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  1. The ability to detect nuclei in embryos is essential for studying the development of multicellular organisms. A system of automated nuclear detection has already been tested on a set of four-dimensional (4D) N...

    Authors: Shugo Hamahashi, Shuichi Onami and Hiroaki Kitano
    Citation: BMC Bioinformatics 2005 6:125
  2. 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 ...

    Authors: Frédéric Ehrler, Antoine Geissbühler, Antonio Jimeno and Patrick Ruch
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S23

    This article is part of a Supplement: Volume 6 Supplement 1

  3. 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...

    Authors: Simon B Rice, Goran Nenadic and Benjamin J Stapley
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S22

    This article is part of a Supplement: Volume 6 Supplement 1

  4. 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...

    Authors: Francisco M Couto, Mário J Silva and Pedro M Coutinho
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S21

    This article is part of a Supplement: Volume 6 Supplement 1

  5. 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...

    Authors: Karin Verspoor, Judith Cohn, Cliff Joslyn, Sue Mniszewski, Andreas Rechtsteiner, Luis M Rocha and Tiago Simas
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S20

    This article is part of a Supplement: Volume 6 Supplement 1

  6. 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,...

    Authors: Martin Krallinger, Maria Padron and Alfonso Valencia
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S19

    This article is part of a Supplement: Volume 6 Supplement 1

  7. 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...

    Authors: Soumya Ray and Mark Craven
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S18

    This article is part of a Supplement: Volume 6 Supplement 1

  8. 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...

    Authors: Evelyn B Camon, Daniel G Barrell, Emily C Dimmer, Vivian Lee, Michele Magrane, John Maslen, David Binns and Rolf Apweiler
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S17

    This article is part of a Supplement: Volume 6 Supplement 1

  9. 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...

    Authors: Christian Blaschke, Eduardo Andres Leon, Martin Krallinger and Alfonso Valencia
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S16

    This article is part of a Supplement: Volume 6 Supplement 1

  10. 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...

    Authors: Katrin Fundel, Daniel Güttler, Ralf Zimmer and Joannis Apostolakis
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S15

    This article is part of a Supplement: Volume 6 Supplement 1

  11. 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...

    Authors: Daniel Hanisch, Katrin Fundel, Heinz-Theodor Mevissen, Ralf Zimmer and Juliane Fluck
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S14

    This article is part of a Supplement: Volume 6 Supplement 1

  12. 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...

    Authors: Jeremiah Crim, Ryan McDonald and Fernando Pereira
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S13

    This article is part of a Supplement: Volume 6 Supplement 1

  13. 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...

    Authors: Marc E Colosimo, Alexander A Morgan, Alexander S Yeh, Jeffrey B Colombe and Lynette Hirschman
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S12

    This article is part of a Supplement: Volume 6 Supplement 1

  14. 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....

    Authors: Lynette Hirschman, Marc Colosimo, Alexander Morgan and Alexander Yeh
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S11

    This article is part of a Supplement: Volume 6 Supplement 1

  15. 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...

    Authors: Jörg Hakenberg, Steffen Bickel, Conrad Plake, Ulf Brefeld, Hagen Zahn, Lukas Faulstich, Ulf Leser and Tobias Scheffer
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S9

    This article is part of a Supplement: Volume 6 Supplement 1

  16. 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...

    Authors: Tomohiro Mitsumori, Sevrani Fation, Masaki Murata, Kouichi Doi and Hirohumi Doi
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S8

    This article is part of a Supplement: Volume 6 Supplement 1

  17. 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...

    Authors: GuoDong Zhou, Dan Shen, Jie Zhang, Jian Su and SoonHeng Tan
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S7

    This article is part of a Supplement: Volume 6 Supplement 1

  18. 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...

    Authors: Ryan McDonald and Fernando Pereira
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S6

    This article is part of a Supplement: Volume 6 Supplement 1

  19. 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.

    Authors: Jenny Finkel, Shipra Dingare, Christopher D Manning, Malvina Nissim, Beatrice Alex and Claire Grover
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S5

    This article is part of a Supplement: Volume 6 Supplement 1

  20. 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...

    Authors: Shuhei Kinoshita, K Bretonnel Cohen, Philip V Ogren and Lawrence Hunter
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S4

    This article is part of a Supplement: Volume 6 Supplement 1

  21. 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...

    Authors: Lorraine Tanabe, Natalie Xie, Lynne H Thom, Wayne Matten and W John Wilbur
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S3

    This article is part of a Supplement: Volume 6 Supplement 1

  22. 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...

    Authors: Alexander Yeh, Alexander Morgan, Marc Colosimo and Lynette Hirschman
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S2

    This article is part of a Supplement: Volume 6 Supplement 1

  23. 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 ...

    Authors: Lynette Hirschman, Alexander Yeh, Christian Blaschke and Alfonso Valencia
    Citation: BMC Bioinformatics 2005 6(Suppl 1):S1

    This article is part of a Supplement: Volume 6 Supplement 1

  24. The availability of the human genome sequence as well as the large number of physically accessible oligonucleotides, cDNA, and BAC clones across the entire genome has triggered and accelerated the use of sever...

    Authors: Björn Menten, Filip Pattyn, Katleen De Preter, Piet Robbrecht, Evi Michels, Karen Buysse, Geert Mortier, Anne De Paepe, Steven van Vooren, Joris Vermeesch, Yves Moreau, Bart De Moor, Stefan Vermeulen, Frank Speleman and Jo Vandesompele
    Citation: BMC Bioinformatics 2005 6:124
  25. Sequence comparison by alignment is a fundamental tool of molecular biology. In this paper we show how a number of sequence comparison tasks, including the detection of unique genomic regions, can be accomplis...

    Authors: Bernhard Haubold, Nora Pierstorff, Friedrich Möller and Thomas Wiehe
    Citation: BMC Bioinformatics 2005 6:123
  26. Searching for approximate patterns in large promoter sequences frequently produces an exceedingly high numbers of results. Our aim was to exploit biological knowledge for definition of a sheltered search space...

    Authors: Stefania Bortoluzzi, Alessandro Coppe, Andrea Bisognin, Cinzia Pizzi and Gian Antonio Danieli
    Citation: BMC Bioinformatics 2005 6:121
  27. Many complex random networks have been found to be scale-free. Existing literature on scale-free networks has rarely considered potential false positive and false negative links in the observed networks, espec...

    Authors: Nan Lin and Hongyu Zhao
    Citation: BMC Bioinformatics 2005 6:119
  28. Computational biologists use Expectation values (E-values) to estimate the number of solutions that can be expected by chance during a database scan. Here we focus on computing Expectation values for RNA motif...

    Authors: André Lambert, Matthieu Legendre, Jean-Fred Fontaine and Daniel Gautheret
    Citation: BMC Bioinformatics 2005 6:118
  29. Periodic phenomena are widespread in biology. The problem of finding periodicity in biological time series can be viewed as a multiple hypothesis testing of the spectral content of a given time series. The exa...

    Authors: Miika Ahdesmäki, Harri Lähdesmäki, Ron Pearson, Heikki Huttunen and Olli Yli-Harja
    Citation: BMC Bioinformatics 2005 6:117
  30. The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determine which clustering algorithm is most appro...

    Authors: Matthew A Hibbs, Nathaniel C Dirksen, Kai Li and Olga G Troyanskaya
    Citation: BMC Bioinformatics 2005 6:115
  31. Understanding transcriptional regulation of gene expression is one of the greatest challenges of modern molecular biology. A central role in this mechanism is played by transcription factors, which typically b...

    Authors: D Corà, C Herrmann, C Dieterich, F Di Cunto, P Provero and M Caselle
    Citation: BMC Bioinformatics 2005 6:110
  32. This paper addresses the problem of recognising DNA cis-regulatory modules which are located far from genes. Experimental procedures for this are slow and costly, and computational methods are hard, because th...

    Authors: Irina Abnizova, Rene te Boekhorst, Klaudia Walter and Walter R Gilks
    Citation: BMC Bioinformatics 2005 6:109
  33. Comparison of data produced on different microarray platforms often shows surprising discordance. It is not clear whether this discrepancy is caused by noisy data or by improper probe matching between platform...

    Authors: Scott L Carter, Aron C Eklund, Brigham H Mecham, Isaac S Kohane and Zoltan Szallasi
    Citation: BMC Bioinformatics 2005 6:107
  34. Cluster analyses are used to analyze microarray time-course data for gene discovery and pattern recognition. However, in general, these methods do not take advantage of the fact that time is a continuous varia...

    Authors: Hua Liu, Sergey Tarima, Aaron S Borders, Thomas V Getchell, Marilyn L Getchell and Arnold J Stromberg
    Citation: BMC Bioinformatics 2005 6:106
  35. G- Protein coupled receptors (GPCRs) comprise the largest group of eukaryotic cell surface receptors with great pharmacological interest. A broad range of native ligands interact and activate GPCRs, leading to...

    Authors: Nikolaos G Sgourakis, Pantelis G Bagos, Panagiotis K Papasaikas and Stavros J Hamodrakas
    Citation: BMC Bioinformatics 2005 6:104
  36. Microarray analysis has become a widely used technique for the study of gene-expression patterns on a genomic scale. As more and more laboratories are adopting microarray technology, there is a need for powerf...

    Authors: Michael Maurer, Robert Molidor, Alexander Sturn, Juergen Hartler, Hubert Hackl, Gernot Stocker, Andreas Prokesch, Marcel Scheideler and Zlatko Trajanoski
    Citation: BMC Bioinformatics 2005 6:101

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