ABSTRACT
PubMed is a freely accessible system for searching the biomedical literature, with ~ 2.5 million users worldwide on an average workday. In order to better meet our users' needs in an era of information overload, we have recently developed PubMed Labs (www.pubmed.gov/labs), an experimental system for users to test new search features/tools (e.g. Best Match) and provide feedback, which enables us to make more informed decisions about potential changes to improve the search quality and overall usability of PubMed. In addition, PubMed Labs features a mobile-first and responsive layout that offers better support for accessing PubMed from increasingly popular mobiles and small-screen devices. In this paper, we detail PubMed Labs, its purpose, new features and best practices. We also encourage users to share their experience with us; based on which we are continuously improving PubMed Labs with more advanced features and better user experience.
Subject(s)
PubMed , Publications , Search Engine , Statistics as TopicABSTRACT
PubMed is a free search engine for biomedical literature accessed by millions of users from around the world each day. With the rapid growth of biomedical literature-about two articles are added every minute on average-finding and retrieving the most relevant papers for a given query is increasingly challenging. We present Best Match, a new relevance search algorithm for PubMed that leverages the intelligence of our users and cutting-edge machine-learning technology as an alternative to the traditional date sort order. The Best Match algorithm is trained with past user searches with dozens of relevance-ranking signals (factors), the most important being the past usage of an article, publication date, relevance score, and type of article. This new algorithm demonstrates state-of-the-art retrieval performance in benchmarking experiments as well as an improved user experience in real-world testing (over 20% increase in user click-through rate). Since its deployment in June 2017, we have observed a significant increase (60%) in PubMed searches with relevance sort order: it now assists millions of PubMed searches each week. In this work, we hope to increase the awareness and transparency of this new relevance sort option for PubMed users, enabling them to retrieve information more effectively.
Subject(s)
Data Mining/methods , Information Storage and Retrieval/methods , Algorithms , Humans , MEDLINE , Machine Learning , PubMed , Publications , Search EngineABSTRACT
The National Center for Biotechnology Information has created the dbGaP public repository for individual-level phenotype, exposure, genotype and sequence data and the associations between them. dbGaP assigns stable, unique identifiers to studies and subsets of information from those studies, including documents, individual phenotypic variables, tables of trait data, sets of genotype data, computed phenotype-genotype associations, and groups of study subjects who have given similar consents for use of their data.
Subject(s)
Databases, Genetic , Genotype , Phenotype , Computational Biology , Databases, Factual , National Library of Medicine (U.S.)/organization & administration , United StatesABSTRACT
Single-nucleotide polymorphisms (SNPs) constitute the great majority of variations in the human genome, and as heritable variable landmarks they are useful markers for disease mapping and resolving population structure. Redundant coverage in overlaps of large-insert genomic clones, sequenced as part of the Human Genome Project, comprises a quarter of the genome, and it is representative in terms of base compositional and functional sequence features. We mined these regions to produce 500,000 high-confidence SNP candidates as a uniform resource for describing nucleotide diversity and its regional variation within the genome. Distributions of marker density observed at different overlap length scales under a model of recombination and population size change show that the history of the population represented by the public genome sequence is one of collapse followed by a recent phase of mild size recovery. The inferred times of collapse and recovery are Upper Paleolithic, in agreement with archaeological evidence of the initial modern human colonization of Europe.
