隨著檢測技術的進步，現今的研究已導入了所謂「體學」概念，其重要特性就是高通量分析，研究一群基因間彼此的關係，這也使得以往針對單一或少數基因研究的文獻回顧工作更為繁重，因應體學時代的來臨，新的文獻探勘工具必須要能夠在龐大的文獻資料中，為基因與蛋白質名稱建立正確的索引，讓使用者能夠正確地搜尋到基因相關的文獻，以及利用基因註解資訊以及文獻內容判斷基因間關聯性；對於不同型態的文獻資源，包含基因的註解資訊、文獻摘要以及全文內容，要能夠有效運用以提升文獻使用的效率。
在本論文中，針對建立索引時過程中的基因名稱正規化提出了一創新的演算法來改善基因名稱一文多義的問題；此外，本研究另提出了一度量基因功能性註解辭彙間語意相似性的方法，利用度量不同基因間功能的相似性來協助使用者快速找出彼此的關聯，並從相關的文獻摘要中，利用自然語言處理與有限狀態機等技術，擷取描述基因與基因、功能、疾病間關係的句子供使用者參考；最後，本研究利用資訊檢索的方法與文件摘要的技術，提出一段落排序演算法，以摘要中的資訊做為查詢詞，對於全文中的段落評估與摘要中資訊的相關度與重要性，得到段落排序的結果，並將較重要的段落推薦給使用者瀏覽或利用，以提升全文文獻閱讀的效率。

Nowadays, biomedical study has entered the omics era. Omic study features high throughput analysis and investigation of relationship among a group of genes. This increases burdens of literature survey for such large amount of genes and raises pressing need for a suitable text mining tool in omic era. A text mining tool for omic study should have reliable indexing of gene/proteins, and abilities of summarizing information from various types of resources.
In this dissertation, a novel gene normalization algorithm for improving accurate indexing has been proposed. This approach integrated maximum entropy model and fuzzy approach to dealing with ambiguous gene mentions, and achieved approved performance. To group related genes, a novel semantic similarity measure for Gene Ontology term has been implemented to group genes sharing similar annotations. In abstracts, sentences describing relationships of a gene and other genes, functions and diseases provide very essential information. Therefore, a practical information extraction method was proposed to extract useful information from abstracts. In addition to annotation and abstract, full-text article carries more detailed information but its long length rises time-cost of reading. A proposed paragraph ranking approach recommends important paragraphs of full-text by keywords in abstracts for efficient reading.
With regard to requirements of text mining tool for omic study, the study proposed novel and practical approaches and evaluated their performance.

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