Machine Learning

Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case

This topic contains 0 replies, has 1 voice, and was last updated by  arXiv 2 years, 1 month ago.


  • arXiv
    5 pts

    Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case

    In an effort to overcome the data deluge in computational biology and bioinformatics and to facilitate bioinformatics research in the era of big data, we identify some of the most influential algorithms that have been widely used in the bioinformatics community. These top data mining and machine learning algorithms cover classification, clustering, regression, graphical model-based learning, and dimensionality reduction. The goal of this study is to guide the focus of scalable computing experts in the endeavor of applying new storage and scalable computation designs to bioinformatics algorithms that merit their attention most, following the engineering maxim of “optimize the common case”.

    Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case
    by Faraz Faghri, Sayed Hadi Hashemi, Mohammad Babaeizadeh, Mike A. Nalls, Saurabh Sinha, Roy H. Campbell
    https://arxiv.org/pdf/1710.00112v1.pdf

You must be logged in to reply to this topic.