Please wait a minute...
Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (8): 98-106    DOI: 10.11925/infotech.2096-3467.2018.0142
Current Issue | Archive | Adv Search |
Finding Association Between Diseases and Genes from Literature Abstracts
Mu Dongmei(), Jin Shan, Ju Yuanhong
School of Public Health, Jilin University, Changchun 130021, China
Download: PDF (619 KB)   HTML ( 4
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This study tries to find association between genes and diseases from literature abstracts, aiming to provide evidence for the prevention and treatment of diseases. [Methods] First, we established the entity extraction rules with the help of recognition techniques based on thesaurus. Then, we proposed a model to discover the association between disease and gene entities. Finally, we validated the new model with abstracts of diabete nephropathy studies. [Results] A total of 656 diabetic nephropathy associated genes were obtained, which included high frequency, mid frequency and low frequency genes. [Limitations] More research is needed to explore other diabete complications with the proposed model. [Conclusions] (I)The high frequency associated genes of disease are possibly the theoretical foundations of current research. (II)Intermediate frequency associated genes are the focus of current research. (III) Low frequency associated genes could become new fields for knowledge discovery.

Key wordsEntity Recognition      Information Extraction      Cluster Analysis      Genes Association Relationship     
Received: 02 February 2018      Published: 08 September 2018
ZTFLH:  G350  

Cite this article:

Mu Dongmei,Jin Shan,Ju Yuanhong. Finding Association Between Diseases and Genes from Literature Abstracts. Data Analysis and Knowledge Discovery, 2018, 2(8): 98-106.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0142     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I8/98

序号 ID 基因符号 频次
1 618 APOL1 26
2 12679 VDR 19
3 333 AGT 18
4 1662 CD34 17
5 11850 TLR4 15
6 634 AQP2 14
7 2500 CTGF 14
8 6769 SMAD3 14
9 14929 SIRT1 12
10 613 APOE 11
PMID 涉及内容
27054572 非裔人群中APOL1肾风险变异与终末期肾脏病密切相关
26776194 终末期肾病的非裔美国人的基因变异与邻近的APOL1
强连锁不平衡密切相关
26668025 APOL1的风险等位基因与HIV阳性黑人的肾病和局灶性节段性肾小球硬化症关联
26343748 非裔美国人APOL1肾病危险变异体呈常染色体隐性遗传模式
26215860 APOL1的遗传变异引起慢性终末期肾脏疾病的风险
26180129 APOL1基因变异与黑人肾脏疾病相关
26150607 APOL1变异与黑人肾脏表型相关
26112018 非裔人群中APOL1变异与慢性肾脏疾病关联
25573908 APOL1变异引起肾脏疾病的风险
25549121 非裔美国人群中APOL1与慢性肾病和终末期肾脏疾病的发病率增加相关
基因名称 PMID 涉及内容
TLR4 26568190 TLR4可诱导多种炎性细胞因子的生产, 因此认为TLR4有益于治疗糖尿病肾病。
TLR4 26497229 发现维生素D3在糖尿病肾病尿毒症的炎症免疫反应与TLR4相关。
TLR4 26398934 发现TLR4的内源性配体和核因子-kb启动子活性在糖尿病小鼠肾脏中明显升高。
SMAD3 26449625 发现尿SMAD3与肾小球滤过率双相变化显著相关, 可作为一种新型的标志物筛查2型糖尿病患者的肾病。
SMAD3 26052839 发现灭活TGF-β/SMAD3的通路引起细胞外基质蛋白降低, 进而抑制糖尿病肾病。
SMAD3 26041445 发现高血糖增加SMAD3磷酸化, 进而导致肾功能衰竭。
SIRT1 27470548 发现SIRT1/p53轴在高血糖条件下可诱导肾近曲小管上皮细胞凋亡。
SIRT1 26588494 综述了SIRT1在糖尿病肾病中的作用机制。
SIRT1 25386563 SIRT1可抑制肾细胞, 它的激活可能成为糖尿病肾病的新的治疗靶点。
基因名称 PMID 涉及内容
ABCA1 26379423 胆固醇紊乱可致糖尿病肾病, 实验发现花青素加强ABCA1表达和胆固醇流出。
ABCA1 25181357 发现ABCA1表达降低可能促进胆固醇的积累, 可能促进糖尿病肾病的发展。
RhoA 25641678 发现PTEN引起RhoA活化失衡, 引起足细胞损伤, 进而可促进糖尿病肾病的发展。
RhoA 24925721 SMPDL3b的表达增加RhoA的活性, 使足细胞更易发生凋亡, 促进糖尿病肾病的发展。
CCR2 27926736 CCR2及其配体与糖尿病肾病相关。
CCR2 26268910 实验数据表明CCR2在2型糖尿病肾病患者治疗中具有肾脏保护作用。
CYP11B2 27009287 研究表明CYP11B2基因多态性可能有助于糖尿病肾病的发展, 尤其是在亚洲人群中。
CYP11B2 25957425 发现CYP11B2基因多态性可能对糖尿病患者的慢性肾病发展产生影响。
[1] 王郝日钦. 深度学习在文本挖掘中的应用研究[D]. 通辽: 内蒙古民族大学, 2015.
[1] (Wang Haoriqin.Application of Deep Learning in Text Mining [D]. Tongliao: Inner Mongolia University for Nationalities, 2015.)
[2] 吴潇泽. 科技文献趋势挖掘技术研究[D]. 杭州: 杭州电子科技大学, 2010.
[2] (Wu Xiaoze.Research of Scientific Literature Trend Mining Technology [D]. Hangzhou: Hangzhou Dianzi University, 2010.)
[3] 郑玲, 刘秋爽, 金晶, 等. 糖尿病并发症治疗靶点的研究进展[J]. 海峡药学, 2014, 26(1):13-17.
[3] (Zheng Ling, Liu Qiushuang, Jin Jing, et al.Research Progress on the Target of Diabetic Complications Treatment[J]. Strait Pharmaceutical Journal, 2014, 26(1): 13-17.)
[4] 李英, 唐英琪. 载脂蛋白E(ApoE)基因多态性与2型糖尿病(T2DM)血脂及其脑梗死并发症的关系研究[J]. 中外医疗, 2014(31): 37-38,41.
[4] (Li Ying, Tang Yingqi.Study of the Relationship Between Apolipoprotein E (ApoE) Gene Polymorphism, Type 2 Diabetes (T2DM) Blood Lipid and Cerebral Infarction Complication[J]. China & Foreign Medical Treatment, 2014(31): 37-38,41.)
[5] 许慧宁, 代青湘. PON2 Cys311Ser 基因多态性与高原老年糖尿病并发症的关系[J]. 世界最新医学信息文摘:电子版, 2014(21):11,13.
doi: 10.3969/j.issn.1671-3141.2014.21.005
[5] (Xu Huining, Dai Qingxiang. Relationship Between PON2 Cys311Ser Gene Polymorphism and Elderly Diabetic Complications in Plateau [J]. World Latest Medicine Information, 2014(21):11,13.)
doi: 10.3969/j.issn.1671-3141.2014.21.005
[6] 唐珊珊. Irisin及其基因多态性与中国人2型糖尿病相关临床性状及微血管并发症的关系[D]. 上海: 上海交通大学, 2015.
[6] (Tang Shanshan.Association of Irisin and Its Genetic Variants with Type 2 Diabetes-related Traits and Microvascular Complications in the Chinese Population [D]. Shanghai: Shanghai Jiaotong University, 2015.)
[7] 徐哲奕. 2型糖尿病大血管并发症中血管平滑肌细胞增殖相关基因DNA甲基化的作用研究[D]. 武汉: 华中科技大学, 2014.
[7] (Xu Zheyi.Study on DNA Methylation Alteration of Vascular Smooth Muscle Cell Proliferation Related Genes in Type 2 Diabetic Macrovascular Complications [D]. Wuhan: Huazhong University of Science and Technology, 2014.)
[8] 余翠, 熊钱颖, 王李卓, 等. 糖尿病肾病的发病机制及治疗进展[J]. 医学综述, 2015, 21(21): 3944-3947.
[8] (Yu Cui, Xiong Qianying, Wang Lizhuo, et al.Recent Progress in the Pathogenesis of Diabetic Nephropathy and Its Treatment[J]. Medical Recapitulate, 2015, 21(21): 3944-3947.)
[9] 张晓艳, 王挺, 陈火旺. 命名实体识别研究[J]. 计算机科学, 2005, 32(4): 44-48.
[9] (Zhang Xiaoyan, Wang Ting, Chen Huowang.Research on Named Entity Recognition[J]. Computer Science, 2005, 32(4): 44-48.)
[10] Karadeniz İ, Özgür A.Detection and Categorization of Bacteria Habitats Using Shallow Linguistic Analysis[J]. BMC Bioinformatics, 2015, 16(S10): S5.
doi: 10.1186/1471-2105-16-S10-S5 pmid: 4511461
[11] Yimam S M, Biemann C, Majnaric L, et al.An Adaptive Annotation Approach for Biomedical Entity and Relation Recognition[J]. Brain Informatics, 2016, 3(3): 157-168.
doi: 10.1007/s40708-016-0036-4 pmid: 4999566
[12] Lin W, Ji D, Lu Y.Disorder Recognition in Clinical Texts Using Multi-label Structured SVM[J]. BMC Bioinformatics, 2017, 18: 75.
doi: 10.1186/s12859-017-1476-4 pmid: 5282630
[13] 舒刚. 基于生物医学文本挖掘技术的天然产物的靶标蛋白预测[D]. 上海: 复旦大学, 2012.
[13] (Shu Gang.Prediction on Target Protein of Natural Products Based on Biomedical Text Mining Technology [D]. Shanghai: Fudan University, 2012.)
[14] 李保利, 陈玉忠, 俞士汶. 信息抽取研究综述[J]. 计算机工程与应用, 2003, 39(10):1-5.
[14] (Li Baoli, Chen Yuzhong, Yu Shiwen.Research on Information Extraction: A Survey[J]. Computer Engineering and Applications, 2003, 39(10): 1-5.)
[15] 方福德. 人类基因的命名和书写[J]. 基础医学与临床, 2010(10). DOI: 10.16352/j.issn.1001-6325.2010.10.001.
[15] (Fang Fude. Nomenclature and Writing of Human Gene [J]. Basic & Clinical Medicine, 2010(10). DOI:10.16352/j.issn.1001-6325.2010.10.001.)
[16] HGNC [EB/OL]. [2017-05-20].
[17] 尚美辰. 基于UMLS和通路数据的潜在语义分析技术的研究与实现[D]. 哈尔滨:黑龙江大学, 2015.
[17] (Shang Meichen.Research and Implementation of Latent Semantic Analysis Technology Based on UMLS and Path Data [D]. Harbin: Heilongjiang University, 2015.)
[18] 肖袁. 基于DOM4J的XML文档解析技术[J]. 科技信息, 2011(2): 229-230.
[18] (Xiao Yuan.XML Document Parsing Technology Based on DOM4J[J]. Science & Technology Information, 2011(2): 229-230.)
[19] 杨宏新, 毛培春, 孟林, 等. 19份高燕麦草种质材料苗期抗旱性评价[J]. 干旱地区农业研究, 2011, 29(2): 6-14.
[19] (Yang Hongxin, Mao Peichun, Meng Lin, et al.Assessment of Drought Resistance for 19 Germplasm and Materials of Arrhenatherum Elatius at the Seedling Stage[J]. Agricultural Research in the Arid Areas, 2011, 29(2): 6-14.)
[20] Amberger J, Bocchini C, Hamosh A.A New Face and New Challenges for Online Mendelian Inheritance in Man (OMIM®)[J]. Human Mutation, 2011, 32(5): 564-567.
doi: 10.1002/humu.21466 pmid: 21472891
[21] OMIM [EB/OL]. [2017-07-05].
[22] Dorr C R, Freedman B I, Hicks P J, et al.Deceased-Donor Apolipoprotein L1 Renal-Risk Variants Have Minimal Effects on Liver Transplant Outcomes[J]. PLoS One, 2016, 11(4): e0152775.
doi: 10.1371/journal.pone.0152775
[23] Dummer P D, Limou S, Rosenberg A Z, et al.APOL1 Kidney Disease Risk Variants: An Evolving Landscape[J]. Seminars in Nephrology, 2015, 35(3): 222-236.
doi: 10.1016/j.semnephrol.2015.04.008 pmid: 26215860
[24] Dollerup P, Thomsen T M, Nejsum L N, et al.Partial Nephrogenic Diabetes Insipidus Caused by a Novel AQP2 Variation Impairing Trafficking of the Aquaporin-2 Water Channel[J]. BMC Nephrology, 2015, 16(1): 217.
doi: 10.1186/s12882-015-0213-3
[25] Guo K, Lu J, Kou J, et al.Increased Urinary Smad3 is Significantly Correlated with Glomerular Hyperfiltration and a Reduced Glomerular Filtration Rate and is a New Urinary Biomarker for Diabetic Nephropathy[J]. BMC Nephrology, 2015, 16(1): 159.
doi: 10.1186/s12882-015-0156-8
[26] Tsun J G S, Yung S, Chau M K M, et al. Cellular Cholesterol Transport Proteins in Diabetic Nephropathy[J]. PLoS One, 2014, 9(9): e105787.
doi: 10.1371/journal.pone.0105787 pmid: 4152117
[27] Xu H, Wang X, Liu M, et al.Association of Aldosterone Synthase (CYP11B2) -344 T/C Polymorphism with Diabetic Nephropathy: A Meta-analysis[J]. Journal of the Renin-Angiotensin-Aldosterone System: JRAAS, 2016, 17(1): 1470320316633896.
[1] Gao Yuan,Shi Yuanlei,Zhang Lei,Cao Tianyi,Feng Jun. Reconstructing Tour Routes Based on Travel Notes[J]. 数据分析与知识发现, 2020, 4(2/3): 165-172.
[2] Ma Jianxia,Yuan Hui,Jiang Xiang. Extracting Name Entities from Ecological Restoration Literature with Bi-LSTM+CRF[J]. 数据分析与知识发现, 2020, 4(2/3): 78-88.
[3] Han Huang,Hongyu Wang,Xiaoguang Wang. Automatic Recognizing Legal Terminologies with Active Learning and Conditional Random Field Model[J]. 数据分析与知识发现, 2019, 3(6): 66-74.
[4] Zhiqiang Liu,Yuncheng Du,Shuicai Shi. Extraction of Key Information in Web News Based on Improved Hidden Markov Model[J]. 数据分析与知识发现, 2019, 3(3): 120-128.
[5] Meishan Chen,Chenxi Xia. Identifying Entities of Online Questions from Cancer Patients Based on Transfer Learning[J]. 数据分析与知识发现, 2019, 3(12): 61-69.
[6] Chengzhi Zhang,Zheng Li. Extracting Sentences of Research Originality from Full Text Academic Articles[J]. 数据分析与知识发现, 2019, 3(10): 12-18.
[7] Lianjie Xiao,Tao Meng,Wei Wang,Zhixiang Wu. Entity Recognition of Intelligence Method Based on Deep Learning: Taking Area of Security Intelligence for Example[J]. 数据分析与知识发现, 2019, 3(10): 20-28.
[8] Li Yu,Li Qian,Changlei Fu,Huaming Zhao. Extracting Fine-grained Knowledge Units from Texts with Deep Learning[J]. 数据分析与知识发现, 2019, 3(1): 38-45.
[9] Tang Huihui,Wang Hao,Zhang Zixuan,Wang Xueying. Extracting Names of Historical Events Based on Chinese Character Tags[J]. 数据分析与知识发现, 2018, 2(7): 89-100.
[10] Fan Xinyue,Cui Lei. Using Text Mining to Discover Drug Side Effects: Case Study of PubMed[J]. 数据分析与知识发现, 2018, 2(3): 79-86.
[11] He Yue,Wang Aixin,Feng Yue,Wang Li. Optimizing Layouts of Outpatient Pharmacy Based on Association Rules[J]. 数据分析与知识发现, 2018, 2(1): 99-108.
[12] Chen Runwen,Qiu Yong,Huang Wenbin,Wang Jun. Analyzing Private College Students’ Online Lifestyle with Web-logs[J]. 数据分析与知识发现, 2017, 1(8): 31-38.
[13] Wang Xueying,Zhang Zixuan,Wang Hao,Deng Sanhong. Evaluating Brands of Agriculture Products: A Literature Review[J]. 数据分析与知识发现, 2017, 1(7): 13-21.
[14] Cui Jiawang,Li Chunwang. Identifying Semantic Relations of Clusters Based on Linked Data[J]. 数据分析与知识发现, 2017, 1(4): 57-66.
[15] Sui Mingshuang,Cui Lei. Extracting Chemical and Disease Named Entities with Multiple-Feature CRF Model[J]. 现代图书情报技术, 2016, 32(10): 91-97.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn