Biomedical subjects

Jonathan Rowe

Publications and source records attributed to Jonathan Rowe.

4 recordsLinked to original sources

PubMed2006

An extension of Geiringer's theorem for a wide class of evolutionary search algorithms.

The frequency with which various elements of the search space of a given evolutionary algorithm are sampled is affected by the family of recombination (reproduction) operators. The original Geiringer theorem tells us the limiting frequency of occurrence of a given individual under repeated application of crossover alone for the classical genetic algorithm. Recently, Geiringer's theorem has been generalized to include the case of linear GP with homologous crossover (which can also be thought of as a variable length GA). In the current paper we prove a general theorem which tells us that under rather mild conditions on a given evolutionary algorithm, call it A, the stationary distribution of a certain Markov chain of populations in the absence of selection is unique and uniform. This theorem not only implies the already existing versions of Geiringer's theorem, but also provides a recipe of how to obtain similar facts for a rather wide class of evolutionary algorithms. The techniques which are used to prove this theorem involve a classical fact about random walks on a group and may allow us to compute and/or estimate the eigenvalues of the corresponding Markov transition matrix which is directly related to the rate of convergence towards the unique limiting distribution.

Algorithms↗

PubMed2005

Evaluation of the current models for the evolution of bacterial DNA uptake signal sequences.

Current opinion considers two main hypotheses for the evolutionary origin of uptake signal sequences in bacteria: one model regards the uptake signal sequence (USS) as the result of biased gene conversion, whereas the second model views the USS as a molecular tag that evolved as an adaptation. In this article, we present various computational models that implement specific versions of those hypotheses. Those models show that the two hypothesis are not necessarily as opposed to each other as may appear at first glance.

Algorithms↗

PubMed2004

Properties of gray and binary representations.

Representations are formalized as encodings that map the search space to the vertex set of a graph. We define the notion of bit equivalent encodings and show that for such encodings the corresponding Walsh coefficients are also conserved. We focus on Gray codes as particular types of encoding and present a review of properties related to the use of Gray codes. Gray codes are widely used in conjunction with genetic algorithms and bit-climbing algorithms for parameter optimization problems. We present new convergence proofs for a special class of unimodal functions; the proofs show that a steepest ascent bit climber using any reflected Gray code representation reaches the global optimum in a number of steps that is linear with respect to the encoding size. There are in fact many different Gray codes. Shifting is defined as a mechanism for dynamically switching from one Gray code representation to another in order to escape local optima. Theoretical results that substantially improve our understanding of the Gray codes and the shifting mechanism are presented. New proofs also shed light on the number of unique Gray code neighborhoods accessible via shifting and on how neighborhood structure changes during shifting. We show that shifting can improve the performance of both a local search algorithm as well as one of the best genetic algorithms currently available.

Algorithms↗

PubMed2003

The relationship between nonfasting and fasting lipid measurements in patients with or without type 2 diabetes mellitus receiving treatment with 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors.

BACKGROUND: Studies have confirmed a lack of patient and physician adherence to the revised National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) guidelines. These guidelines state that lipid panels should be obtained while the patient is in the fasting state. However, this restriction may limit the ordering of these tests and thus decrease the number of patients on drug therapy and the number treated until goal cholesterol levels are reached. Evidence shows that testing in the nonfasting state may not be clinically or significantly different from testing in the fasting state in identifying patients at risk for a future cardiovascular event. OBJECTIVES: The purpose of this study was to determine whether a relationship exists between nonfasting and fasting lipid values in diabetic or nondiabetic patients that will permit the more ready identification of patients who require treatment to meet NCEP ATP III guidelines. A secondary goal was to determine whether diabetic patients who appear to have reached goal cholesterol levels in the fasting state meet those goals when the non-high-density lipoprotein cholesterol (HDL-C) levels are measured in the nonfasting state. METHODS: This observational study was conducted at Androscoggin Cardiology Associates (Auburn, Maine). Patients with hyperlipidemia receiving statin therapy whose doses had not changed for > or =2 months were enrolled. For all patients, nonfasting and fasting lipid panels (total cholesterol, triglycerides [TGs], and HDL-C) were calculated, whereas low-density lipoprotein cholesterol (LDL-C)levels were measured directly. The direct LDL-C method was used to determine the variance of the calculated LDL-C from the actual value. RESULTS: One hundred consecutive hyperlipidemic patients were tested. Patients included 70 men and 30 women, with a mean (SD) age of 66.2 (12.0) years(range, 24-93 years). Eighteen patients had type 2 diabetes mellitus (DM). Non-fasting TG, HDL-C, and LDL-C levels were able to identify almost all patients who did not meet ATP III guidelines in terms of cholesterol levels (95%, 100%, and 95%, respectively). No predictive differences were found, regardless of whether the patients had type 2 DM. For the total population, statistically significant differences were found between calculated nonfasting and fasting measurements for mean (SD)LDL-C levels (90.2 [24.8] mg/dL vs 99.7 [26.1] mg/dL, respectively; P < 0.001). The regression equation was fasting LDL-C = 22.7 + 0.854 x nonfasting LDL-C.A nonfasting LDL-C level >130 mg/dL predicted a fasting LDL-C level >100 mg/dL(95% CI, -12.79 to -6.24), and a nonfasting LDL-C level >130 mg/dL predicted cases of fasting LDL-C level >100 mg/dL (95% CI, -5.79 to -1.35). CONCLUSIONS: In this study population, nonfasting TG, HDL-C, and LDL-C levels successfully identified almost all patients who did not meet ATP III guidelines for cholesterol levels. No clinically significant difference was found in diabetic or nondiabetic patients.

Adult↗