Computational approaches for network-based integrative multi-omics analysis

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

Warfarin Pharmacogenomics for Precision Medicine in Real-Life Clinical Practice in Southern Africa: Harnessing 73 Variants in 29 Pharmacogenes.

Pharmacogenomics is universally relevant for worldwide modern therapeutics and yet needs further development in resource-limited countries. While there is an abundance of genetic association studies in controlled medical settings, there is a paucity of studies with a naturalistic design in real-life clinical practice in patients with comorbidities and under multiple drug treatment regimens. African patients are often burdened with communicable and noncommunicable comorbidities, yet the application of pharmacogenomics in African clinical settings remains limited. Using warfarin as a model, this study aims at minimizing gaps in precision/personalized medicine research in African clinical practice. We present, therefore, pharmacogenomic profiles of a cohort of 503 black Africans (n = 252) and Mixed Ancestry (n = 251) patients from Southern Africa, on warfarin and co-prescribed drugs in a naturalized noncontrolled environment. Seventy-three (n = 73) single nucleotide polymorphisms (SNPs) in 29 pharmacogenes were characterized using a combination of allelic discrimination, Sanger sequencing, restriction fragment length polymorphism, and Sequenom Mass Array. The common comorbidities were hypertension (43-46%), heart failure (39-45%), diabetes mellitus (18%), arrhythmia (25%), and HIV infection (15%). Accordingly, the most common co-prescribed drugs were antihypertensives, antiarrhythmic drugs, antidiabetics, and antiretroviral therapy. We observed marked variation in major pharmacogenes both at interethnic levels and within African subpopulations. The Mixed Ancestry group presented a profile of genetic variants reflecting their European, Asian, and African admixture. Precision medicine requires that African populations begin to capture their own pharmacogenetic SNPs as they cannot always infer with absolute certainty from Asian and European populations. In the current historical moment of the COVID-19 pandemic, we also underscore that the spectrum of drugs interacting with warfarin will likely increase, given the systemic and cardiovascular effects of COVID-19, and the anticipated influx of COVID-19 medicines in the near future. This observational clinical pharmacogenomics study of warfarin, together with past precision medicine research, collectively, lends strong support for incorporation of pharmacogenetic profiling in clinical settings in African patients for effective and safe administration of therapeutics.

Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology.

Artificial intelligence (AI) is one of the key drivers of digital health. Digital health and AI applications in medicine and biology are emerging worldwide, not only in resource-rich but also resource-limited regions. AI predates to the mid-20th century, but the current wave of AI builds in part on machine learning (ML), big data, and algorithms that can learn from massive amounts of online user data from patients or healthy persons. There are lessons to be learned from AI applications in different medical specialties and across developed and resource-limited contexts. A case in point is congenital heart defects (CHDs) that continue to plague sub-Saharan Africa, which calls for innovative approaches to improve risk prediction and performance of the available diagnostics. Beyond CHDs, AI in cardiology is a promising context as well. The current suite of digital health applications in CHD and cardiology include complementary technologies such as neural networks, ML, natural language processing and deep learning, not to mention embedded digital sensors. Algorithms that build on these advances are beginning to complement traditional medical expertise while inviting us to redefine the concepts and definitions of expertise in molecular diagnostics and precision medicine. We examine and share here the lessons learned in current attempts to implement AI and digital health in CHD for precision risk prediction and diagnosis in resource-limited settings. These top 10 lessons on AI and digital health summarized in this expert review are relevant broadly beyond CHD in cardiology and medical innovations. As with AI itself that calls for systems approaches to data capture, analysis, and interpretation, both developed and developing countries can usefully learn from their respective experiences as digital health continues to evolve worldwide.