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  1. Recent advances in molecular biology techniques provide an opportunity for developing detailed mathematical models of biological processes. An iterative scheme is introduced for model identification using avai...

    Authors: Kapil G Gadkar, Rudiyanto Gunawan and Francis J Doyle III
    Citation: BMC Bioinformatics 2005 6:155
  2. The extraction of biological knowledge from genome-scale data sets requires its analysis in the context of additional biological information. The importance of integrating experimental data sets with molecular...

    Authors: David J Reiss, Iliana Avila-Campillo, Vesteinn Thorsson, Benno Schwikowski and Timothy Galitski
    Citation: BMC Bioinformatics 2005 6:154
  3. Ambiguity is a problem in biosequence analysis that arises in various analysis tasks solved via dynamic programming, and in particular, in the modeling of families of RNA secondary structures with stochastic c...

    Authors: Janina Reeder, Peter Steffen and Robert Giegerich
    Citation: BMC Bioinformatics 2005 6:153
  4. Protein subcellular localization is an important determinant of protein function and hence, reliable methods for prediction of localization are needed. A number of prediction algorithms have been developed bas...

    Authors: Deepak Sarda, Gek Huey Chua, Kuo-Bin Li and Arun Krishnan
    Citation: BMC Bioinformatics 2005 6:152
  5. Analysis of variance is a powerful approach to identify differentially expressed genes in a complex experimental design for microarray and macroarray data. The advantage of the anova model is the possibility t...

    Authors: Christelle Hennequet-Antier, Hélène Chiapello, Karine Piot, Séverine Degrelle, Isabelle Hue, Jean-Paul Renard, François Rodolphe and Stéphane Robin
    Citation: BMC Bioinformatics 2005 6:150
  6. Massive text mining of the biological literature holds great promise of relating disparate information and discovering new knowledge. However, disambiguation of gene symbols is a major bottleneck.

    Authors: Bob JA Schijvenaars, Barend Mons, Marc Weeber, Martijn J Schuemie, Erik M van Mulligen, Hester M Wain and Jan A Kors
    Citation: BMC Bioinformatics 2005 6:149
  7. In the clinical context, samples assayed by microarray are often classified by cell line or tumour type and it is of interest to discover a set of genes that can be used as class predictors. The leukemia datas...

    Authors: Thanyaluk Jirapech-Umpai and Stuart Aitken
    Citation: BMC Bioinformatics 2005 6:148
  8. Sequencing of EST and BAC end datasets is no longer limited to large research groups. Drops in per-base pricing have made high throughput sequencing accessible to individual investigators. However, there are f...

    Authors: Stephen E Diener, Thomas D Houfek, Sam E Kalat, DE Windham, Mark Burke, Charles Opperman and Ralph A Dean
    Citation: BMC Bioinformatics 2005 6:147
  9. To date, 35 human diseases, some of which also exhibit anticipation, have been associated with unstable repeats. Anticipation has been reported in a number of diseases in which repeat expansion may have a role...

    Authors: Perseus I Missirlis, Carri-Lyn R Mead, Stefanie L Butland, BF Francis Ouellette, Rebecca S Devon, Blair R Leavitt and Robert A Holt
    Citation: BMC Bioinformatics 2005 6:145
  10. Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially...

    Authors: Seon-Young Kim and David J Volsky
    Citation: BMC Bioinformatics 2005 6:144
  11. Computational Biology needs computer-readable information records. Increasingly, meta-analysed and pre-digested information is being used in the follow up of high throughput experiments and other investigation...

    Authors: Barend Mons
    Citation: BMC Bioinformatics 2005 6:142
  12. Chemical information is now seen as critical for most areas of life sciences. But unlike Bioinformatics, where data is openly available and freely re-usable, most chemical information is closed and cannot be r...

    Authors: Peter Murray-Rust, John BO Mitchell and Henry S Rzepa
    Citation: BMC Bioinformatics 2005 6:141
  13. A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendline...

    Authors: Roland Schwarz, Patrick Musch, Axel von Kamp, Bernd Engels, Heiner Schirmer, Stefan Schuster and Thomas Dandekar
    Citation: BMC Bioinformatics 2005 6:135
  14. Codon substitution probabilities are used in many types of molecular evolution studies such as determining Ka/Ks ratios, creating ancestral DNA sequences or aligning coding DNA. Until the recent dramatic incre...

    Authors: Adrian Schneider, Gina M Cannarozzi and Gaston H Gonnet
    Citation: BMC Bioinformatics 2005 6:134
  15. Despite the continuous production of genome sequence for a number of organisms, reliable, comprehensive, and cost effective gene prediction remains problematic. This is particularly true for genomes for which ...

    Authors: Eduardo Eyras, Alexandre Reymond, Robert Castelo, Jacqueline M Bye, Francisco Camara, Paul Flicek, Elizabeth J Huckle, Genis Parra, David D Shteynberg, Carine Wyss, Jane Rogers, Stylianos E Antonarakis, Ewan Birney, Roderic Guigo and Michael R Brent
    Citation: BMC Bioinformatics 2005 6:131
  16. Phylogenetic footprinting is the identification of functional regions of DNA by their evolutionary conservation. This is achieved by comparing orthologous regions from multiple species and identifying the DNA ...

    Authors: Matthew J Wakefield, Peter Maxwell and Gavin A Huttley
    Citation: BMC Bioinformatics 2005 6:130
  17. Global regulatory mechanisms involving chromatin assembly and remodelling in the promoter regions of genes is implicated in eukaryotic transcription control especially for genes subjected to spatial and tempor...

    Authors: Mythily Ganapathi, Pragya Srivastava, Sushanta Kumar Das Sutar, Kaushal Kumar, Dipayan Dasgupta, Gajinder Pal Singh, Vani Brahmachari and Samir K Brahmachari
    Citation: BMC Bioinformatics 2005 6:126
  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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

  30. 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

  31. 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

  32. 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

  33. 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

  34. 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

  35. 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

  36. 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

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