Biology subjects

Flynn, E.

Publications and source records attributed to Flynn, E..

2 recordsLinked to original sources

bioRxiv2019

Risks to pollinators from different land-use transitions: bee species’ responses to agricultural expansion show strong phylogenetic signal

Bee species worldwide are facing a future of further land-use change and intensification. Populations of closely-related species with similar ecological characteristics are likely to respond similarly to such pressures. Such phylogenetic signal in species responses could undermine the stability of pollination services in agricultural and natural systems. We use abundance data from a global compilation of bee assemblages in different land uses to assess the sensitivity of 573 bee species to agricultural expansion, intensification and urbanization; and combine the results with the Bee Tree of Life to assess phylogenetic signal. In addition, we assess whether variation in species sensitivity to land-use change is better explained by phylogenetic or available functional trait differences. Bee species show strong phylogenetic signal in sensitivity to agricultural land expansion but only a weak signal in sensitivity to agricultural intensification and urbanisation. Sensitivities were usually best explained by a combination of functional and phylogenetic distances. This finding suggests that the commonly-recorded traits, despite being meaningful as functional response traits, do not capture all important determinants of bee species vulnerability or resistance. However, it also suggests that model-based predictions of the sensitivity of poorly known species may be sufficient to help guide conservation efforts.

ecology↗

bioRxiv2019

GRep: Gene Set Representation via Gaussian Embedding

Molecular interaction networks are our basis for understanding functional interdependencies among genes. Network embedding approaches analyze these complicated networks by representing genes as low-dimensional vectors based on the network topology. These low-dimensional vectors have recently become the building blocks for a larger number of systems biology applications. Despite the success of embedding genes in this way, it remains unclear how to effectively represent gene sets, such as protein complexes and signaling pathways. The direct adaptation of existing gene embedding approaches to gene sets cannot model the diverse functions of genes in a set. Here, we propose GRep, a novel gene set embedding approach, which represents each gene set as a multivariate Gaussian distribution rather than a single point in the low-dimensional space. The diversity of genes in a set, or the uncertainty of their contribution to a particular function, is modeled by the covariance matrix of the multivariate Gaussian distribution. By doing so, GRep produces a highly informative and compact gene set representation. Using our representation, we analyze two major pharmacogenomics studies and observe substantial improvement in drug target identification from expression-derived gene sets. Overall, the GRep framework provides a novel representation of gene sets that can be used as input features to off-the-shelf machine learning classifiers for gene set analysis.

bioinformatics↗