Hava T Siegelmann | PubMed Health

PubMed2004

Computation in gene networks.

Genetic regulatory networks have the complex task of controlling all aspects of life. Using a model of gene expression by piecewise linear differential equations we show that this process can be considered as a process of computation. This is demonstrated by showing that this model can simulate memory bounded Turing machines. The simulation is robust with respect to perturbations of the system, an important property for both analog computers and biological systems. Robustness is achieved using a condition that ensures that the model equations, that are generally chaotic, follow a predictable dynamics.

Algorithms↗

PubMed2001

Conversion of laparoscopic cholecystectomy to open cholecystectomy in acute cholecystitis: artificial neural networks improve the prediction of conversion.

Laparoscopic cholecystectomy is now also performed for acute cholecystitis. In the presence of inflammatory conditions, technical difficulties leading to conversion to open cholecystectomy may occur and overshadow the advantages of the laparoscopic approach. Factors associated with these undue events combined with techniques capable of learning from them may help in determining when to completely avoid the laparoscopic procedure. In this study we determined predictors of conversion in acute cholecystitis and tested their predictive ability by means of statistical multivariate analysis and artificial neural networks. Between January 1994 and February 1997, 225 patients underwent laparoscopic cholecystectomy for acute cholecystitis. Preoperative and operative data were prospectively collected on standardized forms. The first 180 laparoscopically approached cases entered the training set, which was learned by both the statistical and the artificial neural networks methods. Conversion was first studied in relation to a set of preoperative data. Prediction models were then fitted by both of these methods. The last 45 operated cases, which remained unknown to the learning systems, served for testing the fitted models. The forward stepwise logistic regression technique, the forward stepwise linear discriminant analysis, and the artificial neural networks method enabled positive prediction of conversion in 0%, 27%, and 100% of the cases, and a negative prediction in 80%, 85.5%, and 97% respectively, in the training set. A positive prediction of conversion in 0%, 25%, and 67% of the cases, and a negative prediction in 82%, 88%, and 94%, respectively, in the untrained, validation set of patients. An artificial neural networks based model provides a practical tool for the prediction of successful laparoscopic cholecystectomies and their conversion. The high degree of certainty of prediction in untrained cases reveals its potential, and justifies, under appropriate conditions, the complete avoidance of laparoscopy and turning directly to open cholecystectomy.

Acute Disease↗