A Rare Disease Patient Manager

ABSTRACT publicado: 6th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB. Salamanca, 28-30 Março 2012The personal health implications behind rare diseases are seldom considered in widespread medical care. The low incidence rate and complex treatment process makes rare disease research an underrated field in the life sciences. However, it is in these particular conditions that the strongest relations between genotypes and phenotypes are identified. The rare disease patient manager, detailed in this manuscript, presents an innovative perspective for a patient-centric portal integrating genetic and medical data. With this strategy, patient’s digital records are transparently integrated and connected to wet-lab genetics research in a seamless working environment. The resulting knowledge base offers multiple data views, geared towards medical staff, with patient treatment and monitoring data; genetics researchers, through a custom locus-specific database; and patients, who for once play an active role in their treatment and rare diseases research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press