|本期目录/Table of Contents|

[1]刘 浩,曾兴兴,鲁艳柳.生物医学数据分析方法与应用[J].遵义医科大学学报,2019,42(05):607-612.
 Liu Hao,Zeng Xingxing,Lu Yanliu.Analysis methods and applications of biological and medical data[J].Journal of Zunyi Medical University,2019,42(05):607-612.
点击复制

生物医学数据分析方法与应用()
     
分享到:

《遵义医科大学学报》[ISSN:1000-9035/CN:22-1262/O4]

卷:
第42卷
期数:
2019年05期
页码:
607-612
栏目:
综述
出版日期:
2019-10-25

文章信息/Info

Title:
Analysis methods and applications of biological and medical data
文章编号:
1000-2715(2019)05-0607-06
作者:
刘 浩曾兴兴鲁艳柳
(遵义医科大学 基础药理教育部重点实验室暨特色民族药教育部国际合作联合实验室,贵州 遵义 563099)
Author(s):
Liu HaoZeng XingxingLu Yanliu
(Key Laboratory of Basic Pharmacology of Ministry of Education and Joint International Research Laboratory of Ethnomedicine of Ministry of Education,Zunyi Medical University,Zunyi Guizhou 563099,China)
关键词:
大数据 生物医学数据 数据分析方法
Keywords:
big data biological and medical data data analysis methods
分类号:
R965.2
DOI:
-
文献标志码:
A
摘要:
随着高通量组学技术的迅速发展,生物医学迎来了大数据的时代。实验数据展现出了爆发式的增长并且在不断地积累,生物医学和大数据之间的结合产生了生物医学数据。生物医学数据的累积,为生物医学的研究带来新的发展机遇,与此同时也对传统的数据分析方法提出了极大的挑战。本文将对生物医学数据的分析方法及其应用进行综述,从而更好地推进“大数据”在生物医学研究中的应用。
Abstract:
With the development of high-throughput omics technologies,the accumulation of research data is rapid.Biomedicine is accelerating into the era of big data.The combination of biomedicine and big data produces biological and medical data.The biomedical data not only provides unprecedented opportunities for biomedicine,but also challenges the traditional data analysis technology.In this review,recent studies on analysis methods and applications of biomedical data were summarized to promote the development of biomedicine.

参考文献/References:

[1] Kodama Y,Shumway M,Leinonen R,et al.The sequence read archive:explosive growth of sequencing data[J].Nucleic Acids Research,2012,40(Database issue):54-56. [2] 郭丽,胡栋,王俊,等.生物医学数据背景下学习生物信息学的学科特点分析[J].高教学刊,2016,91(19):48-49. [3] 潘霞.系统水平整合分子数据及表型数据重构罕见疾病分类体系[D].上海:华东师范大学,2018. [4] Miller J B,Shan G,Lombardo J,et al.Biomedical informatics applications for precision management of neurodegenerative diseases[J].Alzheimer's & Dementia:Translational Research & Clinical Interventions,2018,4:357-365. [5] Costa F F.Big data in biomedicine[J].Drug Discovery Today,2014,19(4):433-440. [6] 李艳明,杨亚东,张昭军,等.精准医学大数据的分析与共享[J].中国医学前沿杂志:电子版,2015,7(6):4-10. [7] Auton A,Abecasis G R,Altshuler D,et al.A global reference for human genetic variation[J].Nature,2015,526(7571):68-74. [8] 王波,吕筠,李立明.生物医学大数据:现状与展望[J].中华流行病学杂志,2014,35(6):617-620. [9] Feingold E A,Good P J,Guyer M S,et al.The ENCODE(ENCyclopedia of DNA Elements)Project[J].Science,2004,306(5696):636-640. [10]Chadwick L H.The NIH roadmap epigenomics program data resource[J].Epigenomics,2012,4(3):317-324. [11]Duan Q,Flynn C,Niepel M,et al.LINCS canvas browser:interactive web app to query,browse and interrogate LINCS L1000 gene expression signatures[J].Nucleic Acids Research,2014,42(W1):449-460. [12]Barrett T,Troup D B,Wilhite S E,et al.NCBI GEO:archive for functional genomics data sets-10 years on[J].Nucleic Acids Research,2011,39(Database issue):1005-1010. [13]Tomczak K,Patrycja Czerwids Research,2011BI.Review the cancer genome Atlas(TCGA):an immeasurable source of knowledge[J].Contemporary Oncology/Wspeview The Cancer Genome Atlas,2015,19(1A):68–77. [14]路东方,许俊富,项超娟,等.生物大数据中的聚类方法分析[J].上海大学学报(自然科学版),2016,22(1):45-57. [15]陈立宏.生物信息学在基因组学研究中的应用[D].北京:清华大学,2002. [16]李胜,张爱萍,贺林.中国精神分裂症的全基因组关联分析及其转化医学进展[J].中国科学:生命科学,2013,43(1):31-38. [17]宁康,陈挺.生物医学大数据的现状与展望[J].科学通报,2015,60(5-6):534-546. [18]Theodoulou F L,Kerr I D.ABC transporter research:going strong 40 years on[J].Biochemical Society Transactions,2015,43(5):1033-1040. [19]Zhengbradley X,Flicek P.Applications of the 1000 Genomes Project resources[J].Briefings in Functional Genomics,2016,16(3):163-170. [20]Fishilevich S,Zimmerman S,Kohn A,et al.Genic insights from integrated human proteomics in GeneCards[J].Database,2016,2016:Baw030. [21]Yang J,Li J,Jiang S,et al.ChIPBase:a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data[J].Nucleic Acids Research,2013,41(Database issue):177-187. [22]Harrow J,Frankish A,Gonzalez J M,et al.GENCODE:The reference human genome annotation for The ENCODE Project[J].Genome Research,2012,22(9):1760-1774. [23]Dawson N L,Lewis T E,Das S,et al.CATH:an expanded resource to predict protein function through structure and sequence[J].Nucleic Acids Research,2017,45(D1):289-295. [24]Hulo N,Bairoch A M,Bulliard V,et al.The PROSITE database[J].Nucleic Acids Research,2006,34(90001):227-230. [25]Hemmateenejad B,Miri R,Elyasi M,et al.A segmented principal component analysis--regression approach to QSAR study of peptides[J].Journal of Theoretical Biology,2012,305:37-44. [26]龚著琳,陈瑛,苏懿,等.数据挖掘在生物医学数据分析中的应用[J].上海交通大学学报(医学版),2010,30(11):1420-1423. [27]Candes E J,Li X,Ma Y,et al.Robust principal component analysis[J].Journal of the ACM,2011,58(3):1-37. [28]Huang Q,Tao D,Li X,et al.Exploiting local coherent patterns for unsupervised feature ranking[J].IEEE Transactions on Systems,Man,and Cybernetics.Part B,Cybernetics:a publication of the IEEE Systems,Man,and Cybernetics Society,2011,41(6):1471-1482. [29]Li X,Pang Y,Yuan Y.L1-Norm-Based 2DPCA[J].IEEE Transactions on Systems,Man,and Cybernetics.Part B,Cybernetics:a publication of the IEEE Systems,Man,and Cybernetics Society,2010,40(4):1170-1175. [30]陈涛.基于主成分分析和模糊识别的生物分子太赫兹光谱识别[J].量子电子学报,2016,33(4):392-398. [31]Saeys Y,Inza I,Larranaga P,et al.A review of feature selection techniques in bioinformatics[J].Bioinformatics,2007,23(19):2507-2517. [32]Chin T,Suter D.Incremental kernel principal component analysis[J].IEEE Transactions on Image Processing,2007,16(6):1662-1674. [33]Christie O H.Introduction to multivariate methodology,an alternative way?[J].Chemometrics and Intelligent Laboratory Systems,1995,29(2):177-188. [34]孔晶.高脂饮食对C57BL/6J小鼠肝脏胆汁酸调控通路基因表达的影响[D].遵义:遵义医科大学,2018. [35]李欣,刘万军.回归分析数据挖掘技术[J].海军航空工程学院学报,2006,21(3):386-388. [36]Schneider A,Hommel G,Blettner M,et al.Linear regression analysis:part 14 of a series on evaluation of scientific publications[J].Deutsches Arzteblatt International,2010,107(44):776-782. [37]Sperandei S.Understanding logistic regression analysis[J].Biochemia Medica,2014,24(1):12-18. [38]苗立志.基于Spark和随机森林的乳腺癌风险预测分析[J].计算机技术与发展,2019,29(8):142-144. [39]许晓丽.基于聚类分析的图像分割算法研究[D].哈尔滨:哈尔滨工程大学,2012. [40]Anwar H,Qamar U,Qureshi A W,et al.Global optimization ensemble model for classification methods[J].The Scientific World Journal,2014,2014:313164.doi:10.1155/2014/313164. [41]In J G,Foulkeabel J,Estes M K,et al.Human mini-guts:new insights into intestinal physiology and host–pathogen interactions[J].Nature Reviews Gastroenterology & Hepatology,2016,13(11):633-642. [42]杜婧.基于贝叶斯方法的乳腺癌预后分析[J].计算机工程与应用,2009,39(3):1-8. [43]Boulesteix A,Strimmer K.Partial least squares:a versatile tool for the analysis of high-dimensional genomic data[J].Briefings in Bioinformatics,2006,8(1):32-44. [44]Zhang T,Wu X,Ke C,et al.Identification of potential biomarkers for ovarian cancer by urinary metabolomic profiling[J].Journal of Proteome Research,2013,12(1):505-512. [45]吴静,杨睿,张磊,等.基于液相色谱-质谱技术的乳腺癌转移相关代谢标志物的筛选[J].天津医药,2018,46(10):7-12. [46]方开泰,潘恩沛.聚类分析[M].北京:地质出版社,1982. [47]Aggarwal C C,Reddy C K.Data clustering:algorithms and applications[M].Data Clustering:Algorithms and Applications.Chapman & Hall/CRC,2013. [48]苏志中.聚类分析研究及其在生物数据分析中的应用[D].长沙:湖南大学,2009. [49]Wang M,Zhang W,Ding W,et al.Parallel clustering algorithm for large-scale biological data sets[J].Plos One,2014,9(4):e91315. [50]蒋红卫,夏结来.偏最小二乘回归及其应用[J].第四军医大学学报,2003,24(3):280-283. [51]Xuan Q,Hu C,Yu D,et al.Development of a high coverage pseudotargeted lipidomics method based on ultra-high performance liquid chromatography-mass spectrometry[J].Analytical Chemistry,2018,90(12):7608-7616. [52]张蕾,张琪,游云,等.基于代谢组学技术探讨高脂血症及动脉粥样硬化痰瘀证候的演变规律[J].中国中西医结合杂志,2015,35(7):823-833. [53]Li Z,Guan M,Lin Y,et al.Aberrant lipid metabolism in hepatocellular carcinoma revealed by liver lipidomics[J].International Journal of Molecular Sciences,2017,18(12):E2550. [54]Ishikawa M,Saito K,Yamada H,et al.Plasma lipid profiling of different types of hepatic fibrosis induced by carbon tetrachloride and lomustine in rats[J].Lipids in Health and Disease,2016,15(1):74. [55]焦京.基于蛋白质组学策略的脓毒症生物标志物研究[D].长沙:中南大学,2014. [56]王晓东.基于蛋白质组学的血管细胞力学信号转导与基因调控网络[D].上海:上海交通大学,2014. [57]陈磊,刘毅慧.基于CART算法的肺癌微阵列数据的分类[J].生物信息学,2011,9(3):229-234. [58]梁君雅.控制混杂的随机森林方法评价及其在高维组学数据分析中的应用[D].南京:南京医科大学,2018. [59]王欢.基于RNA-seq技术的乙肝相关肝硬化肝细胞转录组学研究[D].长春:吉林大学,2017. [60]Sancesario G M,Bernardini S.Alzheimer's disease in the omics era[J].Clinical Biochemistry,2018,59:9-16. [61]刘华,王永庆.数据挖掘工具SAS Enterprise Miner进展研究[J].信息系统工程,2013(8):149-150. [62]Zeng C,Jiang Y,Zheng L,et al.FIU-Miner:a fast,integrated,and user-friendly system for data mining in distributed environment[C].Knowledge Discovery and Data Mining,2013:1506-1509. [63]Argandacarreras I,Kaynig V,Rueden C T,et al.Trainable weka segmentation:A machine learning tool for microscopy pixel classification[J].Bioinformatics,2017,33(15):2424-2426. [64]Wallach D,David M,James W,et al.The R programming language and software[C].Academic Press,2019:45-95. [65]方匡南.基于数据挖掘的分类和聚类算法研究及R语言实现[D].广州:暨南大学,2007.

相似文献/References:

备注/Memo

备注/Memo:
[基金项目]国家自然科学基金资助项目(NO:81660685,81560673); 贵州省科技重大专项(NO:黔科合重大专项字[2015]6010); 贵州省科学技术基金项目(NO:黔科合JZ字[2015]2010,黔科合J字[2015]2158); 贵州省教育厅自然科学研究项目(NO:黔教合KY字[2015]373); 遵义医学院研究生创新计划项目(NO:ZYK018)。 [通信作者]鲁艳柳,女,博士,教授,硕士生导师,研究方向:药物代谢与毒理学, E-mail:Yanliu.lu@foxmail.c
更新日期/Last Update: 2019-10-25