PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 14630662

MASV--Multiple (BLAST) Annotation System Viewer.

Kim Carter·Matthew Bellgard
View original source ↗Export metadata ↓

Abstract

UNLABELLED: Multiple (BLAST) Annotation System Viewer (MASV) is a tool designed to aid in the annotation of genomic sequences. MASV enables the researcher to compare and analyse differences in annotation and analysis, resulting from changes in databases, analysis program parameters and results. This provides a unique capability for the user to conduct further bioinformatics analysis from the information obtained. AVAILABILITY: http://cbbc.murdoch.edu.au/projects/masv/

Explore related subjects

Abstracting and IndexingComputer GraphicsDatabase Management SystemsDatabases, GeneticHypermediaInformation Storage and RetrievalInternetSequence Analysis, DNAUser-Computer InterfaceComparative StudyEvaluation StudyJournal ArticleValidation Study

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kim Carter, Matthew Bellgard. 2003-11-22. MASV--Multiple (BLAST) Annotation System Viewer.. https://doi.org/10.1093/bioinformatics%2Fbtg301

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2005

PatGen--a consolidated resource for searching genetic patent sequences.

UNLABELLED: Compared to the wealth of online resources covering genomic, proteomic and derived data the Bioinformatics community is rather underserved when it comes to patent information related to biological sequences. The current online resources are either incomplete or rather expensive. This paper describes, PatGen, an integrated database containing data from bioinformatic and patent resources. This effort addresses the inconsistency of publicly available genetic patent data coverage by providing access to a consolidated dataset. AVAILABILITY: PatGen can be searched at http://www.patgendb.com CONTACT: rjdrouse@patentinformatics.com.

Abstracting and Indexing↗
PubMed2005

MaSTerClass: a case-based reasoning system for the classification of biomedical terms.

MOTIVATION: The sheer volume of textually described biomedical knowledge exerts the need for natural language processing (NLP) applications in order to allow flexible and efficient access to relevant information. Specialized semantic networks (such as biomedical ontologies, terminologies or semantic lexicons) can significantly enhance these applications by supplying the necessary terminological information in a machine-readable form. With the explosive growth of bio-literature, new terms (representing newly identified concepts or variations of the existing terms) may not be explicitly described within the network and hence cannot be fully exploited by NLP applications. Linguistic and statistical clues can be used to extract many new terms from free text. The extracted terms still need to be correctly positioned relative to other terms in the network. Classification as a means of semantic typing represents the first step in updating a semantic network with new terms. RESULTS: The MaSTerClass system implements the case-based reasoning methodology for the classification of biomedical terms.

Abstracting and Indexing↗
PubMed2005

Improved identification of noun phrases in clinical radiology reports using a high-performance statistical natural language parser augmented with the UMLS specialist lexicon.

OBJECTIVE: The aim of this study was to develop and evaluate a method of extracting noun phrases with full phrase structures from a set of clinical radiology reports using natural language processing (NLP) and to investigate the effects of using the UMLS(R) Specialist Lexicon to improve noun phrase identification within clinical radiology documents. DESIGN: The noun phrase identification (NPI) module is composed of a sentence boundary detector, a statistical natural language parser trained on a nonmedical domain, and a noun phrase (NP) tagger. The NPI module processed a set of 100 XML-represented clinical radiology reports in Health Level 7 (HL7)(R) Clinical Document Architecture (CDA)-compatible format. Computed output was compared with manual markups made by four physicians and one author for maximal (longest) NP and those made by one author for base (simple) NP, respectively. An extended lexicon of biomedical terms was created from the UMLS Specialist Lexicon and used to improve NPI performance. RESULTS: The test set was 50 randomly selected reports. The sentence boundary detector achieved 99.0% precision and 98.6% recall. The overall maximal NPI precision and recall were 78.9% and 81.5% before using the UMLS Specialist Lexicon and 82.1% and 84.6% after. The overall base NPI precision and recall were 88.2% and 86.8% before using the UMLS Specialist Lexicon and 93.1% and 92.6% after, reducing false-positives by 31.1% and false-negatives by 34.3%. CONCLUSION: The sentence boundary detector performs excellently. After the adaptation using the UMLS Specialist Lexicon, the statistical parser's NPI performance on radiology reports increased to levels comparable to the parser's native performance in its newswire training domain and to that reported by other researchers in the general nonmedical domain.

Abstracting and Indexing↗