本文關(guān)鍵詞： 基因本體 語義相似性 LDA BTM 主題模型　出處：《華東師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來,當生物學(xué)家發(fā)現(xiàn)未知基因時,往往將它們與已知基因進行比較,然后根據(jù)兩者之間的相似性來推斷未知基因的特性。生物學(xué)者通過比對算法來對基因序列或結(jié)構(gòu)進行比較,進而查找出與其功能上相似或者相關(guān)的基因。但研究表明,在功能上相似或者相關(guān)的基因在序列上并非一定具有很大的相關(guān)性。針對上述問題,目前的主流方法是通過計算基因在基因本體中注釋到的術(shù)語之間的語義相似性來分析和預(yù)測未知基因的特性。但是這類方法僅僅利用術(shù)語在基因本體中的關(guān)聯(lián)關(guān)系間接地反映基因的語義相似度,而沒有涉及到術(shù)語本身所包含的內(nèi)在語義內(nèi)涵。本文提出一種基于主題模型的基因語義相似度算法,從代表術(shù)語的文本中挖掘出內(nèi)在的語義信息,在一定程度上解決了傳統(tǒng)方法中的不足。本文主要有如下三個創(chuàng)新點:1.在計算術(shù)語對之間相似度時,從基因注釋到的術(shù)語本身去挖掘潛在的語義信息,然后將代表術(shù)語語義信息的文本轉(zhuǎn)化為高維的主題向量,從而將術(shù)語之間的相似度轉(zhuǎn)化為代表術(shù)語的高維主題向量之間的相似度。2.提出SSGTLDA和SSGTBTM兩個模型:對于通過Google搜索引擎得到的術(shù)語長文本信息,SSGTLDA模型對文本-主題關(guān)系和主題-詞關(guān)系進行建模,最終得到術(shù)語文本的高維主題向量;對于通過基因本體的定義信息得到的術(shù)語短文本信息,SSGTBTM模型對整個術(shù)語語料庫中的詞對進行建模,最終得到術(shù)語文本的高維主題向量。3.實現(xiàn)SSGTLDA和SSGTBTM兩種基因語義相似度計算方法,并分別在術(shù)語對和蛋白質(zhì)對兩種數(shù)據(jù)集上進行實驗。實驗結(jié)果表明本文提出的兩種算法均具有較好的效果。
[Abstract]:In recent years, when biologists discover unknown genes, they are often compared with known genes. Then according to the similarity between the two to infer the characteristics of unknown genes. Biologists compare the sequence or structure of genes by comparison algorithm. And then find out the similar or related genes. But the study shows that the functional similarity or related genes are not necessarily very relevant in the sequence. In view of the above problems. The current mainstream method is to analyze and predict the characteristics of unknown genes by calculating the semantic similarity between the terms annotated in the gene body. However, such methods use only the association of terms in the gene body. The lines indirectly reflect the semantic similarity of genes. In this paper, a gene semantic similarity algorithm based on topic model is proposed to extract the intrinsic semantic information from the text representing the terms. To some extent, the shortcomings of the traditional methods are solved. This paper mainly has three innovations: 1.When calculating the similarity between terms pairs, we mine the potential semantic information from the terms themselves. Then the text representing the semantic information of terms is transformed into a high-dimensional topic vector. Thus, the similarity between terms is transformed into the similarity between the high-dimensional subject vectors representing the terms. 2. Two models, SSGTLDA and SSGTBTM, are proposed. For the term long text information obtained through the Google search engine. SSGTLDA model models the text-topic relationship and subject-word relationship, and finally gets the high-dimensional topic vector of the terminology text. For the term short text information obtained from the definition information of the gene ontology, the term pairs in the whole term corpus are modeled by the SSGTBTM model. Finally, the high-dimensional topic vector. 3 of the terminology text is obtained. Two methods of gene semantic similarity calculation, SSGTLDA and SSGTBTM, are implemented. The experiments are carried out on two kinds of data sets: term pair and protein pair. The experimental results show that the two algorithms proposed in this paper have good results.
【學(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.1

【參考文獻】

相關(guān)期刊論文 前5條

1 夏利玲;;淺談中文分詞技術(shù)[J];科技資訊;2011年32期

2 ;A measure of semantic similarity between gene ontology terms based on semantic pathway covering[J];Progress in Natural Science;2006年07期

3 李榮;曹順良;李園園;譚灝;朱揚勇;鐘揚;李亦學(xué);;基于語義路徑覆蓋的Gene Ontology術(shù)語間語義相似性度量方法[J];自然科學(xué)進展;2006年07期

4 張春霆;生物信息學(xué)的現(xiàn)狀與展望[J];世界科技研究與發(fā)展;2000年06期

5 解濤,梁衛(wèi)平,丁達夫;后基因組時代的基因組功能注釋[J];生物化學(xué)與生物物理進展;2000年02期

，

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