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Table 3 Summary of reviewed research in clinical big data analysis using the MapReduce programming model

From: Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends

Study category Study Name/Reference Study year Technology used Application
Public database A drug-adverse event extraction algorithm to support pharmacovigilance knowledge mining from PubMed citations/[30] 2011 A MapReduce based algorithm for common adverse drug events (ADE) detection Biomedical data mining
Identifying unproven cancer treatments on the health web: Addressing accuracy, generalizability and scalability/[31] 2012 Using MapReduce and Markove boundary feature selection Identify unproven cancer treatments on the health web
A user-friendly tool to transform large scale administrative data into wide table format using a MapReduce program with a pig latin based script/[33] 2012 MapRedcue and Pig Latin Administrative data management
Biometric Leveraging the cloud for big data biometrics: Meeting the performance requirements of the next generation biometric systems/[34] 2011 MapReduce machine learning algorithms for image regnition on Hadoop paltform Design of secuirty system using biometric identification
Iris recognition on hadoop: A biometrics system implementation on cloud computing/[35] 2011 Human iris MapReduce search algorithm on the cloud Data retrival and secuirty system
Cloud-ready biometric system for mobile security access/[36] 2012 MapReduce algorithm to capture and recognition of biometric information Biometric-identification mobile phone applications
Genome and Protein data analysis Parallelizing bioinformatics applications with MapReduce/[54] 2008 MapRedcue algorithms Bioinformatics applications
Cloudblast: Combining MapReduce and virtualization on distributed resources for bioinformatics applications/[55] 2008 Cloud/MapReduce Bioinformatics applications
CloudBurst: highly sensitive read mapping with MapReduce/[50] 2009 MapRedcue algorithms Genome sequence mapping tool
Cloud technologies for bioinformatics applications/[53] 2009 Cloud/MapReduce Bioinformatics applications
The genome analysis toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data/[44] 2010 HBase for data management and MapReduce jobs for computation Genome sequence comparison application
Nephele: genotyping via complete composition vectors and MapReduce/[64] 2011 MapReduce Algorithms Genotyping sequence tool
A graphical execution platform for MapReduce programs on private and public clouds/[59] 2012 Cloud/MapReduce Bioinformatics applications
Hydra: a scalable proteomic search engine which utilizes the Hadoop distributed computing framework/[60] 2012 MapReduce Algorithms Bioinformatics applications
An efficient algorithm for DNA fragment assembly in MapReduce/[48] 2012 MapReduce algorithm for DNA framentation A tool for DNA fragmentation assembly
De novo assembly of high-throughput sequencing data with cloud computing and new operations on string graphs/[43] 2012 String graph based on the MapReduce algorithms Distributed Genome assembler
Fractal MapReduce decomposition of sequence alignment/[63] 2012 MapReduce Algorithms Genome sequence alignment tool
Genotyping in the cloud with crossbow/[70] 2012 Cloud Genotyping application
BioPig: A hadoop-based analytic toolkit for large-scale sequence data [40] 2013 MapReduce algorithms Bioinformatics processing tool known as BioPig
Implementation of a parallel protein structure alignment service on cloud/[46] 2013 MapReduce alignment algorithm Protein alignment application
BlueSNP: R package for highly scalable genome-wide association studies using hadoop clusters/[47] 2013 R alagorithms executed on top of the Hadoop platform Statistical package in R for Genome analysis
Enhancement of accuracy and efficiency for RNA secondary structure prediction by sequence segmentation and MapReduce/[68] 2013 MapReduce algorithms Enhanced algorithm
Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing/[69] 2013 Cloud Whole-genome sequencing
Study Category Study Name/Reference Study year Technology used Application
Genome and Protein data analysis Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes/[62] 2013 MapReduce Algorithms multivariate neuroimaging phenotypes
Novel and efficient tag SNPs selection algorithms/[37] 2014 MapReduce algorithm for efficient selection of SNP Genom analysis
Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment/[66] 2014 Cloud Algorithm for inferring gene networks
Launching genomics into the cloud: deployment of Mercury, a next generation sequence analysis pipeline/[71] 2014 Cloud sequence analysis application
Biomedical signal analysis HBase, MapReduce, and integrated data visualization for processing clinical signal data/[39] 2011 HBase for data mangement and MapReduce processing algorithm Store and processing clinical signals
Parallel processing of massive EEG data with MapReduce/[73] 2012 MapReduce EEMD algorithm Massive biomedical signal processing
Biomedical image analysis Hadoop-gis: A high performance query system for analytical medical imaging with MapReduce/[74] 2011 HBase for data management and MapReduce processing algorithm Store and processing of medical images
Ultrafast and scalable cone-beam CT reconstruction using MapReduce in a cloud computing environment [76] 2011 MapReduce image processing algorithms on the Cloud Accelerates FDK algorithm for the cone-beam CT
Using MapReduce for Large-Scale Medical Image Analysis/[75] 2012 MapReduce algorithm Medical Image Analysis
  1. The summary includes information related to the study (i.e. category, name, year, technology used, experiment design and potetial applications).