A machine learning pipeline for discriminant pathways identification

Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Results: In this work we propose a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. The proposal is independent from the classification algorithm used. Three applications on genomewide data are presented regarding children susceptibility to air pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's. Availability: Details about the software used for the experiments discussed in this paper are provided in the Appendix

Multi-Level Assessment Protocol (MAP) for Adoption in Multisite Clinical Trials

The National Institute on Drug Abuse (NIDA) Clinical Trials Network (CTN) is intended to test promising drug abuse treatment models in multisite clinical trials and to support adoption of new interventions into clinical practice. Using qualitative research methods we asked the following question: how might the technology of multisite clinical trials be modified to better support adoption of tested interventions? A total of 42 participants, representing eight organizational levels ranging from clinic staff to clinical trial leaders, were interviewed about their role in the clinical trial, its interactions with clinics, and intervention adoption. Among eight clinics participating in the clinical trial, we found adoption of the tested intervention in one clinic only. Analysis of interview data revealed four conceptual themes likely to affect adoption and may be informative in future multisite clinical trials. Planning for adoption in the early stages of protocol development will better serve the aim of integrating new interventions into practice

The role of red processed meat in colorectal cancer development a perspective

This paper is based on a workshop held in Oslo, Norway in November 2013, in which experts discussed how to reach consensus on the healthiness of red and processed meat. Recent nutritional recommendations include reducing intake of red and processed meat to reduce cancer risk, in particular colorectal cancer (CRC). Epidemiological and mechanistic data on associations between red and processed meat intake and CRC are inconsistent and underlying mechanisms are unclear. There is a need for further studies on differences between white and red meat, between processed and whole red meat and between different types of processed meats, as potential health risks may not be the same for all products. Better biomarkers of meat intake and of cancer occurrence and updated food composition databases are required for future studies. Modifying meat composition via animal feeding and breeding, improving meat processing by alternative methods such as adding phytochemicals and improving our diets in general are strategies that need to be followed up