TY - GEN
T1 - Boolean algebraic structures of the genetic code
T2 - 1st International Workshop on Knowledge Discovery and Emergent Complexity in Bioinformatics, KDECB 2006
AU - Grau, Ricardo
AU - Del Chavez, Maria C.
AU - Sanchez, Robersy
AU - Morgado, Eberto
AU - Casas, Gladys
AU - Bone, Isis
PY - 2007
Y1 - 2007
N2 - Authors had reported before two dual Boolean algebras to understand the underlying logic of the genetic code structure. In such Boolean structures, deductions have physico-chemical meaning. We summarize here that these algebraic structures can help us to describe the gene evolution process. Particularly in the experimental confrontation, it was found that most of the mutations of several proteins correspond to deductions in these algebras and they have a small Hamming distance related to their respective wild type. Two applications of the corresponding codification system in bioinformatics problems are also shown. The first one is the classification of mutations in a protein. The other one is related with the problem of detecting donors and acceptors in DNA sequences. Besides, pure mathematical models, Statistical techniques (Decision Trees) and Artificial Intelligence techniques (Bayesian Networks) were used in order to show how to accomplish them to solve these knowledge-discovery practical problems.
AB - Authors had reported before two dual Boolean algebras to understand the underlying logic of the genetic code structure. In such Boolean structures, deductions have physico-chemical meaning. We summarize here that these algebraic structures can help us to describe the gene evolution process. Particularly in the experimental confrontation, it was found that most of the mutations of several proteins correspond to deductions in these algebras and they have a small Hamming distance related to their respective wild type. Two applications of the corresponding codification system in bioinformatics problems are also shown. The first one is the classification of mutations in a protein. The other one is related with the problem of detecting donors and acceptors in DNA sequences. Besides, pure mathematical models, Statistical techniques (Decision Trees) and Artificial Intelligence techniques (Bayesian Networks) were used in order to show how to accomplish them to solve these knowledge-discovery practical problems.
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U2 - 10.1007/978-3-540-71037-0_2
DO - 10.1007/978-3-540-71037-0_2
M3 - Conference contribution
AN - SCOPUS:38349010772
SN - 9783540710363
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 10
EP - 21
BT - Knowledge Discovery and Emergent Complexity in Bioinformatics - First International Workshop, KDECB 2006, Revised Selected Papers
PB - Springer Verlag
Y2 - 10 May 2006 through 10 May 2006
ER -