Quantitative structure–activity relationship study using artificial neural network (ANN) methodology were conducted to predict the inhibition constants of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyan...Quantitative structure–activity relationship study using artificial neural network (ANN) methodology were conducted to predict the inhibition constants of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyanoguanidine derivatives containing different substituent groups such as: benzyl, isopropyl, 4-hydroxybenzyl, ketone, oxime, pyrazole, imidazole, triazole and having anti-HIV-1 protease activities. The results obtained by artificial neural network give advanced regression models with good prediction ability. The two optimal artificial neural network models obtained have coefficients of determination of 0.746 and 0.756. The lowest prediction’s root mean square error obtained is 0.607. Artificial neural networks provide improved models for heterogeneous data sets without splitting them into families. Both the external and cross-validation methods are used to validate the performances of the resulting models. Randomization test is employed to check the suitability of the models.展开更多
The ability to decipher the genetic code of different species would lead to significant future scientific achievements in important areas, including medicine and agriculture. The importance of DNA sequencing necessita...The ability to decipher the genetic code of different species would lead to significant future scientific achievements in important areas, including medicine and agriculture. The importance of DNA sequencing necessitated a need for efficient automation of identification of base sequences from traces generated by existing sequencing machines, a process referred to as DNA base-calling. In this paper, a pattern recognition technique was adopted to minimize the inaccuracy in DNA base-calling. Two new frameworks using Artificial Neural Networks and Polynomial Classifiers are proposed to model electropherogram traces belonging to Homo sapiens, Saccharomyces mikatae and Drosophila melanogaster. De-correlation, de-convolution and normalization were implemented as part of the pre-processing stage employed to minimize data imperfections attributed to the nature of the chemical reactions involved in DNA sequencing. Discriminative features that characterize each chromatogram trace were subsequently extracted and subjected to the chosen classifiers to categorize the events to their respective base classes. The models are trained such that they are not restricted to a specific species or to a specific chemical procedure of sequencing. The base- calling accuracy achieved is compared with the exist- ing standards, PHRED (Phil’s Read Editor) and ABI (Applied Biosystems, version2.1.1) KB base-callers in terms of deletion, insertion and substitution errors. Experimental evidence indicates that the proposed models achieve a higher base-calling accuracy when compared to PHRED and a comparable performance when compared to ABI. The results obtained demon- strate the potential of the proposed models for efficient and accurate DNA base-calling.展开更多
文摘Quantitative structure–activity relationship study using artificial neural network (ANN) methodology were conducted to predict the inhibition constants of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyanoguanidine derivatives containing different substituent groups such as: benzyl, isopropyl, 4-hydroxybenzyl, ketone, oxime, pyrazole, imidazole, triazole and having anti-HIV-1 protease activities. The results obtained by artificial neural network give advanced regression models with good prediction ability. The two optimal artificial neural network models obtained have coefficients of determination of 0.746 and 0.756. The lowest prediction’s root mean square error obtained is 0.607. Artificial neural networks provide improved models for heterogeneous data sets without splitting them into families. Both the external and cross-validation methods are used to validate the performances of the resulting models. Randomization test is employed to check the suitability of the models.
文摘The ability to decipher the genetic code of different species would lead to significant future scientific achievements in important areas, including medicine and agriculture. The importance of DNA sequencing necessitated a need for efficient automation of identification of base sequences from traces generated by existing sequencing machines, a process referred to as DNA base-calling. In this paper, a pattern recognition technique was adopted to minimize the inaccuracy in DNA base-calling. Two new frameworks using Artificial Neural Networks and Polynomial Classifiers are proposed to model electropherogram traces belonging to Homo sapiens, Saccharomyces mikatae and Drosophila melanogaster. De-correlation, de-convolution and normalization were implemented as part of the pre-processing stage employed to minimize data imperfections attributed to the nature of the chemical reactions involved in DNA sequencing. Discriminative features that characterize each chromatogram trace were subsequently extracted and subjected to the chosen classifiers to categorize the events to their respective base classes. The models are trained such that they are not restricted to a specific species or to a specific chemical procedure of sequencing. The base- calling accuracy achieved is compared with the exist- ing standards, PHRED (Phil’s Read Editor) and ABI (Applied Biosystems, version2.1.1) KB base-callers in terms of deletion, insertion and substitution errors. Experimental evidence indicates that the proposed models achieve a higher base-calling accuracy when compared to PHRED and a comparable performance when compared to ABI. The results obtained demon- strate the potential of the proposed models for efficient and accurate DNA base-calling.
