In this paper, we investigate the efficiency of a number of commonly used amino acid encodings by using artificial neural networks and substitution scoring matrices. An important step in many machine learning techniques applied in computational biology is encoding the symbolic data of protein sequences reasonably efficient in numeric vector representations. This encoding can be achieved by either considering the amino acid physicochemical properties or a generic numerical encoding. In order to be effective in the context of a machine learning system, an encoding must preserve information relative to the problem at hand, while diminishing superfluous data. To this end, it is important to measure how much an encoding scheme can conserve the underlying similarities and differences that exist among the amino acids. One way to evaluate the effectiveness of an amino acid encoding scheme is to compare it to the roles that amino acids are actually found to play in biological systems. A numerical representation of the similarities and differences between amino acids can be found in substitution matrices commonly used for sequence alignment, since these substitution matrices are based on measures of the interchangeability of amino acids in biological specimens. In this study, a new encoding scheme is also proposed based on the genetic codon coding occurs during protein synthesis. The experimental results indicate better performances compared to the other commonly used encodings.

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Calibration and uncertainty analysis of a complex, over-parameterized environmental models such as the Soil and Water Assessment Tool (SWAT) requires thousands of simulation runs and multiple calibration iterations. A parallel calibration system is thus desired that can be deployed on cloud-based architectures for reducing calibration runtime. This paper presents a cloud-based calibration and uncertainty analysis system called LCC-SWAT that is designed for SWAT models. Two optimization techniques, sequential uncertainty fitting (SUFI-2) and dynamically dimensioned search (DDS), have been implemented in LCC-SWAT. Moreover, the cloud-based system has been deployed on the Southern Ontario Smart Computing Innovation Platform's (SOSCIP) Cloud Analytics platform for diagnostic assessment of parallel calibration runtime on both single-node and multi-node CPU architectures. Unlike other calibrations/uncertainty analysis systems developed on the cloud, this system is capable of generating a comprehensive set of statistical information automatically, which facilitates broader analyses of the performance of the SWAT models. Experimental results on SWAT models of different complexities showed that LCC-SWAT can reduce runtime significantly. The runtime reduction is more pronounced for more complex and computationally intensive models. However, the reported runtime efficiency is significantly higher for single node systems. Comparative experiments with DDS and SUFI-2 show that parallel DDS outperforms parallel SUFI-2 in terms of both parameter identifiability and reducing uncertainty in model simulations. LCC-SWAT is a flexible calibration system and other optimization algorithms and asynchronous parallelization strategies can be added to it in future.