Volume 6 Supplement 1
Edited by Christian Blaschke, Lynette Hirschman, Alfonso Valencia, Alexander Yeh
A critical assessment of text mining methods in molecular biology. Go to conference site.
Granada, SpainMarch 28-31, 2004
The goal of the first BioCreAtIvE challenge (Critical Assessment of Information Extraction in Biology) was to provide a set of common evaluation tasks to assess the state of the art for text mining applied to ...
The biological research literature is a major repository of knowledge. As the amount of literature increases, it will get harder to find the information of interest on a particular topic. There has been an inc...
Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The a...
Our approach to Task 1A was inspired by Tanabe and Wilbur's ABGene system [1, 2]. Like Tanabe and Wilbur, we approached the problem as one of part-of-speech tagging, adding a GENE tag to the standard tag set. Whe...
Good automatic information extraction tools offer hope for automatic processing of the exploding biomedical literature, and successful named entity recognition is a key component for such tools.
We present a model for tagging gene and protein mentions from text using the probabilistic sequence tagging framework of conditional random fields (CRFs). Conditional random fields model the probability P(t|o) of...
This paper proposes an ensemble of classifiers for biomedical name recognition in which three classifiers, one Support Vector Machine and two discriminative Hidden Markov Models, are combined effectively using...
Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in inf...
In task 1A of the BioCreAtIvE evaluation, systems had to be devised that recognize words and phrases forming gene or protein names in natural language sentences. We approach this problem by building a word cla...
The identification of mentions of gene or gene products in biomedical texts is a critical step in the development of text mining applications in biosciences. The complexity and ambiguity of gene nomenclature m...
Our goal in BioCreAtIve has been to assess the state of the art in text mining, with emphasis on applications that reflect real biological applications, e.g., the curation process for model organism databases....
We prepared and evaluated training and test materials for an assessment of text mining methods in molecular biology. The goal of the assessment was to evaluate the ability of automated systems to generate a li...
Document gene normalization is the problem of creating a list of unique identifiers for genes that are mentioned within a document. Automating this process has many potential applications in both information e...
Identification of gene and protein names in biomedical text is a challenging task as the corresponding nomenclature has evolved over time. This has led to multiple synonyms for individual genes and proteins, a...
Significant parts of biological knowledge are available only as unstructured text in articles of biomedical journals. By automatically identifying gene and gene product (protein) names and mapping these to uni...
Molecular Biology accumulated substantial amounts of data concerning functions of genes and proteins. Information relating to functional descriptions is generally extracted manually from textual data and store...
The Gene Ontology Annotation (GOA) database http://​www.​ebi.​ac.​uk/​GOA aims to provide high-quality supplementary GO annotation to proteins in the UniProt Know...
The BioCreative text mining evaluation investigated the application of text mining methods to the task of automatically extracting information from text in biomedical research articles. We participated in Task...
Within the emerging field of text mining and statistical natural language processing (NLP) applied to biomedical articles, a broad variety of techniques have been developed during the past years. Nevertheless,...
We participated in the BioCreAtIvE Task 2, which addressed the annotation of proteins into the Gene Ontology (GO) based on the text of a given document and the selection of evidence text from the document just...
The development of text mining systems that annotate biological entities with their properties using scientific literature is an important recent research topic. These systems need first to recognize the biolo...
Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed a...
In the context of the BioCreative competition, where training data were very sparse, we investigated two complementary tasks: 1) given a Swiss-Prot triplet, containing a protein, a GO (Gene Ontology) term and ...
Citation Impact 2023
Journal Impact Factor: 2.9
5-year Journal Impact Factor: 3.6
Source Normalized Impact per Paper (SNIP): 0.821
SCImago Journal Rank (SJR): 1.005
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