Representing and querying disease networks using graph databases

BACKGROUND: Systems biology experiments generate large volumes of data of multiple modalities and this information presents a challenge for integration due to a mix of complexity together with rich semantics. Here, we describe how graph databases provide a powerful framework for storage, querying and envisioning of biological data. RESULTS: We show how graph databases are well suited for the representation of biological information, which is typically highly connected, semi-structured and unpredictable. We outline an application case that uses the Neo4j graph database for building and querying a prototype network to provide biological context to asthma related genes. CONCLUSIONS: Our study suggests that graph databases provide a flexible solution for the integration of multiple types of biological data and facilitate exploratory data mining to support hypothesis generation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13040-016-0102-8) contains supplementary material, which is available to authorized users

Ten years of Genome Medicine.

This year marks the 10th anniversary of Genome Medicine. The journal was launched to meet the need in the community for a platform to publish impactful and open science that advances basic and clinical research—using genetic, genomic, omic, and systems approaches—that has the potential to revolutionize the practice of medicine. We have seen the journal evolve along with the changing landscape of health and disease, including the increasing use of genome-scale approaches in medical research and clinical practice, the generation and analysis of patient- and population-level data, and the clinical implementation of these approaches in precision medicine and public health. Genome Medicine, guided by our renowned Section Editors, continues to serve an ever-growing community of interdisciplinary researchers. Here, our Section Editors discuss the major advances in the field and their applications in genomic medicine during the past decade

Integrative systems medicine approaches to identify molecular targets in lymphoid malignancies

Although survival rates for lymphoproliferative disorders are steadily increasing both in the US and in Europe, there is need for optimizing front-line therapies and developing more effective salvage strategies. Recent advances in molecular genetics have highlighted the biological diversity of lymphoproliferative disorders. In particular, integrative approaches including whole genome sequencing, whole exome sequencing, and transcriptome or RNA sequencing have been instrumental to the identification of molecular targets for treatment. Herein, we will discuss how genomic, epigenomic and proteomic approaches in lymphoproliferative disorders have supported the discovery of molecular lesions and their therapeutic targeting in the clinic

Ce-doped LuAG single-crystal fibers grown from the melt for high-energy physics

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Clinical trials in children: Equity, quality and relevance

This thesis investigates the equity, quality and relevance of clinical trials in children to inform better evidence-based child healthcare and outcomes worldwide. A comprehensive review of the literature revealed that despite current initiatives to encourage more trials in children, there is still a paucity of safety and efficacy data of many medicines prescribed in this population. An analysis of trials registered in children showed that disease burden was moderately correlated to trials and this scarcity was particularly prevalent in low-and middle-income countries. We explored the contributory factors to this inequity by conducting a systematic review of stakeholders’ views of trials in children in low-and middle-income countries. In the study evaluating the completeness of protocols of trials in children submitted to ethics committees, we found that protocols are generally comprehensive, but many key domains in trial design and conduct are not reported. Key-informant trial stakeholders who were interviewed proposed strategies to improve trials such as addressing the unique needs of children, embedding trials as part of routine clinical care and streamlining regulatory approvals. Increasing international collaboration, establishing sustainable centralised trials infrastructure, and aligning research to child health priorities were proposed to encourage more high-quality trials that address global child healthcare needs

Location of the Energy Levels of the Rare-Earth Ion in BaF2 and CdF2

The location of the energy levels of rare-earth (RE) elements in the energy band diagram of BaF2 and CdF2 crystals is determined. The role of RE3+ and RE2+ ions in the capture of charge carriers, luminescence, and the formation of radiation defects is evaluated. It is shown that the substantial difference in the luminescence properties of BaF2:RE and CdF2:RE is associated with the location of the excited energy levels in the band diagram of the crystals

A transcriptome-driven analysis of epithelial brushings and bronchial biopsies to define asthma phenotypes in U-BIOPRED

RATIONALE AND OBJECTIVES: Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms. We used transcriptomic profiling of airway tissues to help define asthma phenotypes. METHODS: The transcriptome from bronchial biopsies and epithelial brushings of 107 moderate-to-severe asthmatics were annotated by gene-set variation analysis (GSVA) using 42 gene-signatures relevant to asthma, inflammation and immune function. Topological data analysis (TDA) of clinical and histological data was used to derive clusters and the nearest shrunken centroid algorithm used for signature refinement. RESULTS: 9 GSVA signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper type 2 (Th-2) cytokines and lack of corticosteroid response (Group 1 and Group 3). Group 1 had the highest submucosal eosinophils, high exhaled nitric oxide (FeNO) levels, exacerbation rates and oral corticosteroid (OCS) use whilst Group 3 patients showed the highest levels of sputum eosinophils and had a high BMI. In contrast, Group 2 and Group 4 patients had an 86% and 64% probability of having non-eosinophilic inflammation. Using machine-learning tools, we describe an inference scheme using the currently-available inflammatory biomarkers sputum eosinophilia and exhaled nitric oxide levels along with OCS use that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity. CONCLUSION: This analysis demonstrates the usefulness of a transcriptomic-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target Th2-mediated inflammation and/or corticosteroid insensitivity