A genetic algorithm-Bayesian network approach for the analysis of metabolomics and spectroscopic data: application to the rapid detection of Bacillus spores and identification of Bacillus species

Background The rapid identification of Bacillus spores and bacterial identification are paramount because of their implications in food poisoning, pathogenesis and their use as potential biowarfare agents. Many automated analytical techniques such as Curie-point pyrolysis mass spectrometry (Py-MS) have been used to identify bacterial spores giving use to large amounts of analytical data. This high number of features makes interpretation of the data extremely difficult We analysed Py-MS data from 36 different strains of aerobic endospore-forming bacteria encompassing seven different species. These bacteria were grown axenically on nutrient agar and vegetative biomass and spores were analyzed by Curie-point Py-MS. Results We develop a novel genetic algorithm-Bayesian network algorithm that accurately identifies sand selects a small subset of key relevant mass spectra (biomarkers) to be further analysed. Once identified, this subset of relevant biomarkers was then used to identify Bacillus spores successfully and to identify Bacillus species via a Bayesian network model specifically built for this reduced set of features. Conclusions This final compact Bayesian network classification model is parsimonious, computationally fast to run and its graphical visualization allows easy interpretation of the probabilistic relationships among selected biomarkers. In addition, we compare the features selected by the genetic algorithm-Bayesian network approach with the features selected by partial least squares-discriminant analysis (PLS-DA). The classification accuracy results show that the set of features selected by the GA-BN is far superior to PLS-DA

Automating RT-Level Operand Isolation to Minimize Power Consumption in Datapaths

Designs which do not fully utilize their arithmetic datapath components typically exhibit a significant overhead in power consumption. Whenever a module performs an operation whose result is not used in the downstream circuit, power is being consumed for an otherwise redundant computation. Operand isolation [3] is a technique to minimize the power overhead incurred by redundant operations by selectively blocking the propagation of switching activity through the circuit. This paper discusses how redundant operations can be identified concurrently to normal circuit operation, and presents a model to estimate the power savings that can be obtained by isolation of selected modules at the registertransfer (RT) level. Based on this model, an algorithm is presented to iteratively isolate modules while minimizing the cost incurred by RTL operand isolation. Experimental results with power reductions of up to 30% demonstrate the effectiveness of the approach. 1 Introduction In certain classes..