ArticleBMC bioinformatics2010
A method for automatically extracting infectious disease-related primers and probes from the literature.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- MiPRIME: an integrated and intelligent platform for mining primer and probe sequences of microbial species.Bioinformatics (Oxford, England) · 2024Article
- Using nanoinformatics methods for automatically identifying relevant nanotoxicology entities from the literature.BioMed research international · 2013Article
- e-MIR2: a public online inventory of medical informatics resources.BMC medical informatics and decision making · 2012Article
- Annotating genes and genomes with DNA sequences extracted from biomedical articles.Bioinformatics (Oxford, England) · 2011Article
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10 authors.
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Abstract
backgroundPrimer and probe sequences are the main components of nucleic acid-based detection systems. Biologists use primers and probes for different tasks, some related to the diagnosis and prescription of infectious diseases. The biological literature is the main information source for empirically validated primer and probe sequences. Therefore, it is becoming increasingly important for researchers to navigate this important information. In this paper, we present a four-phase method for extracting and annotating primer/probe sequences from the literature. These phases are: (1) convert each document into a tree of paper sections, (2) detect the candidate sequences using a set of finite state machine-based recognizers, (3) refine problem sequences using a rule-based expert system, and (4) annotate the extracted sequences with their related organism/gene information.
resultsWe tested our approach using a test set composed of 297 manuscripts. The extracted sequences and their organism/gene annotations were manually evaluated by a panel of molecular biologists. The results of the evaluation show that our approach is suitable for automatically extracting DNA sequences, achieving precision/recall rates of 97.98% and 95.77%, respectively. In addition, 76.66% of the detected sequences were correctly annotated with their organism name. The system also provided correct gene-related information for 46.18% of the sequences assigned a correct organism name.
conclusionsWe believe that the proposed method can facilitate routine tasks for biomedical researchers using molecular methods to diagnose and prescribe different infectious diseases. In addition, the proposed method can be expanded to detect and extract other biological sequences from the literature. The extracted information can also be used to readily update available primer/probe databases or to create new databases from scratch.
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