Name
Abstract | ||
|---|---|---|
With the rapid development of next generation sequencing technology, the amount of biological sequence data of the cancer genome increases exponentially, which calls for efficient and effective algorithms that may identify patterns hidden underneath the raw data that may distinguish cancer Achilles' heels. From a signal processing point of view, biological units of information, including DNA and protein sequences, have been viewed as one-dimensional signals. Therefore, researchers have been applying signal processing techniques to mine the potentially significant patterns within these sequences. More specifically, in recent years, wavelet transforms have become an important mathematical analysis tool, with a wide and ever increasing range of applications. The versatility of wavelet analytic techniques has forged new interdisciplinary bounds by offering common solutions to apparently diverse problems and providing a new unifying perspective on problems of cancer genome research. In this paper, we provide a survey of how wavelet analysis has been applied to cancer bioinformatics questions. Specifically, we discuss several approaches of representing the biological sequence data numerically and methods of using wavelet analysis on the numerical sequences. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TCBB.2013.134 | IEEE/ACM Trans. Comput. Biology Bioinform. |
Keywords | Field | DocType |
dna sequences,cancer genome research,cancer achilles,wavelet analysis,next generation sequencing technology,pattern recognition,raw data,biological unit,mathematical analysis tool,wavelet transforms,dna,current cancer genome research,one-dimensional signals,signal processing point of view,biological information units,effective algorithm,signal processing techniques,genomics,numerical sequences,cancer genome,wavelet analytic techniques,cancer achilles' heels,cancer bioinformatics questions,proteins,medical signal processing,protein sequences,biological sequence data representation,mathematical analysis,cancer bioinformatics question,passenger mutation,biological sequence data,driver mutation,cancer,cancer genome increases exponentially,common solutions,wavelet transform,wavelet analytic technique,important mathematical analysis tool,bioinformatics,pattern identification,amino acids | Genome,Signal processing,Data mining,Computer science,Raw data,Genomics,Artificial intelligence,DNA sequencing,Cluster analysis,Wavelet transform,Wavelet,Bioinformatics,Machine learning | Journal |
Volume | Issue | ISSN |
10 | 6 | 1557-9964 |
Citations | PageRank | References |
7 | 0.59 | 27 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Meng | 1 | 81 | 6.38 |
| Ahmed T Soliman | 2 | 9 | 1.30 |
| Mei-Ling Shyu | 3 | 1863 | 141.25 |
| Yimin Yang | 4 | 162 | 11.16 |
| Shu-Ching Chen | 5 | 1978 | 182.74 |
| S. S. Iyengar | 6 | 152 | 25.22 |
| John S Yordy | 7 | 9 | 0.96 |
| Puneeth Iyengar | 8 | 17 | 2.55 |