Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case
Distributed, Parallel, and Cluster Computing
2017-10-03 v1 Machine Learning
Machine Learning
Abstract
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".
Cite
@article{arxiv.1710.00112,
title = {Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case},
author = {Faraz Faghri and Sayed Hadi Hashemi and Mohammad Babaeizadeh and Mike A. Nalls and Saurabh Sinha and Roy H. Campbell},
journal= {arXiv preprint arXiv:1710.00112},
year = {2017}
}