Prediction of Synthetic Lethality by Literature Mining: Case Studies of Colon Cancer

論文翻譯標題: 藉由文獻探勘預測合成致死關係:大腸癌的個案探討
  • 簡 名昱

學生論文: Master's Thesis


Synthetic lethality (SL) is an interaction between two genes which means the cell will be alive if one of these two genes is disabled but dead if both genes are disabled SL was discovered in 1946 but become more popular recently because there are some cancer therapies based on this interaction Due to this special interaction we can make cancer cells lethal but normal cells alive by targeting the synthetic lethal pair of the mutant gene in cancer However it costs a lot to find an SL using traditional experiments Therefore we propose a new SL prediction system to easily predict some potential SL in silico In this study we design an SL prediction system based on a text-mining method and an inference model First we extend some essential genes using the text-mining method Compared with mutant genes there are only a few essential genes in a few cancers recorded in databases With these potential essential genes extended using the text-mining method we can predict more SL not restricted by scant essential genes Second we predict SL from mutant genes and extend essential genes and filter wrong gene pairs using gene co-expression and the co-occurrence of two genes in the literature We also compare our prediction system with experimental screening data Recently there has been a lot of SL discovered through a few screening experiments but it is hard for biological researchers to find more important SL in these screening data Through our prediction system biological researchers can find potential SL more easily We also study some cases of colon cancer and find some information that reveals some novel potential SL might be valid This research predicts potential SL by combining a text-mining method and gene data We extract more essential genes from literature mining to complement the lack of essential gene data We then predict some potential SL using an inference model and compare the results with screening data to allow biological researchers find interesting SL quickly
獎項日期2016 8月 11
監督員Jung-Hsien Chiang (Supervisor)