Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new
disease biomarkers. However, certain critical steps of study design such as cohort selection …

The metabolome, the collection of small-molecule chemical entities involved in metabolism,
has traditionally been studied with the aim of identifying biomarkers in the diagnosis and …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

Multi‐omics studies promise the improved characterization of biological processes across
molecular layers. However, methods for the unsupervised integration of the resulting …

With the rapid adoption of high-throughput omic approaches to analyze biological samples
such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can …

Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing
the goal of precision medicine—tailoring the best treatment for each cancer patient. Recent …

The metabolome of an organism depends on environmental factors and intracellular
regulation and provides information about the physiological conditions. Metabolomics helps …

Recent developments in high-throughput technologies have accelerated the accumulation
of massive amounts of omics data from multiple sources: genome, epigenome …

Mounting evidence has highlighted the implementation of big data handling and
management in the healthcare industry to improve the clinical services. Various private and …

Omic data analysis is steadily growing as a driver of basic and applied molecular biology
research. Core to the interpretation of complex and heterogeneous biological phenotypes …