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

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

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

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

Prognostic Usefulness of Serial C-Reactive Protein Measurements in ST-Elevation Acute Myocardial Infarction

It has been reported that increased levels of C-reactive protein are related to adverse long-term prognosis in the setting of ST-segment elevation acute myocardial infarction (MI). In previous studies, the timing of C-reactive protein determination has varied widely. In the present study, serial high-sensitivity C-reactive protein (hsCRP) measurements were performed to investigate if any of the measurements is superior regarding long-term prognosis. A total of 861 consecutive patients admitted for ST-segment elevation MI and treated with intravenous thrombolysis within the first 6 hours from the index pain were included. HsCRP levels were determined at presentation and at 24, 48, and 72 hours. The median follow-up time was 3.5 years. New nonfatal MI and cardiac death were the study end points. By the end of follow-up, cardiac death was observed in 22.4% and nonfatal MI in 16.1% of the patients. HsCRP levels were found to be increasing during the first 72 hours. Multivariate Cox regression analysis demonstrated that hsCRP levels a presentation were an independent predictor of the 2 end points (relative risk [RR] 2.8, p = 0.002, and RR 2.1, p = 0.03, for MI and cardiac death, respectively), while hsCRP levels at 24 hours did not yield statistically significant results (RR 1.4, p = 0.40, and RR 1.1, p = 0.80, for MI and cardiac death, respectively). The corresponding RRs at 48 hours were 1.2 (p = 0.5) for MI and 3.2 (p = 0.007) for cardiac death and at 72 hours were 1.6 (p = 0.30) for MI and 3.9 (p <0.001) for cardiac death. In conclusion, hsCRP levels at presentation represent an independent predictor for fatal and nonfatal events during long-term follow-up. HsCRP levels at 48 and 72 hours, which are close to peak hsCRP levels, independently predict only cardiac death. (c) 2013 Elsevier Inc. All rights reserved. (Am J Cardiol 2013;111:26-30