Going -omics to identify novel therapeutic targets for cardiovascular disease

Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration

“…Combining these various types of multi-omics datasets and clinical data could have the potential to enhance a comprehensive view of the mechanisms of disease or biological process [3][4][5]. Understanding molecular behaviors, pathway interactions and relationships between and within the different types of data could improve the diagnosis, prognosis and monitoring therapy treatment of cancer [6]. For instance, The Cancer Genome Atlas (TCGA) [7] and the International Cancer Genome Consortium [8] produced and collected thousands of tumor samples at different molecular levels including DNA (somatic mutation, copy number variation), DNA methylation, RNA (or microRNA) and also proteins for the same patients [9].…”

“…For instance, The Cancer Genome Atlas (TCGA) [7] and the International Cancer Genome Consortium [8] produced and collected thousands of tumor samples at different molecular levels including DNA (somatic mutation, copy number variation), DNA methylation, RNA (or microRNA) and also proteins for the same patients [9]. Although cancer is the main application that uses multi-omic integration, the analysis of complex diseases or single-cell is now emerging [6,[10][11][12][13][14]. Metabolomic and lipidomic were also combined with genomic for a better knowledge of phenotypes [13,[15][16][17].…”

Understanding the molecular mechanisms regulating the early placental development using Next-Generation Sequencing (NGS) datasets

“…During the last decade, there has been an exponential growth in the usage of NGS techniques due to the drop in the sequencing costs and development of better analysis methods (cos, 2020). These changes have enabled researchers to comprehensively study genome-wide regulatory mechanisms by collecting multi-omics datasets, multiple types of data observed on the same sampled individuals (Chakraborty et al, 2018) (Harber et al, 2019). There have been various statistical approaches developed for integrating multi-omics datasets while dealing with several challenges, including the multiple datasets, the high dimension of the datasets, the small sample size, and interactions between different omics datasets (Meng et al, 2016b.…”