研究生学习总结_张璇

xuanzhang

在此，总结研究生期间的学习内容和科研成果。

一、计算机专业书籍阅读
《数据挖掘导论》、《机器学习》、《算法》、《数学之美》、《编程珠玑》、《统计学习方法》、《R语言实战》。

二、科研论文阅读
在完成第一篇综述期间精读不少于40篇论文，其中包括：
11. Li Y, Qiu C, Tu J, Geng B, Yang J, Jiang T, Cui Q. HMDD v2. 0: a database for experimentally supported human microRNA and disease associations. Nucleic acids research 2013:gkt1023.
12. Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, Li M, Wang G, Liu Y. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic acids research 2009; 37(suppl 1):D98-D104.
13. Xie B, Ding Q, Han H, Wu D. miRCancer: a microRNA–cancer association database constructed by text mining on literature. Bioinformatics 2013:btt014.
14. Yang Z, Ren F, Liu C, He S, Sun G, Gao Q, Yao L, Zhang Y, Miao R, Cao Y. dbDEMC: a database of differentially expressed miRNAs in human cancers. BMC genomics 2010; 11(Suppl 4):S5.
15. Goh K-I, Cusick ME, Valle D, Childs B, Vidal M, Barabási A-L. The human disease network. Proceedings of the National Academy of Sciences 2007; 104(21):8685-8690.
16. van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JA. A text-mining analysis of the human phenome. European journal of human genetics 2006; 14(5):535-542.
17. Wang D, Wang J, Lu M, Song F, Cui Q. Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics 2010; 26(13):1644-1650.
18. Hsu S-D, Chu C-H, Tsou A-P, Chen S-J, Chen H-C, Hsu PW-C, Wong Y-H, Chen Y-H, Chen G-H, Huang H-D. miRNAMap 2.0: genomic maps of microRNAs in metazoan genomes. Nucleic acids research 2008; 36(suppl 1):D165-D169.
19. Sethupathy P, Corda B, Hatzigeorgiou AG. TarBase: A comprehensive database of experimentally supported animal microRNA targets. Rna 2006; 12(2):192-197.
20. Betel D, Wilson M, Gabow A, Marks DS, Sander C. The microRNA. org resource: targets and expression. Nucleic acids research 2008; 36(suppl 1):D149-D153.
21. Griffiths-Jones S, Grocock RJ, Van Dongen S, Bateman A, Enright AJ. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic acids research 2006; 34(suppl 1):D140-D144.
22. Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E. The role of site accessibility in microRNA target recognition. Nature genetics 2007; 39(10):1278-1284.
23. Nam S, Kim B, Shin S, Lee S. miRGator: an integrated system for functional annotation of microRNAs. Nucleic acids research 2008; 36(suppl 1):D159-D164.
24. Megraw M, Sethupathy P, Corda B, Hatzigeorgiou AG. miRGen: a database for the study of animal microRNA genomic organization and function. Nucleic acids research 2007; 35(suppl 1):D149-D155.
25. Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M. Combinatorial microRNA target predictions. Nature genetics 2005; 37(5):495-500.
26. Grimson A, Farh KK-H, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Molecular cell 2007; 27(1):91-105.
27. Maragkakis M, Reczko M, Simossis VA, Alexiou P, Papadopoulos GL, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K. DIANA-microT web server: elucidating microRNA functions through target prediction. Nucleic acids research 2009:gkp292.
28. Krüger J, Rehmsmeier M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic acids research 2006; 34(suppl 2):W451-W454.
29. Miranda KC, Huynh T, Tay Y, Ang Y-S, Tam W-L, Thomson AM, Lim B, Rigoutsos I. A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 2006; 126(6):1203-1217.
30. Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR. Rfam: an RNA family database. Nucleic acids research 2003; 31(1):439-441.
31. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic acids research 2005; 33(suppl 1):D514-D517.
32. Becker KG, Barnes KC, Bright TJ, Wang SA. The genetic association database. Nature genetics 2004; 36(5):431-432.
33. Li X, Li C, Shang D, Li J, Han J, Miao Y, Wang Y, Wang Q, Li W, Wu C. The implications of relationships between human diseases and metabolic subpathways. PloS one 2011; 6(6):e21131.
34. Prasad TK, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A. Human protein reference database—2009 update. Nucleic acids research 2009; 37(suppl 1):D767-D772.
35. Mørk S, Pletscher-Frankild S, Caro AP, Gorodkin J, Jensen LJ. Protein-driven inference of miRNA–disease associations. Bioinformatics 2013:btt677.
36. Lu M, Zhang Q, Deng M, Miao J, Guo Y, Gao W, Cui Q. An analysis of human microRNA and disease associations. PloS one 2008; 3(10):e3420.
37. Jiang Q, Hao Y, Wang G, Juan L, Zhang T, Teng M, Liu Y, Wang Y. Prioritization of disease microRNAs through a human phenome-microRNAome network. BMC Systems Biology 2010; 4(Suppl 1):S2.
38. Jiang Q, Wang G, Wang Y: An approach for prioritizing disease-related microRNAs based on genomic data integration. In: Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on: 2010. IEEE: 2270-2274.
39. Chen X, Liu M-X, Yan G-Y. RWRMDA: predicting novel human microRNA–disease associations. Mol BioSyst 2012; 8(10):2792-2798.
40. Chen H, Zhang Z. Similarity-based methods for potential human microRNA-disease association prediction. BMC medical genomics 2013; 6(1):12.
41. Shi H, Xu J, Zhang G, Xu L, Li C, Wang L, Zhao Z, Jiang W, Guo Z, Li X. Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes. BMC systems biology 2013; 7(1):101.
42. Xuan P, Han K, Guo M, Guo Y, Li J, Ding J, Liu Y, Dai Q, Li J, Teng Z. Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors. PloS one 2013; 8(8):e70204.
43. Xu C, Ping Y, Li X, Zhao H, Wang L, Fan H, Xiao Y, Li X. Prioritizing candidate disease miRNAs by integrating phenotype associations of multiple diseases with matched miRNA and mRNA expression profiles. Mol BioSyst 2014.
44. Xu J, Li C-X, Lv J-Y, Li Y-S, Xiao Y, Shao T-T, Huo X, Li X, Zou Y, Han Q-L. Prioritizing Candidate Disease miRNAs by Topological Features in the miRNA Target–Dysregulated Network: Case Study of Prostate Cancer. Molecular cancer therapeutics 2011; 10(10):1857-1866.
45. Jiang Q, Wang G, Jin S, Li Y, Wang Y. Predicting human microRNA–disease associations based on support vector machine. International journal of data mining and bioinformatics 2013; 8(3):282-293.
46. Chen X, Yan G-Y. Semi-supervised learning for potential human microRNA-disease associations inference. Scientific reports 2014; 4.
47. Köhler S, Bauer S, Horn D, Robinson PN. Walking the interactome for prioritization of candidate disease genes. The American Journal of Human Genetics 2008; 82(4):949-958.
之后的论文写作中泛读论文大约40篇，具体见发表论文引用，不再赘述。

三、创新性研究工作
1、 融合多种相关数据，设计相似性计算方法，分别计算miRNA间和疾病间的相似性，并通过miRNA与疾病的关联数据整合网络，重新构建异构生物网络。
2、使用基于元路径的关联预测方法，通过设计不同的元路径，探索miRNA与疾病之间的关联。
3、在元路径预测方法的基础上，提出两种的改进策略。分别为考虑miRNA标签策略和使用支持向量机策略。以上两个策略的使用，使miRNA与疾病间的相关性计算更为准确，从而提高预测准确率。


四、程序编写
1.   在研究生课程中，共编写代码约1000行；
2.   在论文实验与完成毕业设计过程中，共编写代码约500行。

附，实验使用的数据和代码供大家参考使用

zouquan

赞！典范！

RockRabbit

zouquan 发表于 2017-6-20 09:33
赞！典范！

以后研究生毕业就按着张璇这个标准来{:243:}