@article{
 title = {Special issue for emerging computational methods for the life sciences workshop},
 type = {article},
 year = {2014},
 volume = {26},
 id = {871f0b0b-4561-3683-9609-b1879de58773},
 created = {2017-11-28T17:32:48.497Z},
 file_attached = {false},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2020-05-11T14:43:29.183Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 citation_key = {Qiu2014},
 folder_uuids = {36d8ccf4-7085-47fa-8ab9-897283d082c5},
 private_publication = {false},
 abstract = {The Special Issue of Concurrency and Computation: Practice and Experience 2013 deals with the latest trends in parallel and distributed high-performance systems applied to life science problems. Mitchel and co-researchers present parallel implementations of two popular microarray data analysis techniques, exploratory clustering analyses using the random forest classifier and feature selection through identification of differentially expressed genes using the rank product method. Luo and co-researchers describe an enhanced MapReduce-based programming model 'Map-Reduce-GlobalReduce', where the computations are expressed as three functions: Map, Reduce, and GlobalReduce. Jha and co-researchers present a runtime environment, Distributed Application Runtime Environment (DARE), that supports the scalable, flexible, and extensible composition of capabilities exploring the interoperability among heterogeneously distributed computing environments for pleasingly parallel applications.},
 bibtype = {article},
 author = {Qiu, J. and Foster, I. and Taylor, R.},
 doi = {10.1002/cpe.2998},
 journal = {Concurrency Computation Practice and Experience},
 number = {4}
}

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