A Method to Avoid Gapped Sequential Patterns in Biological Sequences: Case Study: HIV and Cancer Sequences
Sequential pattern mining is one of important discussion in the data mining with wide application in different area of science. Pattern mining also for biomedical sequences is an important task in Biocomputing and biological problem, which holds all the information of the perfect patterns. The methods of sequential pattern mining search all pattern in sequences, which can contain gap, and some non-related patterns. Gap in the biological sequence is an important issue, because it can be changes amino acids sequences, protein structures and understanding of biological models. In this paper an algorithm proposed to conduct parallel mining of biological sequential patterns on a biological dataset including gap. The proposed algorithm has used dynamic scheduling to avoid tasks idling; moreover we have employed a technique, called random selecting. The experimental results show that our proposed method brings a good efficiency on different input datasets.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
Publication date: June 1, 2017
More about this publication?
- Journal of Neuroscience and Neuroengineering (JNSNE) is an international peer- reviewed journal that covers all aspects of neuroscience and neuroengineering. The journal publishes original full-length research papers, letters, tutorials and review papers in all interdisciplinary disciplines that bridge the gaps between neuroscience, neuroengineering, neurotechnology, neurobiology, brain disorders and diseases, novel medicine, neurotoxicology, biomedical engineering and nanotechnology.
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites