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If hot spots for different taxa coincide, priority-setting surveys in a region could be carried out more cheaply by focusing on indicator taxa. Several previous studies show that hot spots of different taxa rarely coincide. However, in tropical areas indicator taxa may be used in selecting complementary networks to represent biodiversity as a whole. We studied beetles (Coleoptera), Heteroptera, polypores or bracket fungi (Polyporaceae) and vascular plants of old growth boreal taiga forests. Optimal networks for Heteroptera maximized the high overall species richness of beetles and vascular plants, but these networks were least favourable options for polypores. Polypores are an important group indicating the conservation value of old growth taiga forests. Random selection provided a better option. Thus, certain groups may function as good indicators for maximizing the overall species richness of some taxonomic groups, but all taxa should be examined separately.

The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug’s MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).

Background: Evidence-informed decision-making and better use of scientific information in societal decisions has been an area of development for decades but is still topical. Decision support work can be viewed from the perspective of information collection, synthesis and flow between decision-makers, experts and stakeholders. Open policy practice is a coherent set of methods for such work. It has been developed and utilised mostly in Finnish and European contexts. Methods: An overview of open policy practice is given, and theoretical and practical properties are evaluated based on properties of good policy support. The evaluation is based on information from several assessments and research projects developing and applying open policy practice and the authors' practical experiences. The methods are evaluated against their capability of producing quality of content, applicability and efficiency in policy support as well as how well they support close interaction among participants and understanding of each other's views. Results: The evaluation revealed that methods and online tools work as expected, as demonstrated by the assessments and policy support processes conducted. The approach improves the availability of information and especially of relevant details. Experts are ambivalent about the acceptability of opennessit is an important scientific principle, but it goes against many current research and decision-making practices. However, co-creation and openness are megatrends that are changing science, decision-making and the society at large. Against many experts' fears, open participation has not caused problems in performing high-quality assessments. On the contrary, a key challenge is to motivate and help more experts, decision-makers and citizens to participate and share their views. Many methods within open policy practice have also been widely used in other contexts. Conclusions: Open policy practice proved to be a useful and coherent set of methods. It guided policy processes toward a more collaborative approach, whose purpose was wider understanding rather than winning a debate. There is potential for merging open policy practice with other open science and open decision process tools. Active facilitation, community building and improving the user-friendliness of the tools were identified as key solutions for improving the usability of the method in the future.