Assessing the impact of predatory journals on policy and guidance documents: a cross-sectional study protocol.

INTRODUCTION: Many predatory journals fail to follow best publication practices. Studies assessing the impact of predatory journals have focused on how these articles are cited in reputable academic journals. However, it is possible that research from predatory journals is cited beyond the academic literature in policy documents and guidelines. Given that research used to inform public policy or government guidelines has the potential for widespread impact, we will examine whether predatory journals have penetrated public policy. METHODS AND ANALYSIS: This is a descriptive study with no hypothesis testing. Policy documents that cite work from the known predatory publisher OMICS will be downloaded from the Overton database. Overton collects policy documents from over 1200 sources worldwide. Policy documents will be evaluated to determine how the predatory journal article is used. We will also extract epidemiological details of the policy documents, including: who funded their development, the discipline the work is relevant to and the name of the organisations producing the policy. The record of scholarly citations of the identified predatory articles will also be examined. Findings will be reported with descriptive statistics using counts and percentages. ETHICS AND DISSEMINATION: No ethical approval was required for this study since it does not involve human or animal research. Study findings will be discussed at workshops on journalology and predatory publishing and will be disseminated through preprint, peer-reviewed literature and conference presentations.

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes.

Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.

Speeding disease gene discovery by sequence based candidate prioritization.

BACKGROUND: Regions of interest identified through genetic linkage studies regularly exceed 30 centimorgans in size and can contain hundreds of genes. Traditionally this number is reduced by matching functional annotation to knowledge of the disease or phenotype in question. However, here we show that disease genes share patterns of sequence-based features that can provide a good basis for automatic prioritization of candidates by machine learning. RESULTS: We examined a variety of sequence-based features and found that for many of them there are significant differences between the sets of genes known to be involved in human hereditary disease and those not known to be involved in disease. We have created an automatic classifier called PROSPECTR based on those features using the alternating decision tree algorithm which ranks genes in the order of likelihood of involvement in disease. On average, PROSPECTR enriches lists for disease genes two-fold 77% of the time, five-fold 37% of the time and twenty-fold 11% of the time. CONCLUSION: PROSPECTR is a simple and effective way to identify genes involved in Mendelian and oligogenic disorders. It performs markedly better than the single existing sequence-based classifier on novel data. PROSPECTR could save investigators looking at large regions of interest time and effort by prioritizing positional candidate genes for mutation detection and case-control association studies.