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Several multiple comparison methods have been proposed for large scale data set to deal with the situations where the variables (loci) are not independent [ 24- 28].
Future work includes extending the framework to deal with large-scale data sets, to incorporate novel data analytics techniques specifically defined for tasks such as user sentiment analysis and to incorporate novel IoT technologies.
Similar to the massively parallel signature sequencing (MPSS) approach [ 19], the DGE tag-based approach provides large-scale data sets to establish a complete quantitative and qualitative analysis of cell transcriptome without prior knowledge of genome annotation [ 20].
We used large-scale data sets to study whether there was relationship between flight and intron size by comparing intron sizes in flying species and nonflying sister lineages in both mammals and birds.
This creates a tension between the need for the scientific community to have access to large scale data sets of sequence and the need to preserve confidentiality of all patients taking part in a study.
How to integrate large scale data sets from various sources is a key question that needs to be addressed in the current big data scenario.
Previous work has demonstrated the utility of exploring the reverse causal interpretation of large scale data sets using HYPs as opposed to reasoning downstream from the data [ 12- 14].
These machine learning methods are recently proposed, claimed to facilitate large scale data sets, and their packages are online freely available [ 7].
Moreover, we wish to identify genes that are important for the agricultural research community and provide GO annotation to support further functional modeling of large scale data sets.
Google's technology infrastructure, is optimized for large scale data sets and not rapid iteration.
The framework is implemented as an efficient R extension package BHTSpack and is suitable for large scale data sets.
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