Name
Abstract | ||
|---|---|---|
The increasing infectious disease outbreaks has led to a need for new research to better understand the disease's origins, epidemiological features and pathogenicity caused by fast-mutating, fast-spreading viruses. Traditional sequence analysis methods do not take into account the spatio-temporal dynamics of rapidly evolving and spreading viral species. They are also focused on identifying single-point mutations. In this paper, we propose a novel approach that incorporates space-time relationships for studying changes in protein sequences from fast mutating viruses. We aim to detect both single-point mutations as well as k-mutations in the viral sequences. We define the problem of mutation chain pattern mining and design algorithms to discover valid mutation chains. Compact data structures to facilitate the mining process as well as pruning strategies to increase the scalability of the algorithms are devised. Experiments on both synthetic datasets and real world influenza A virus dataset show that our algorithms are scalable and effective in discovering mutations that occur geographically over time. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICDE.2010.5447869 | ICDE |
Keywords | Field | DocType |
mining mutation chains,chain pattern mining,spatio temporal dynamics,data structures,epidemiological features,space time relationships,biology computing,data mining,biological sequences,single point mutations,data structure,protein sequence,amino acids,genetics,amino acid,sequence alignment,immune system,space time,point mutation,indexes,mutation analysis,proteins,nucleotides,population density,algorithm design and analysis,infectious disease | Data mining,Data structure,Algorithm design,Computer science,Computational biology,Pathogenicity,Bioinformatics,Influenza A virus,Infectious disease (medical specialty),Sequence analysis,Scalability,Mutation | Conference |
ISSN | ISBN | Citations |
1084-4627 | 978-1-4244-5444-0 | 2 |
PageRank | References | Authors |
0.41 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chang Sheng | 1 | 20 | 1.59 |
| Wynne Hsu | 2 | 3878 | 353.89 |
| Mong Li Lee | 3 | 2031 | 267.53 |
| Joo Chuan Tong | 4 | 182 | 9.00 |
| See-Kiong Ng | 5 | 657 | 47.82 |