22 research outputs found

    Inferring causal molecular networks: empirical assessment through a community-based effort

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    It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

    Inferring causal molecular networks: empirical assessment through a community-based effort

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    Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

    Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

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    Correction: vol 7, 13205, 2016, doi:10.1038/ncomms13205Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in Bone-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2) = 0.18, P value = 0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.Peer reviewe

    Oncologists and hematologists' perceptions of fertility-related communication - a nationwide survey

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    BACKGROUND: Despite the negative impacts of several cancer treatments on fertility, many patients do not recall having fertility-related discussions with their physicians. This study was conducted to identify those factors related to physicians' discussing the treatment impacts on fertility with cancer patients of reproductive age. MATERIAL AND METHODS: In this nationwide survey of cancer care physicians (n = 329, response rate 55%), oncologists and hematologists (mainly) completed a questionnaire on practice behavior, barriers, attitudes and confidence in knowledge regarding treatment-related fertility risks. Logistic regression analyses were conducted to identify factors associated with not routinely discussing fertility issues with patients. RESULTS: Most of the physicians agreed that they were responsible for discussing fertility issues with patients of reproductive age (91%), but approximately 30% did not do so regularly. Those factors decreasing the likelihood of discussion were: patient already had children (female/male OR 3.0/6.9), high workload (OR 3.3/4.8), seeing <5 female/male patients of reproductive age weekly (OR 3.2/3.4) and access to a reproduction clinic (OR 5.2/4.2). CONCLUSIONS: Most Swedish oncologists and hematologists regularly discuss impact of treatment on fertility with their patients. Those factors having a negative impact on fertility discussions may guide targeted organizational and educational efforts to further improve fertility-related communication in cancer care
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