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A Method to Avoid Gapped Sequential Patterns in Biological Sequences: Case Study: HIV and Cancer Sequences

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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.
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Keywords: BIOLOGICAL PATTERNS; CANCER AND HIV SEQUENCES; DNA SEQUENCES; GAPPED SEQUENTIAL; SEQUENTIAL PATTERNS

Document Type: Research Article

Publication date: June 1, 2017

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  • 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.
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