 | Datasets | Contributor(s): Nicholas G. Smith, Michael Schuster, Jeffrey Dukes | doi:10.4231/R7TQ5ZG3

    Plant community and soil moisture data at the Prairie Invasion and Climate Experiment (PRICLE) in 2012 and 2013

    https://purr.purdue.edu/publications/2009

  3. Simplicity of K-means versus deepness of Deep Learning. A Case of Unsupervised Feature Learning with Limited Data

    2015-09-30 20:07:55 | Datasets | Contributor(s): Murat Dundar, Qiang Kou, Baichuan Zhang, Yicheng He, Bartlomiej P. Rajwa | doi:10.4231/R7N58J9Z

    A study contrasting K-means-based unsupervised feature learning and deep learning techniques for small data sets with limited intra- as well as inter-class diversity

    https://purr.purdue.edu/publications/1988

  4. A Non-parametric Bayesian Model for Joint Cell Clustering and Cluster Matching: Identification of Anomalous Sample Phenotypes with Random Effects.

    2014-09-02 15:30:44 | Datasets | Contributor(s): Murat Dundar, Ferit Akova, Halid Ziya Yerebakan, Bartlomiej P. Rajwa | doi:10.4231/R7KK98PG

    The manuscript presents a non-parametric Bayesian algorithm called ASPIRE (Anomalous Sample Phenotype Identification with Random Effects) able to identify phenotypic differences across batches of cytometry samples in the presence of random effects

    https://purr.purdue.edu/publications/1712

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