Comparative evaluation of the performance of online databases in answering toxicology queries

An evaluation of toxicology information resources is reported, comparing commercial online databases and a specialised in-house database. A mixed qualitative/quantitative approach, using ten test queries and detailed failure analysis was used. The main conclusions are: the in-house database is superior to any ‘general’ database in recall and precision; commercial databases are a useful complement, usually providing unique material; a range of databases should be used for good recall; for the commercial databases, complex search strategies are necessary, using the specific access points of each database; retrieval failures are due primarily to coverage, secondly to poor indexing of specific toxic effect. Detailed discussion is given of indexing policies and search strategies

Special Libraries, May-June 1978

Volume 69, Issue 5-6https://scholarworks.sjsu.edu/sla_sl_1978/1004/thumbnail.jp

The evolution of an on-line chemical search system for an industrial research unit.

The objectives of this study were to design an information system, using modern computer technology, to meet a research chemist's need for chemical structural information, to quantify the effects of increasing degrees of computer technology on the use made of the facilities, and to relate the use of the service back to the individual chemist, his performance and background. A computer system was developed based on Wiswesser Line Notation and molecular formula as the chemical structure descriptors. Systems design and analysis were performed so that access to the information could be obtained directly for individual compounds and more generally for classes of compounds. As the system was being developed, its use by information staff was monitored by constant interaction with the people concerned. Where appropriate, the system was modifiea to meet information staff's requirements, but a number of precautions had to be introduced to prevent mis-use. The research chemists' use of the information services was studied retrospectively over a two-year period. In addition to the use made, several other factors were observed for each chemist. These included performance measures and background information on the chemists' research role. The data showed a steady increase in the demand for the services by the research chemist as the degree of computerisation increased. The use made of the services related closely to the number of compounds prepared by each chemist, but there was no significant correlation between a chemist's success in preparing biologically active compounds and his information use. The very individual way in which chemists conduct their research was highlighted by the wide range of use of the information facilities and the low correlation with background factors. This makes the design of on-line systems for use by chemists themselves complex and justifies the existence of the information scientist as an interface

Data Base Mapping Model and Search Scheme to Facilitate Resource Sharing: Volume 1, Mapping of Chemical Data Bases and Mapping of Data Base Data Elements Using a Rational Data Base Structure

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNational Science Foundation / NSF SIS 74-1855

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Image-based Automated Chemical Database Annotation with Ensemble of Machine-Vision Classifiers

This paper presents an image-based annotation strategy for automated annotation of chemical databases. The proposed strategy is based on the use of a machine vision-based classifier for extracting a 2D chemical structure diagram in research articles and converting them into standard chemical file formats, a virtual Chemical Expert" system for screening the converted structures based on the level of estimated conversion accuracy, and a fragment-based measure for calculation intermolecular similarity. In particular, in order to overcome limited accuracies of individual machine-vision classifier, inspired by ensemble methods in machine learning, it is attempted to use of the ensemble of machine-vision classifiers. For annotation, calculated chemical similarity between the converted structures and entries in a virtual small molecule database is used to establish the links. Annotation test to link 121 journal articles to entries in PubChem database demonstrates that ensemble approach increases the coverage of annotation, while keeping the annotation quality (e.g., recall and precision rates) comparable to using a single machine-vision classifier.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87266/4/Saitou55.pd

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