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

On Zero inflated models with applications to maternal healthcare utilization

We consider the problem of modelling count data with excess zeros and over-dispersion which are commonly encountered in various disciplines  that limit the use of traditional models for count outcomes. Our research work applies the Zero-inflated Poisson and Negative Binomial models in  modelling Maternal Health Care (MHC) utilization in Nigeria, employing the Andersen’s behavioural model to examine the effect of predisposing,  enabling, and need factors on MHC utilization. The performance of these models are compared to the traditional Poisson and negative binomial  models. The Vuong test and AIC suggests that the Zero-inflated Negative Binomial model provided the most significant improvement over traditional  models for count outcomes.&nbsp