Challenges in biomarker discovery: combining expert insights with statistical analysis of complex omics data

Abstract

Introduction The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful molecular signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities for more sophisticated approaches to integrating purely statistical and expert knowledge-based approaches. Areas covered In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered in deriving valid and useful signatures of disease. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. Expert opinion Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to identify predictive signatures of disease are key to future success in the biomarker field. We will describe our recommendations for possible approaches to this problem including metrics for the evaluation of biomarkers.

Machine Learning for Health Informatics

“…The big picture of the HCI-KDD approach: The horizontal process chain (blue box) encompasses the whole machine learning pipeline from physical aspects of raw data, to human aspects of data visualization; while the vertical topics (green box) include important aspects of structure (graphs/networks), space (computational topology) and time (entropy); privacy, data protection, safety and security are mandatory topics within the health domain and provide kind of a base compartment (Color figure online) (Image taken from hci-kdd.org) NGS etc. ), microarrays, transcriptomic technologies, proteomic and metabolomic technologies, etc., which all plays important roles for biomarker discovery and drug design [34,35]. (2) Clinical data (e.g.…”

Integrative methods for analyzing big data in precision medicine

“…Since the beginning of the Human Genome Project [9], novel technological developments led to the era of omics sciences. Using novel high-throughput capturing technologies, we are now able to access the DNA of an individual (genetic data), the transcribed RNA over time (expression and co-expression data), proteins (protein profiles and protein interaction data), metabolism (metabolic profiles) and epigenome (DNA methylation data), among other data types [10]. The environment is also taken into account (e.g., nutrition and bacterial environment by nutriomics and metagenomics, respectively) [11,12], and also histopathological and medical imaging data are now subject to high throughput capturing and analysis methods [13][14][15][16].…”

Learning from Our Mistakes: The Future of Validating Complex Diagnostics

“…To prevent such disasters, a number of responses have been proposed. For example, some suggest that test development should use analytes with a plausible link to underlying biology (5 ). Others have advocated increased federal regulation, as exemplified by the recently proposed expansion in active oversight of laboratorydeveloped tests by the US Food and Drug Administration (6 ).…”