Use and Management of Classification Systems for Knowledge-Based Indexing

The MedIndEx (Medical Indexing Expert) research project combines artificial intelligence and information retrieval principles and methods to develop and test an interactive knowledge-based prototype for computer-assisted indexing of the MEDLINE database. By encoding the indexing scheme in a knowledge base (KB), and designing a system for indexers to use in a workstation environment, the objective of this project is to facilitate "expert indexing" that is performed at the National Library of Medicin

Contextual Hierarchies in Classification Schemes

This paper is concerned with the encoding of contextual hierarchies. In particular, such hierarchies make it possible to create a single, complete classified display of very large thesauri. This classification may use the same descriptor with different views, as evidenced by the same descriptor as more than one node in the classification, where the nodes have different sets of children. This son of knowledge organization requires special computer representation techniques using contextual indicators for encoding the parent-child relationship. One solution" is to avoid having a unified classification in favor of many hierarchical families as used by the INSPEC® and ERIC® thesauri. However, this author considers it a particular strength to have a unified classification, of which the Medical Subject Headings (MeSH®) tree structures is a primary example. This paper describes the traditional method of using tree numbers as contextual indicators. We then propose a new experimental method of semantic labels, developed for the MedIndEx™ prototype, as possibly having certain advantages over tree numbers. We conclude with the hope that this workshop will provide feedback regarding the significance of the problem and substance of our proposal

Automatic indexing by discipline and high-level categories: Methodology and potential applications.

This paper first describes the methodology of journal descriptor (JD) ndexing, based on human indexing at the journal level using only 127 descriptors, and applying statistical methods that associate this journal indexing with text words in a training set of MEDLINE® citations. These associations form the basis for automatic indexing of documents outside the training set. The paper then presents the new technique of semantic type (ST) indexing, based on JD indexing associated with each of 134 ST's, and applying the standard cosine coefficient measure to compare the similarity between the JD indexing of a document and the JD indexing of each ST. The ST indexing of the document is the list of ST's ranked in decreasing order of similarity between the JD indexing of the document and the JD indexing of the ST's. Discussion of the potential usefulness and application of the very general indexing provided by JD's and ST's comprises the remainder of the paper. JD's have been used for more than thirty years to search MEDLINE by discipline, and discipline-based indexing is in evidence on the Web. It is suggested, with several examples, that ST's may convey a unique slant of a document's content not normally represented in standard indexing vocabularies. Use of ST indexing to rank retrieved output is mentioned as a possible application. Notwithstanding the importance of methodology and performance issues, the intent of this paper is to explore questions of the potential utility and applicability of JD and ST indexing

A single-gene cause in 29.5% of cases of steroid-resistant nephrotic syndrome

Steroid-resistant nephrotic syndrome (SRNS) is the second most frequent cause of ESRD in the first two decades of life. Effective treatment is lacking. First insights into disease mechanisms came from identification of single-gene causes of SRNS. However, the frequency of single-gene causation and its age distribution in large cohorts are unknown. We performed exon sequencing of NPHS2 and WT1 for 1783 unrelated, international families with SRNS. We then examined all patients by microfluidic multiplex PCR and next-generation sequencing for all 27 genes known to cause SRNS if mutated. We detected a single-gene cause in 29.5% (526 of 1783) of families with SRNS that manifested before 25 years of age. The fraction of families in whom a single-gene cause was identified inversely correlated with age of onset. Within clinically relevant age groups, the fraction of families with detection of the single-gene cause was as follows: onset in the first 3 months of life (69.4%), between 4 and 12 months old (49.7%), between 1 and 6 years old (25.3%), between 7 and 12 years old (17.8%), and between 13 and 18 years old (10.8%). For PLCE1, specific mutations correlated with age of onset. Notably, 1% of individuals carried mutations in genes that function within the coenzyme Q10 biosynthesis pathway, suggesting that SRNS may be treatable in these individuals. Our study results should facilitate molecular genetic diagnostics of SRNS, etiologic classification for therapeutic studies, generation of genotype-phenotype correlations, and the identification of individuals in whom a targeted treatment for SRNS may be available

New insights into the genetic etiology of Alzheimer's disease and related dementias

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele

VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center in 2023.

The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) is a Bioinformatics Resource Center funded by the National Institutes of Health with additional funding from the Wellcome Trust. VEuPathDB supports >600 organisms that comprise invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts. Since 2004, VEuPathDB has analyzed omics data from the public domain using contemporary bioinformatic workflows, including orthology predictions via OrthoMCL, and integrated the analysis results with analysis tools, visualizations, and advanced search capabilities. The unique data mining platform coupled with >3000 pre-analyzed data sets facilitates the exploration of pertinent omics data in support of hypothesis driven research. Comparisons are easily made across data sets, data types and organisms. A Galaxy workspace offers the opportunity for the analysis of private large-scale datasets and for porting to VEuPathDB for comparisons with integrated data. The MapVEu tool provides a platform for exploration of spatially resolved data such as vector surveillance and insecticide resistance monitoring. To address the growing body of omics data and advances in laboratory techniques, VEuPathDB has added several new data types, searches and features, improved the Galaxy workspace environment, redesigned the MapVEu interface and updated the infrastructure to accommodate these changes