• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Jiang Tao, Li Zhanhuai. A Survey on Local Pattern Mining in Gene Expression Data[J]. Journal of Computer Research and Development, 2018, 55(11): 2343-2360. DOI: 10.7544/issn1000-1239.2018.20170629
Citation: Jiang Tao, Li Zhanhuai. A Survey on Local Pattern Mining in Gene Expression Data[J]. Journal of Computer Research and Development, 2018, 55(11): 2343-2360. DOI: 10.7544/issn1000-1239.2018.20170629

A Survey on Local Pattern Mining in Gene Expression Data

More Information
  • Published Date: October 31, 2018
  • As an unprecedented breakthrough in experimental molecular biology domain, DNA microarray enables simultaneously monitoring of the expression level of thousands of genes over many experimental conditions. Studies have shown that analyzing microarray data is essential for finding gene co-expression network, designing new types of drugs, preventing disease, and so on. To analyze gene expression datasets, the researchers design many clustering methods, which can only find fewer of useful knowledge. Due to a subset of genes co-regulate and co-express only under a subset of experimental conditions, and also not co-express at the same level, they can belong to several genetic pathways that are not apparent. In this situation, the biclustering method is proposed. At the same time, the direction of gene expression analysis changes from the whole pattern mining to the local pattern discovery, and then it changes the situation of clustering data only based on all the objects or attributes of the data. The paper introduces the state-of-the-art progress, which includes the definition of local pattern, the types and criteria of local pattern, mining and query methods of local pattern. Then it concludes the mining criteria based on quantity and quality, and related software. Further, it gives the problems in the existing algorithms and tools. Finally, we discuss the research direction in the future.
  • Related Articles

    [1]Li Jianhui, Shen Zhihong, Meng Xiaofeng. Scientific Big Data Management: Concepts, Technologies and System[J]. Journal of Computer Research and Development, 2017, 54(2): 235-247. DOI: 10.7544/issn1000-1239.2017.20160847
    [2]Shen Bilong, Zhao Ying, Huang Yan, Zheng Weimin. Survey on Dynamic Ride Sharing in Big Data Era[J]. Journal of Computer Research and Development, 2017, 54(1): 34-49. DOI: 10.7544/issn1000-1239.2017.20150729
    [3]ZhuWeiheng, YinJian, DengYuhui, LongShun, QiuShiding. Efficient Duplicate Detection Approach for High Dimensional Big Data[J]. Journal of Computer Research and Development, 2016, 53(3): 559-570. DOI: 10.7544/issn1000-1239.2016.20148218
    [4]Meng Xiaofeng, Du Zhijuan. Research on the Big Data Fusion: Issues and Challenges[J]. Journal of Computer Research and Development, 2016, 53(2): 231-246. DOI: 10.7544/issn1000-1239.2016.20150874
    [5]Li Weibang, Li Zhanhuai, Chen Qun, Jiang Tao, Liu Hailong, Pan Wei. Functional Dependencies Discovering in Distributed Big Data[J]. Journal of Computer Research and Development, 2015, 52(2): 282-294. DOI: 10.7544/issn1000-1239.2015.20140229
    [6]Meng Xiaofeng, Zhang Xiaojian. Big Data Privacy Management[J]. Journal of Computer Research and Development, 2015, 52(2): 265-281. DOI: 10.7544/issn1000-1239.2015.20140073
    [7]Liu Yahui, Zhang Tieying, Jin Xiaolong, Cheng Xueqi. Personal Privacy Protection in the Era of Big Data[J]. Journal of Computer Research and Development, 2015, 52(1): 229-247. DOI: 10.7544/issn1000-1239.2015.20131340
    [8]Meng Xiaofeng, Li Yong, Jonathan J. H. Zhu. Social Computing in the Era of Big Data: Opportunities and Challenges[J]. Journal of Computer Research and Development, 2013, 50(12): 2483-2491. DOI: 10.7544/issn1000-1239.2013.20130890
    [9]Li Jianzhong and Liu Xianmin. An Important Aspect of Big Data: Data Usability[J]. Journal of Computer Research and Development, 2013, 50(6): 1147-1162.
    [10]Meng Xiaofeng and Ci Xiang. Big Data Management: Concepts,Techniques and Challenges[J]. Journal of Computer Research and Development, 2013, 50(1): 146-169.
  • Cited by

    Periodical cited type(5)

    1. 廖鑫,黎懿熠,欧阳军林,周江盟,戴湘桃,秦拯. 一种基于深度学习的移动端隐写方法. 湖南大学学报(自然科学版). 2022(04): 18-25 .
    2. 何凤英. 改进卷积神经网络在图像隐写检测中的应用. 福建电脑. 2022(09): 1-6 .
    3. 黄思远,张敏情,柯彦,毕新亮. 基于显著性检测的图像隐写分析方法. 计算机应用. 2021(02): 441-448 .
    4. 黄思远,张敏情,柯彦,毕新亮. 基于自注意力机制的图像隐写分析方法. 计算机应用研究. 2021(04): 1190-1194 .
    5. 吴煌,李凯勇. 基于DCT域的数字图像隐写容量归一化方法. 计算机仿真. 2021(08): 207-211 .

    Other cited types(5)

Catalog

    Article views (1669) PDF downloads (626) Cited by(10)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return