Finally, the problems and future directions are highlighted for integrative analysis of omics big data.īayesian Big data Complex diseases High throughput Integration Machine learning Matrix decomposition Omics Precision medicine Subtype. Current integration approaches on biological data have two modes: one is 'bottom-up integration' mode with follow-up manual integration, and the other one is 'top-down integration' mode with follow-up in silico integration.This paper will firstly summarize the combinatory analysis approaches to give candidate protocol on biological experiment design for effectively integrative study on genomics and then survey the data fusion approaches to give helpful instruction on computational model development for biological significance detection, which have also provided newly data resources and analysis tools to support the precision medicine dependent on the big biomedical data. These data sets may come from a variety of sources, such as web, mobile, email, social media, and networked smart devices. Big Data architecture maps the processes necessary to manage Big Data on its journey across four basic layers, from data sources, to data storage, then on to Big Data analysis, and finally through the consumption layer in which the analysed results are presented as business intelligence. Integrative analysis can combine and investigate many datasets in a cost-effective reproducible way. Big data analytics refers to the methods, tools, and applications used to collect, process, and derive insights from varied, high-volume, high-velocity data sets. Several benchmark studies have revealed the heterogeneity and trade-off that existed in the analysis of omics data.

They are opening a new challenge from horizontal data ensemble (e.g., the similar types of data collected from different labs or companies) to vertical data ensemble (e.g., the different types of data collected for a group of person with match information), which requires the integrative analysis in biology and biomedicine and also asks for emergent development of data integration to address the great changes from previous population-guided to newly individual-guided investigations.Data integration is an effective concept to solve the complex problem or understand the complicate system. The diversity and huge omics data take biology and biomedicine research and application into a big data era, just like that popular in human society a decade ago.

Big data analysis