ABSTRACT
The advent of omics-based technologies has enhanced the ability to gain specific insights into the molecular pathogenesis of human diseases. The vast amounts of patient-specific data produced by these technologies have been used to augment disease understanding and generate hypotheses around novel diagnostic and prognostic disease biomarkers. Integration of genomic, tissue-based, and clinical information presents promising opportunities to better identify biomarkers of disease detection, extract molecular and/or cellular patient subsets, and develop a potential prognosis and/or predictive biomarkers of therapeutic treatment response. Transcriptomic analysis of histologically normal tissue has been shown in many instances to be altered in individuals who eventually go on to develop diseases, including cancers. Transcriptomics allows the assessment of expression changes across a large set of genes, while tissue phenomics elucidates cell-to-cell interactions within biopsy specimens and measures distributions to quantify specific cell types. The art of image mining combines the both relevant and feasible analyses with appropriate algorithms and complex image processing/analysis with clinical outcomes.
