IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v481y2017icp235-242.html
   My bibliography  Save this article

Weighted measures based on maximizing deviation for alignment-free sequence comparison

Author

Listed:
  • Qian, Kun
  • Luan, Yihui

Abstract

Alignment-free sequence comparison is becoming fairly popular in many fields of computational biology due to less requirements for sequence itself and computational efficiency for a large scale of sequence data sets. Especially, the approaches based on k-tuple like D2, D2S and D2∗ are used widely and effectively. However, these measures treat each k-tuple equally without accounting for the potential importance differences among all k-tuples. In this paper, we take advantage of maximizing deviation method proposed in multiple attribute decision making to evaluate the weights of different k-tuples. We modify D2, D2S and D2∗ with weights and test them by similarity search and evaluation on functionally related regulatory sequences. The results demonstrate that the newly proposed measures are more efficient and robust compared to existing alignment-free methods.

Suggested Citation

  • Qian, Kun & Luan, Yihui, 2017. "Weighted measures based on maximizing deviation for alignment-free sequence comparison," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 235-242.
    • Handle: RePEc:eee:phsmap:v:481:y:2017:i:c:p:235-242
    DOI: 10.1016/j.physa.2017.04.062
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117303540
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2017.04.062?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Chun & Yang, Yan & Jia, Meiduo & Zhang, Yingying & Yu, Xiaoqing & Wang, Changzhong, 2014. "Phylogenetic analysis of DNA sequences based on k-word and rough set theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 162-171.
    2. Yu, Hong-Jie & Huang, De-Shuang, 2012. "Novel graphical representation of genome sequence and its applications in similarity analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6128-6136.
    3. Tiee-Jian Wu & Ya-Ching Hsieh & Lung-An Li, 2001. "Statistical Measures of DNA Sequence Dissimilarity under Markov Chain Models of Base Composition," Biometrics, The International Biometric Society, vol. 57(2), pages 441-448, June.
    4. Liao, Bo & Xiang, Qilin & Cai, Lijun & Cao, Zhi, 2013. "A new graphical coding of DNA sequence and its similarity calculation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4663-4667.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dan-Ping Li & Li Xie & Peng-Fei Cheng & Xiang-Hong Zhou & Cheng-Xun Fu, 2021. "Green Supplier Selection Under Cloud Manufacturing Environment: A Hybrid MCDM Model," SAGE Open, , vol. 11(4), pages 21582440211, November.
    2. Qian, Kun & Luan, Yihui, 2018. "Phylogenetic analysis of DNA sequences based on fractional Fourier transform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 795-808.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qian, Kun & Luan, Yihui, 2018. "Phylogenetic analysis of DNA sequences based on fractional Fourier transform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 795-808.
    2. repec:jss:jstsof:35:i05 is not listed on IDEAS
    3. Keng-Hoong Ng & Chin-Kuan Ho & Somnuk Phon-Amnuaisuk, 2012. "A Hybrid Distance Measure for Clustering Expressed Sequence Tags Originating from the Same Gene Family," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-14, October.
    4. Jin, Xin & Nie, Rencan & Zhou, Dongming & Yao, Shaowen & Chen, Yanyan & Yu, Jiefu & Wang, Quan, 2016. "A novel DNA sequence similarity calculation based on simplified pulse-coupled neural network and Huffman coding," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 325-338.
    5. Hou, Wenbing & Pan, Qiuhui & He, Mingfeng, 2014. "A novel representation of DNA sequence based on CMI coding," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 409(C), pages 87-96.
    6. Peter B. Gilbert & Chunyuan Wu & David V. Jobes, 2008. "Genome Scanning Tests for Comparing Amino Acid Sequences Between Groups," Biometrics, The International Biometric Society, vol. 64(1), pages 198-207, March.
    7. Ma, Tingting & Liu, Yuxin & Dai, Qi & Yao, Yuhua & He, Ping-an, 2014. "A graphical representation of protein based on a novel iterated function system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 21-28.
    8. Li, Yushuang & Liu, Qian & Zheng, Xiaoqi, 2016. "DUC-Curve, a highly compact 2D graphical representation of DNA sequences and its application in sequence alignment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 256-270.
    9. Marcello D’Agostino & Valentino Dardanoni, 2009. "What’s so special about Euclidean distance?," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 33(2), pages 211-233, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:481:y:2017:i:c:p:235-242. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.