A Study on Gene Annotation from Biological Literature
Date Issued
2007
Date
2007
Author(s)
Hou, Wen-Juan
DOI
en-US
Abstract
In this dissertation we study various issues which will help biologists obtain relevant gene information from the rapidly growing body of online material in the biomedical field. Namely, our studies aim to improve the performance of biomedical named entity recognition, to classify the relevant documents for database curators, to improve gene function extraction and Gene Ontology annotation. We propose some approaches for each issue. Our final goal is to integrate the information extracted from the biological literature into the existing databases.
Given biomedical documents, it is fundamental to recognize the biomedical entities first. For improving the performance of biomedical entity recognition, we introduce a hybrid strategy for a filtering strategy and an integration strategy. We show a fully automatic method of mining collocates from scientific texts in the protein and gene domain and applying collocates to filter out unlikely protein/gene candidates. Furthermore, we use the integration strategy to increase recall rates. The experimental results demonstrate this hybrid strategy performs better than the original protein/gene taggers. After biomedical entities are recognized, another important issue is to retrieve the relevant documents for database curators so that this information can be added to the existing database. The dissertation also investigates different granularities of classification for GO annotation. We utilize the three parts of an article, i.e., (1) titles and abstracts, (2) Mesh terms and (3) captions of tables and figures, as well as the semantic network of UMLS as features for SVM. Evaluation results demonstrate overall high performance in this work. Thirdly, gene function extraction is essential for biologists to understand genes. Currently, researchers can manually submit GeneRIFs in the Entrez gene database. We propose two approaches, a "function extraction approach" and a "machine learning approach" to automatically extract GeneRIFs from the curatable documents generated in the previous step. The experimental results are promising.
Finally, in order to integrate the extracted information into the database, it is necessary to present genes with standard vocabularies. We use the highly popular controlled vocabularies, Gene Ontology (GO), in this dissertation. Researchers usually do GO annotation at different levels, i.e., "document level" and "gene level." The former annotates the GO terms in the document without identifying the relevant genes while the latter explicitly identifies the annotation of genes, GO terms and documents. This dissertation explores GO annotation at both levels. At the document level, we annotate genes by the relevance detection approach. At the gene level, we introduce density and gravitation models. Moreover, we utilize GeneRIFs extracted in the previous stage as the references for annotating GO terms at the document level. It will be of great help for database curators. In addition, we explore the proximity of genes and GO terms in the paragraph at the gene level. Our experiments show that density and gravitation relationships are good features for GO annotation.
Subjects
生物醫學名詞辨識
基因功能
基因本體標示
支援向量機
biomedical named entity recognition
biological document classification
gene function
Gene Ontology annotation
Support Vector Machine
Type
thesis
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