Name
Abstract | ||
|---|---|---|
Massive sequencing technologies are producing an increasing amount of whole genome data, which need to be explored and analyzed. New computational tools are thus required to deal with the dimensionality and complexity of these data. Single Nucleotide Polymorphisms (SNPs) are the most common human genome variation and can be involved in disease conditions. Identifying SNPs and annotating its functional and clinical role in whole human genomes is a challenging task, which requires expert curation. There are several software tools that assist researchers in the SNP calling and SNP annotation processes. However, these tools do not focus on the association of SNPs to regulatory regions such as Transcription Factor Binding Sites (TFBSs). This paper proposes a methodology to assist the annotation of SNPs in whole genome sequences, including not only genes but also known TFBSs. Our main contribution is that we use an intuitionistic-based similarity measure (SCintuit [1]), based on fuzzy technology and intuitionistic sets, to perform accurate comparisons between DNA sequences and identify TFBSs affected by a SNP. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ISDA.2011.6121821 | Intelligent Systems Design and Applications |
Keywords | Field | DocType |
DNA,bioinformatics,computational complexity,diseases,fuzzy set theory,genomics,molecular biophysics,software tools,DNA sequence,SNP annotation process,SNP association,clinical role,computational tools,data complexity,disease condition,functional role,fuzzy technology,genome data,genome sequence,human genome variation,intuitionistic sets,intuitionistic-based similarity measure,massive sequencing technology,next generation sequencing data,single nucleotide polymorphism,software tools,transcription factor binding site,Bioinformatics,SNP Annotation,Sequence similarity measures,TFBS,intuitionistic fuzzy sets | Genome,Annotation,Computer science,Genomics,Artificial intelligence,DNA sequencing,Single-nucleotide polymorphism,Human genome,SNP annotation,SNP,Machine learning | Conference |
ISSN | ISBN | Citations |
2164-7143 | 978-1-4577-1676-8 | 0 |
PageRank | References | Authors |
0.34 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. A. Morente-Molinera | 1 | 0 | 0.34 |
| Francisco J. Martinez | 2 | 705 | 58.69 |
| Carlos Cano | 3 | 92 | 6.89 |
| Marta Cuadros Cuadros | 4 | 8 | 1.22 |
| A. Blanco | 5 | 141 | 11.03 |