A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Text retrieval conference (TREC) genomics pre-track workshop
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Supervised term weighting for automated text categorization
Proceedings of the 2003 ACM symposium on Applied computing
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A pitfall and solution in multi-class feature selection for text classification
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An application of text categorization methods to gene ontology annotation
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Contrast and variability in gene names
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Data Analysis and Visualization in Genomics and Proteomics
Data Analysis and Visualization in Genomics and Proteomics
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Human gene name normalization using text matching with automatically extracted synonym dictionaries
BioNLP '06 Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis
Unsupervised gene/protein named entity normalization using automatically extracted dictionaries
ISMB '05 Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics
Gene Functional Annotation with Dynamic Hierarchical Classification Guided by Orthologs
DS '09 Proceedings of the 12th International Conference on Discovery Science
Application of semantic kernels to literature-based gene function annotation
DS'11 Proceedings of the 14th international conference on Discovery science
Text Mining in Bioinformatics: Research and Application
International Journal of Information Retrieval Research
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Gene ontology (GO) consists of three structured controlled vocabularies, i.e., GO domains, developed for describing attributes of gene products, and its annotation is crucial to provide a common gateway to access different model organism databases. This paper explores an effective application of text categorization methods to this highly practical problem in biology. As a first step, we attempt to tackle the automatic GO annotation task posed in the Text Retrieval Conference (TREC) 2004 Genomics Track. Given a pair of genes and an article reference where the genes appear, the task simulates assigning GO domain codes. We approach the problem with careful consideration of the specialized terminology and pay special attention to various forms of gene synonyms, so as to exhaustively locate the occurrences of the target gene. We extract the words around the spotted gene occurrences and used them to represent the gene for GO domain code annotation. We regard the task as a text categorization problem and adopt a variant of kNN with supervised term weighting schemes, making our method among the top-performing systems in the TREC official evaluation. Furthermore, we investigate different feature selection policies in conjunction with the treatment of terms associated with negative instances. Our experiments reveal that round-robin feature space allocation with eliminating negative terms substantially improves performance as GO terms become specific.