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Organizing and accessing biomedical big data will require quite different business models, say Philip E. Bourne, Jon R. Lorsch and Eric D. Green.

Federal law requires that the results of many clinical trials be publicly reported at ClinicalTrials.gov in a timely manner. A review of clinical trials revealed generally poor compliance with the law, although industry-funded trials fared better than others. The human experimentation that is conducted in clinical trials creates ethical obligations to make research findings publicly available. However, there are numerous historical examples of potentially harmful data being withheld from public scrutiny and selective publication of trial results. 1 – 3 In 2000, Congress authorized the creation of the ClinicalTrials.gov registry to provide information about and access to clinical trials for persons with serious medical conditions. In 2007, Section 801 of the Food and Drug Administration Amendments Act (FDAAA) expanded this mandate by requiring sponsors of applicable clinical trials to register and report basic summary results at ClinicalTrials.gov. 4 Such trials generally . . .

Most researchers agree that open access to data is the scientific ideal, so what is stopping it happening? Bryn Nelson investigates why many researchers choose not to share.

Background High-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume of data. Researchers often rely on functional enrichment tools to interpret the biological significance of the affected genes from these high-throughput studies. However, currently available functional enrichment tools need to be updated frequently to adapt to new entries from the functional database repositories. Hence there is a need for a simplified tool that can perform functional enrichment analysis by using updated information directly from the source databases such as KEGG, Reactome or Gene Ontology etc. Results In this study, we focused on designing a command-line tool called GeneSCF (Gene Set Clustering based on Functional annotations), that can predict the functionally relevant biological information for a set of genes in a real-time updated manner. It is designed to handle information from more than 4000 organisms from freely available prominent functional databases like KEGG, Reactome and Gene Ontology. We successfully employed our tool on two of published datasets to predict the biologically relevant functional information. The core features of this tool were tested on Linux machines without the need for installation of more dependencies. Conclusions GeneSCF is more reliable compared to other enrichment tools because of its ability to use reference functional databases in real-time to perform enrichment analysis. It is an easy-to-integrate tool with other pipelines available for downstream analysis of high-throughput data. More importantly, GeneSCF can run multiple gene lists simultaneously on different organisms thereby saving time for the users. Since the tool is designed to be ready-to-use, there is no need for any complex compilation and installation procedures.

Objectives (i) to demonstrate a method which ameliorates the problem of self-selection in the estimation of population norms from web-based data and (ii) to use the method to calculate population norms for two multi-attribute utility (MAU) instruments, the AQoL-6D and AQoL-8D, and population norms for the sub-scales from which they are constructed. Methods A web-based survey administered the AQoL-8D MAU instrument (which subsumes the AQoL-6D questionnaire), to members of the public along with the AQoL-4D which has extant population norms. Age, gender and the AQoL-4D were used as post-stratification auxiliary variables to construct weights to ameliorate the potential effects of self-selection associated with web-based surveys. The weights were used to estimate unbiased population norms. Standard errors from the weighted samples were calculated using Jackknife estimation. Results For both AQoL-6D and AQoL-8D, physical health dimensions decline significantly with age. In contrast, for the majority of the psycho-social dimensions there is a significant U-shaped profile. The net effect is a shallow U-shaped relationship between age and both the AQoL-6D and AQoL-8D utilities. This contrasts with the almost monotonie decline in the utilities derived from the AQoL-4D and SF-6D MAU instruments. Conclusions Post-stratification weights were used to ameliorate potential bias in the derivation of norms from web-based data for the AQoL-6D and AQoL-8D. The methods may be used generally to obtain norms when suitable auxiliary variables are available. The inclusion of an enlarged psycho-social component in the two instruments significantly alters the demographic profile.

Background Vast quantities of electronic data are collected about patients and service users as they pass through health service and other public sector organisations, and these data present enormous potential for research and policy evaluation. The Health Information Research Unit (HIRU) aims to realise the potential of electronically-held, person-based, routinely-collected data to conduct and support health-related studies. However, there are considerable challenges that must be addressed before such data can be used for these purposes, to ensure compliance with the legislation and guidelines generally known as Information Governance. Methods A set of objectives was identified to address the challenges and establish the Secure Anonymised Information Linkage (SAIL) system in accordance with Information Governance. These were to: 1) ensure data transportation is secure; 2) operate a reliable record matching technique to enable accurate record linkage across datasets; 3) anonymise and encrypt the data to prevent re-identification of individuals; 4) apply measures to address disclosure risk in data views created for researchers; 5) ensure data access is controlled and authorised; 6) establish methods for scrutinising proposals for data utilisation and approving output; and 7) gain external verification of compliance with Information Governance. Results The SAIL databank has been established and it operates on a DB2 platform (Data Warehouse Edition on AIX) running on an IBM 'P' series Supercomputer: Blue-C. The findings of an independent internal audit were favourable and concluded that the systems in place provide adequate assurance of compliance with Information Governance. This expanding databank already holds over 500 million anonymised and encrypted individual-level records from a range of sources relevant to health and well-being. This includes national datasets covering the whole of Wales (approximately 3 million population) and local provider-level datasets, with further growth in progress. The utility of the databank is demonstrated by increasing engagement in high quality research studies. Conclusion Through the pragmatic approach that has been adopted, we have been able to address the key challenges in establishing a national databank of anonymised person-based records, so that the data are available for research and evaluation whilst meeting the requirements of Information Governance.

Background Drug repositioning is a cost-efficient and time-saving process to drug development compared to traditional techniques. A systematic method to drug repositioning is to identify candidate drug's gene expression profiles on target disease models and determine how similar these profiles are to approved drugs. Databases such as the CMAP have been developed recently to help with systematic drug repositioning. Methods To overcome the limitation of connectivity maps on data coverage, we constructed a comprehensive in silico drug-protein connectivity map called DMAP, which contains directed drug-to-protein effects and effect scores. The drug-to-protein effect scores are compiled from all database entries between the drug and protein have been previously observed and provide a confidence measure on the quality of such drug-to-protein effects. Results In DMAP, we have compiled the direct effects between 24,121 PubChem Compound ID (CID), which were mapped from 289,571 chemical entities recognized from public literature, and 5,196 reviewed Uniprot proteins. DMAP compiles a total of 438,004 chemical-to-protein effect relationships. Compared to CMAP, DMAP shows an increase of 221 folds in the number of chemicals and 1.92 fold in the number of ATC codes. Furthermore, by overlapping DMAP chemicals with the approved drugs with known indications from the TTD database and literature, we obtained 982 drugs and 622 diseases; meanwhile, we only obtained 394 drugs with known indication from CMAP. To validate the feasibility of applying new DMAP for systematic drug repositioning, we compared the performance of DMAP and the well-known CMAP database on two popular computational techniques: drug-drug-similarity-based method with leave-one-out validation and Kolmogorov-Smirnov scoring based method. In drug-drug-similarity-based method, the drug repositioning prediction using DMAP achieved an Area-Under-Curve (AUC) score of 0.82, compared with that using CMAP, AUC = 0.64. For Kolmogorov-Smirnov scoring based method, with DMAP, we were able to retrieve several drug indications which could not be retrieved using CMAP. DMAP data can be queried using the existing C2MAP server or downloaded freely at: http://bio.informatics.iupui.edu/cmaps Conclusions Reliable measurements of how drug affect disease-related proteins are critical to ongoing drug development in the genome medicine era. We demonstrated that DMAP can help drug development professionals assess drug-to-protein relationship data and improve chances of success for systematic drug repositioning efforts.

End-of-life cancer care has been criticized as frequently inappropriate and aggressive. Providing appropriate care to people with cancer is a public health priority. Quality indicators are considered a valid way to evaluate the appropriateness of end-of-life cancer care within a health care system. We conducted a population-level retrospective observational study of all cancer decedents in Belgium in 2012 to assess end-of-life care and risk factors for exposure to care. We linked eight full-population databases on health care use, cancer diagnoses, and demographic and socioeconomic variables. We used analysis of variance to examine factors associated with exposure to appropriate or inappropriate end-of-life cancer care. Of the 26,464 people in Belgium who died from cancer in 2012, 47 percent received specialist palliative care, and 30 percent died at home. In the last thirty days of life, 17 percent received chemotherapy, and 66 percent received diagnostic testing. For 17 percent, palliative care was initiated only in the last fourteen days of life. Our results suggest a need to focus policy on reducing aggressive and inappropriate care at the end of life and an opportunity to increase the proportion of people who receive specialist palliative care and die at home.