Blagoj Ristevski , Snezana Savoska and Pece Mitrevski


In this paper we describe various biological omics data (e.g. ge-nomics, epigenomics, transcriptomics, proteomics, metabolomics and microbi-omics) generated using high-throughput sequencing technologies. These omics data are generated in huge amounts and they follow the 6 V’s properties of big data. To discover hidden knowledge from this big omics data, complex network analysis is used. Biological complex networks such as gene regulatory and pro-tein-protein interaction networks are appreciated resources for discovering dis-ease genes and pathways, to investigate topological properties of the most im-portant genes associated with particular disease. The inference of biological regulatory networks from different high dimensional omics data is a fundamen-tal and very complex task in bioinformatics. Various big omics data have a great potential to uncover diverse perspectives of biological complex networks. Taking into account the properties of the big omics data, we suggest some di-rection for further works.


Complex Networks, Big Data, Omics Data, Biological Networks