Overcoming Selection Bias in Synthetic Lethality Prediction

MotivationSynthetic lethality (SL) between two genes occurs when simultaneous loss of function leads to cell death. This holds great promise for developing anti-cancer therapeutics that target synthetic lethal pairs of endogenously disrupted genes. Identifying novel SL relationships through exhaustive experimental screens is challenging, due to the vast number of candidate pairs. Computational SL prediction is therefore sought to identify promising SL gene pairs for further experimentation. However, current SL prediction methods lack consideration for generalizability in the presence of selection bias in SL data.ResultsWe show that SL data exhibit considerable gene selection bias. Our experiments designed to assess the robustness of SL prediction reveal that models driven by the topology of known SL interactions (e.g. graph, matrix factorization) are especially sensitive to selection bias. We introduce selection bias-resilient synthetic lethality (SBSL) prediction using regularized logistic regression or random forests. Each gene pair is described by 27 molecular features derived from cancer cell line, cancer patient tissue and healthy donor tissue samples. SBSL models are built and tested using approximately 8000 experimentally derived SL pairs across breast, colon, lung and ovarian cancers. Compared to other SL prediction methods, SBSL showed higher predictive performance, better generalizability and robustness to selection bias. Gene dependency, quantifying the essentiality of a gene for cell survival, contributed most to SBSL predictions. Random forests were superior to linear models in the absence of dependency features, highlighting the relevance of mutual exclusivity of somatic mutations, co-expression in healthy tissue and differential expression in tumour samples.Availability and implementationhttps://github.com/joanagoncalveslab/sbslSupplementary informationSupplementary data are available at Bioinformatics online

Overcoming Selection Bias in Synthetic Lethality Prediction

MotivationSynthetic lethality (SL) between two genes occurs when simultaneous loss of function leads to cell death. This holds great promise for developing anti-cancer therapeutics that target synthetic lethal pairs of endogenously disrupted genes. Identifying novel SL relationships through exhaustive experimental screens is challenging, due to the vast number of candidate pairs. Computational SL prediction is therefore sought to identify promising SL gene pairs for further experimentation. However, current SL prediction methods lack consideration for generalizability in the presence of selection bias in SL data.ResultsWe show that SL data exhibit considerable gene selection bias. Our experiments designed to assess the robustness of SL prediction reveal that models driven by the topology of known SL interactions (e.g. graph, matrix factorization) are especially sensitive to selection bias. We introduce selection bias-resilient synthetic lethality (SBSL) prediction using regularized logistic regression or random forests. Each gene pair is described by 27 molecular features derived from cancer cell line, cancer patient tissue and healthy donor tissue samples. SBSL models are built and tested using approximately 8000 experimentally derived SL pairs across breast, colon, lung and ovarian cancers. Compared to other SL prediction methods, SBSL showed higher predictive performance, better generalizability and robustness to selection bias. Gene dependency, quantifying the essentiality of a gene for cell survival, contributed most to SBSL predictions. Random forests were superior to linear models in the absence of dependency features, highlighting the relevance of mutual exclusivity of somatic mutations, co-expression in healthy tissue and differential expression in tumour samples.Availability and implementationhttps://github.com/joanagoncalveslab/sbslSupplementary informationSupplementary data are available at Bioinformatics online.Pattern Recognition and Bioinformatic

Bottom-nets as a new method for quantitatively sampling tadpole populations (Amphibia, Anura)

A new technique to reliably estimate true tadpole abundance in small ephemeral forest ponds based on a sample taken from a bottom net is presented. The bottom net is placed in the pond before it fills with water. At given intervals the bottom net is lifted through the water column and all tadpoles can be counted. Based on a series of calibrations, the technique showed significant correlations between estimated abundance of Osteocephalus taurinus Steindachner, 1862, Colostelhus marchesianus Melin, 1941, Phyllomedusa tomoptema Cope, 1868, and aeshnid larvae, and true abundances based on total counts

Commodification of body parts: by medicine or by media?

Commentators frequently point to the involvement of biomedicine and bioscience in the objectification and commodification of human body parts and the consequent potential for violation of personal, social and community meanings. Through a study of UK media coverage of controversies associated with the removal of body parts and human materials from children, we argue that an exclusive emphasis on the role of medicine and the biosciences in the commodification of human materials ignores the important role played by commercially motivated mass media organisations. Analysis of the language of news reports covering the period of the organ retention controversies in the UK reveals the ways in which the mass media contribute to the commodification of body parts by recruiting them for use in the manufacture of a media scandal. This is achieved through use of horror language, the fetishisation of certain body parts, emphasis on the fragmentation of the body, and the use of a variety of rhetorical devices to convey enormity and massive scale. Media participation in the commodification of children’s body parts has profound implications for practices and policies in relation to use of body parts, and has significantly influenced the governmental regulation of science and medicine. The role of mass media deserves fuller recognition by theorists of body commodification

Permeability of biological and synthetic molecules through dentine

The diffusion through dentine by a number of biological and synthetic molecules, including resins and dyes, is reported. In vitro measurements were derived by experiments with a modified ‘split-chamber device’. Diffusion was found to be indirectly proportional to dentine thickness for all molecules. Permeability of water-soluble molecules and ethanol-soluble molecules was proportional to the molecular weights, except for fluorescein, hydrogen peroxide (H 2 O 2 ) and urethane dimethacrylate. The resin components tested are not soluble enough in an aqueous medium to diffuse through 0.5 mm dentine at sufficient concentrations to cause cytotoxicity to pulpal cells.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71760/1/j.1365-2842.1994.tb01162.x.pd

Exploring the impact of sexual orientation on experiences and concerns about end of life care and on bereavement for lesbian, gay and bisexual older people

This article explores how sexual orientation1 may impact on concerns about, and experiences of, end of life care and bereavement within same-sex relationships. We draw on exploratory data from four focus groups with lesbian and gay elders ( N = 15), which formed part of a larger project investigating a range of older people’s concerns about end of life care. We set the findings in the context of debates about broader changes to family forms within late modernity, alongside social change and demographic shifts. Our focus on end of life care and bereavement sheds light on a series of relatively neglected issues associated with lesbian, gay and bisexual (LGB) ageing and, more broadly, the topics of care and support within ‘non-traditional’ intimate relationships and personal networks. We point to the importance of further research into the lives of older lesbians and gay men facing issues of end of life care and bereavement