We discuss current approaches for integrative analysis involving different types of omics data and indicate some relevant projects and future challenges in this field.
This paper argues that technography is a useful methodological approach for the integrative study of social technical configurations.
Here we propose a novel unsupervised approach for the integrative modelling of multiple datasets, which may be of different types.
Although experimental validation is required to determine the suitability of such an approach for the discovery of novel drug targets, our computational evaluation shows that, at least for some protein families, our approach is highly successful (up to ROCAUC), and therefore may prove promising for integrative approaches toward computational drug repurposing.
Despite this, the need for integrative approaches is increasingly being recognised [ 11– 13], particularly given the limitations of current distributional data for inferring historical or ecological processes [ 14, 15].
This comparison highlights some of the limitations associated with statistical analyses that rely on a single source of evidence to derive conclusions on biological function, and argues for the need for integrative approaches to data analysis.
Mathias Schwab (University of Tübingen and IKP, Stuttgart, Germany) discussed the individualization of thiopurine methyltransferase therapy (used to treat cancer) and stressed the need for integrative approaches that will include microarrays, genome-wide association data, systems biology and regulatory epigenetics.
The nematode Pristionchus pacificus is a model organism increasingly used for integrative approaches in evolution biology, through interdisciplinary studies in evo-devo, population genetics, and ecology (Hong and Sommer 2006; Sommer 2009).
Despite the large increase of transcriptomic studies that look for gene signatures on diseases, there is still a need for integrative approaches that obtain separation of multiple pathological states providing robust selection of gene markers for each disease subtype and information about the possible links or relations between those genes.
In the last three decades, a number of social cognitive theories have been developed to understand the determinants of health behaviours, among which the Theory of Planned Behaviour [ 18], Social Cognitive Theory [ 19], and Protection Motivation Theory [ 20] are the most commonly used frameworks to explain health behaviour (for integrative approaches, see [ 21, 22].
