COMPARISON OF RNA SECONDARY STRUCTURE USING DISCRETE WAVELET TRANSFORM AND FRACTAL DIMENSION

被引：1
|
作者：
Liu, Yang [1 ]
Yang, Lina [1 ]
Tang, Yuan Yan [2 ]
Wang, Patrick [3 ]
机构：
[1] Guangxi Univ, Comp & Elect Informat, Nanning, Guangxi, Peoples R China
[2] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp BDB, Beijing, Peoples R China
[3] Northeastern Univ, Comp & Informat Sci, Boston, MA 02115 USA
来源：
PROCEEDINGS OF 2020 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION (ICWAPR) | 2020年
基金：
中国国家自然科学基金;
关键词：
RNA secondary structure; Fractal dimension; Discrete wavelet transform; RNA triple vector curve;
D O I：
10.1109/ICWAPR51924.2020.9494386
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
As the role of RNA molecules was discovered more and more, the similarity between RNA sequences was studied widely. However, because RNA has a conserved secondary structure rather than a primary structure, it is important to consider structural information in RNA comparisons. In this paper, an RNA secondary structure comparison method based on fractal dimension and wavelet transform is proposed. First, the secondary structure of RNA was represented as TV-curve. Next, based on wavelet transform, the windowed fractal dimension is applied to calculate the similarity between sequences. RNA sequences from the RFAM database were selected for the experiment, and the results obtained were closer to the standard MEGA software compared with Li's algorithm, with lower time complexity.
引用
收藏
页码：1 / 7
页数：7
相关论文
共 50 条
  • [21] Fatigue Data Analysis using Continuous Wavelet Transform and Discrete Wavelet Transform
    Abdullah, S.
    Sahadan, S. N.
    Nuawi, M. Z.
    Nopiah, Z. M.
    FRACTURE AND STRENGTH OF SOLIDS VII, PTS 1 AND 2, 2011, 462-463 : 461 - 466
  • [22] EPILEPTIC SEIZURE PREDICTION USING WAVELET TRANSFORM, FRACTAL DIMENSION, SUPPORT VECTOR MACHINE, AND EEG SIGNALS
    Perez-Sanchez, Andrea V.
    Valtierra-Rodriguez, Martin
    Perez-Ramirez, Carlos A.
    De-Santiago-Perez, J. Jesus
    Amezquita-Sanchez, Juan P.
    FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY, 2022, 30 (07)
  • [23] Anomaly Detection in a Smart Grid Using Wavelet Transform, Variance Fractal Dimension and an Artificial Neural Network
    Ghanbari, Maryam
    Kinsner, Witold
    Ferens, Ken
    2016 IEEE ELECTRICAL POWER AND ENERGY CONFERENCE (EPEC), 2016,
  • [24] Analysis of primary aberration with the two-dimension discrete wavelet transform
    Sheu, JY
    Chang, RS
    Lin, CH
    WAVELET APPLICATIONS VII, 2000, 4056 : 372 - 380
  • [25] Image Resolution Enhancement using Discrete Curvelet Transform and Discrete Wavelet Transform
    Shrirao, Shruti A.
    Zaveri, Riddhi
    Patil, Milind S.
    2017 INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN COMPUTER, ELECTRICAL, ELECTRONICS AND COMMUNICATION (CTCEEC), 2017, : 149 - 154
  • [26] A Fractal Dimension and Wavelet Transform Based Method for Protein Sequence Similarity Analysis
    Yang, Lina
    Tang, Yuan Yan
    Lu, Yang
    Luo, Huiwu
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2015, 12 (02) : 348 - 359
  • [27] Detection of protein secondary structures via the discrete wavelet transform
    Pando, Jesus
    Sands, Luke
    Shaheen, Sean E.
    PHYSICAL REVIEW E, 2009, 80 (05):
  • [28] Performance comparison of SIMD implementations of the discrete wavelet transform
    Shahbahrami, A
    Juurlink, B
    Vassiliadis, S
    16th International Conference on Application-Specific Systems, Architecture and Processors, Proceedings, 2005, : 393 - 398
  • [29] Data compression by discrete wavelet transform using matched wavelet
    Gupta, Rashmi
    Kapoor, Rajiv
    INTERNATIONAL JOURNAL OF SIGNAL AND IMAGING SYSTEMS ENGINEERING, 2015, 8 (04) : 205 - 214
  • [30] HIF analysis using discrete wavelet transform
    Torres, Vicente
    Ruiz-Paredes, Hector F.
    Venegas, Vicente R.
    Ramirez Z, Salvador
    2017 IEEE INTERNATIONAL AUTUMN MEETING ON POWER, ELECTRONICS AND COMPUTING (ROPEC), 2017,
12345