3.1     Task 1A.  Figures.
       3.1.1    Task 1 B Gene List Task.
       3.2     Task 2.    Corrected Figure.
      3.2.2.1 Prediction of GO terms
       3.2.2.2  Prediction of proteins
       3.2.2.3  Complete predictions of proteins and correspondig GO terms.

     4.1 Task 1A

•           Yu Song, Eunji Yi, Eunju Kim and Gary Geunbae Lee: POSBIOTMNER: A Machine Learning  Approach for BioNamed Entity Recognition.

•           Tomohiro Mitsumori, Sevrani Fation, Masaki Murata, Kouichi Doi and Hirohumi Doi: Gene/protein name recognition using Support Vector Machine  after dictionary matching

•           Ryan McDonald and Fernando Pereira: Identifying Gene and Protein Mentions in Text Using Conditional Random Fields

•           Yikun Guo, Henk Harkema, Ian Roberts, Rob Gaizauskas and Mark Hepple: University of Sheffield: Preliminary investigation of a dictionarybased approach to the Biocreative gene and protein identification task

•           Steffen Bickel, Ulf Brefeld, Lukas Faulstich, Jörg Hakenberg, Ulf Leser, Conrad Plake and Tobias Scheffer: A Support Vector Machine Classifier for Gene Name Recognition

•           Zhou GuoDong, Shen Dan, Zhang Jie, Su Jian, Tan Soon Heng and Tan Chew Lim: Recognition of Protein/Gene Names from Text using an Ensemble of Classifiers and Effective Abbreviation Resolution

•           Shuhei Kinoshita, K. Bretonnel Cohen, Philip Ogren and Lawrence Hunter: Entity  identification in the molecular biology domain with a stochastic POS tagger: the BioCreative task

•           Harald Kirsch and Dietrich Rebholz: Method used for BioCreAtIvE Task 1A

•           Shipra Dingare, Jenny Finkel, Christopher Manning, Malvina Nissim and Beatrice Alex: Exploring the Boundaries: Gene and Protein Identification in Biomedical Text

•           Javier Tamames: Text Detective: BioAlma's gene annotation tool

•           Daniel Hanisch, Katrin Fundel, HeinzTheodor Mevissen, Ralf Zimmer and Juliane Fluck: ProMiner: Organismspecific protein name detection using approximate string matching

•           Jeremiah Crim, Ryan McDonald and Fernando Pereira: Automatically Annotating Documents with Normalized Gene Lists

•            Ben Hachey, Huy Nguyen, Malvina Nissim, Bea Alex and Claire Grover: Grounding Gene Mentions with Respect to Gene Database Identifiers

•            Hongfang Liu, Cathy Wu and Carol Friedman: BioTagger: A Biological Entity Tagging System

•            Katrin Fundel, Daniel G¸ttler, Ralf Zimmer, Joannis Apostolakis : Exact versus approximate string matching for protein name identification

•           Soumya Ray and Mark Craven: Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text

•            JungHsien Chiang and HsuChun Yu: Extracting Functional Annotations of Proteins Based on Hybrid Text Mining Approaches

•             Francisco M. Couto, Mário J. Silva and Pedro Coutinho: FiGO: Finding GO Terms in Unstructured Text

•            Frédéric Ehrler and Patrick Ruch: Preliminary Report on the BioCreative Experiment: Task Presentation, System Description and Preliminary Results.

•           Simon B. Rice, Goran Nenadic and Benjamin J. Stapley: Protein function assignment using termbased support vector machines ­ BioCreative Task Two 2003

•           Martin Krallinger and María Magdalena Padron: Prediction of GO annotation by combining entity specific sentence sliding window profiles

•           Yuval Krymolowski, Beatrice Alex and Jochen L. Leidner: BioCreative Task 2.1: The EdinburghStanford system

•            Karin Verspoor, Judith Cohn, Cliff Joslyn, Sue Mniszewski, Andreas Rechtsteiner, Luis M. Rocha and Tiago Simas: Protein Annotation as Term Categorization in the Gene Ontology