TY - JOUR AB - 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. AU - Brown, P.* AU - RELISH Consortium (Filipp, F.V.) AU - RELISH Consortium (Roobol, M.J.) AU - Zhou, Y.* C1 - 57380 C2 - 47724 SP - 1-67 TI - Large expert-curated database for benchmarking document similarity detection in biomedical literature search. JO - Database VL - 2019 PY - 2019 SN - 1758-0463 ER - TY - JOUR AB - The drug-minded protein interaction database (DrumPID) has been designed to provide fast, tailored information on drugs and their protein networks including indications, protein targets and side-targets. Starting queries include compound, target and protein interactions and organism-specific protein families. Furthermore, drug name, chemical structures and their SMILES notation, affected proteins (potential drug targets), organisms as well as diseases can be queried including various combinations and refinement of searches. Drugs and protein interactions are analyzed in detail with reference to protein structures and catalytic domains, related compound structures as well as potential targets in other organisms. DrumPID considers drug functionality, compound similarity, target structure, interactome analysis and organismic range for a compound, useful for drug development, predicting drug side-effects and structure-activity relationships.Database URL:http://drumpid.bioapps.biozentrum.uni-wuerzburg.de. AU - Kunz, M.* AU - Liang, C.B.* AU - Nilla, S.* AU - Cecil, A. AU - Dandekar, T.* C1 - 48298 C2 - 39997 CY - Oxford TI - The Drug-minded Protein Interaction Database (DrumPID) for efficient target analysis and drug development. JO - Database VL - 2016 PB - Oxford Univ Press PY - 2016 SN - 1758-0463 ER - TY - JOUR AB - Plants are sessile and therefore exposed to a number of biotic and abiotic stresses. Drought is the major abiotic stress restricting plant growth worldwide. A number of genes involved in drought stress response have already been characterized, mainly in the model species Arabidopsis thaliana and Oryza sativa. However, with the aim to produce drought tolerant crop varieties, it is of importance to identify the respective orthologs for each species. We have developed DroughtDB, a manually curated compilation of molecularly characterized genes that are involved in drought stress response. DroughtDB includes information about the originally identified gene, its physiological and/or molecular function and mutant phenotypes and provides detailed information about computed orthologous genes in nine model and crop plant species including maize and barley. All identified orthologs are interlinked with the respective reference entry in MIPS/PGSB PlantsDB, which allows retrieval of additional information like genome context and sequence information. Thus, DroughtDB is a valuable resource and information tool for researchers working on drought stress and will facilitate the identification, analysis and characterization of genes involved in drought stress tolerance in agriculturally important crop plants. Database URL: http://pgsb.helmholtz-muenchen.de/droughtdb/ AU - Alter, S.* AU - Bader, K.C. AU - Spannagl, M. AU - Wang, Y.* AU - Bauer, E.* AU - Schön, C.C.* AU - Mayer, K.F.X. C1 - 45082 C2 - 37202 TI - DroughtDB: An expert-curated compilation of plant drought stress genes and their homologs in nine species. JO - Database VL - 2015 PY - 2015 SN - 1758-0463 ER - TY - JOUR AB - The study of developmental processes in the mouse and other vertebrates includes the understanding of patterning along the anterior-posterior, dorsal-ventral and medial- lateral axis. Specifically, neural development is also of great clinical relevance because several human neuropsychiatric disorders such as schizophrenia, autism disorders or drug addiction and also brain malformations are thought to have neurodevelopmental origins, i.e. pathogenesis initiates during childhood and adolescence. Impacts during early neurodevelopment might also predispose to late-onset neurodegenerative disorders, such as Parkinson's disease. The neural tube develops from its precursor tissue, the neural plate, in a patterning process that is determined by compartmentalization into morphogenetic units, the action of local signaling centers and a well-defined and locally restricted expression of genes and their interactions. While public databases provide gene expression data with spatio-temporal resolution, they usually neglect the genetic interactions that govern neural development. Here, we introduce Mouse IDGenes, a reference database for genetic interactions in the developing mouse brain. The database is highly curated and offers detailed information about gene expressions and the genetic interactions at the developing mid-/hindbrain boundary. To showcase the predictive power of interaction data, we infer new Wnt/β-catenin target genes by machine learning and validate one of them experimentally. The database is updated regularly. Moreover, it can easily be extended by the research community. Mouse IDGenes will contribute as an important resource to the research on mouse brain development, not exclusively by offering data retrieval, but also by allowing data input. Database URL: http://mouseidgenes.helmholtz-muenchen.de. AU - Matthes, M. AU - Preusse, M. AU - Zhang, J. AU - Schechter, J. AU - Mayer, D. AU - Lentes, B. AU - Theis, F.J. AU - Prakash, N. AU - Wurst, W. AU - Trümbach, D. C1 - 32002 C2 - 34926 CY - Oxford TI - Mouse IDGenes: A reference database for genetic interactions in the developing mouse brain. JO - Database VL - 2014 PB - Oxford Univ Press PY - 2014 SN - 1758-0463 ER - TY - JOUR AB - Osteosarcoma (OS) is the most common primary bone cancer exhibiting high genomic instability. This genomic instability affects multiple genes and microRNAs to a varying extent depending on patient and tumor subtype. Massive research is ongoing to identify genes including their gene products and microRNAs that correlate with disease progression and might be used as biomarkers for OS. However, the genomic complexity hampers the identification of reliable biomarkers. Up to now, clinico-pathological factors are the key determinants to guide prognosis and therapeutic treatments. Each day, new studies about OS are published and complicate the acquisition of information to support biomarker discovery and therapeutic improvements. Thus, it is necessary to provide a structured and annotated view on the current OS knowledge that is quick and easily accessible to researchers of the field. Therefore, we developed a publicly available database and Web interface that serves as resource for OS-associated genes and microRNAs. Genes and microRNAs were collected using an automated dictionary-based gene recognition procedure followed by manual review and annotation by experts of the field. In total, 911 genes and 81 microRNAs related to 1331 PubMed abstracts were collected (last update: 29 October 2013). Users can evaluate genes and microRNAs according to their potential prognostic and therapeutic impact, the experimental procedures, the sample types, the biological contexts and microRNA target gene interactions. Additionally, a pathway enrichment analysis of the collected genes highlights different aspects of OS progression. OS requires pathways commonly deregulated in cancer but also features OS-specific alterations like deregulated osteoclast differentiation. To our knowledge, this is the first effort of an OS database containing manual reviewed and annotated up-to-date OS knowledge. It might be a useful resource especially for the bone tumor research community, as specific information about genes or microRNAs is quick and easily accessible. Hence, this platform can support the ongoing OS research and biomarker discovery. Database URL: http://osteosarcoma-db.uni-muenster.de. AU - Poos, K.* AU - Smida, J. AU - Nathrath, M. AU - Maugg, D. AU - Baumhoer, D. AU - Neumann, A.* AU - Korsching, E.* C1 - 31503 C2 - 34525 CY - Oxford TI - Structuring osteosarcoma knowledge: An osteosarcoma-gene association database based on literature mining and manual annotation. JO - Database VL - 2014 PB - Oxford Univ Press PY - 2014 SN - 1758-0463 ER -