Biomedical subjects

F M Stefanini

Publications and source records attributed to F M Stefanini.

3 recordsLinked to original sources

PubMed2000

Bayesian estimation of range for microsatellite loci.

Microsatellite loci have become important in population genetics because of their high level of polymorphism in natural populations, very frequent occurrence throughout the genome, and apparently high mutation rate. Observed repeat numbers (alleles size) in natural populations and expectations based on computer simulations suggest that the range of repeat numbers at a microsatellite locus is restricted. This range is a key parameter that should be properly estimated in order to proceed with calculations of divergence times in phylogenetic studies and to better investigate the within- and between-population variability. The 'plug-in' estimate of range based on the minimum and maximum value observed in a sample is not satisfactory because of the relatively large number of alleles in comparison with typical sample sizes. In this paper, a set of data from 30 dinucleotide microsatellite loci is analysed under the assumption of independence among loci. Bayesian inference on range for one locus is obtained by assuming that constraints on range values exist as sharp bounds. Closed-form calculations and robustness revealed by our analysis suggest that the proposed Bayesian approach might be routinely used by researchers to classify microsatellite loci according to the estimated value of their allelic range.

Bayes Theorem↗

PubMed2000

The reduction of large molecular profiles to informative components using a genetic algorithm.

MOTIVATION: Molecular profiles (DNA fingerprints) may be used to allocate an individual of unknown membership to one among the known groups of a reference population. Time and costs of profile assessment may be reduced by identifying informative profile components (markers). RESULTS: A genetic algorithm (GA) is proposed to identify promising candidate markers from a pilot experiment in which observations are supposed to be without measurement error. The analysis of simulated datasets suggests reasonable values for GA parameters and confirms that the GA finds components of the profile showing association with the considered groups. Our GA may be used to perform a first screening of candidate markers to be included in subsequent experiments. AVAILABILITY: The 32-bit executable (Windows 95, 98 and NT) is available at http://www.ds.unifi.it/ approximately stefanin/bioinformatics.htm.

Algorithms↗

PubMed1993

APLOGEN: an object-oriented genetic algorithm performing Monte Carlo optimization.

Problem-solving and modelling within a biological context often need a level of descriptive accuracy that is unlikely to be capable of analytical treatment, especially if the mathematical background of the biologist is poor. Furthermore solver-model maintenance is often difficult without the availability of trained specialists. Better prospects are found in the genetic algorithm field. Genetic algorithms are a set of procedures formulated to solve complex problems without specifying rules for intermediate steps. This approach becomes feasible performing a Monte Carlo simulation of the natural evolution process, in which population improvement (search for solutions) in a considered environment (the specific problem domain) is achieved by following the genetic paradigm. Starting with a randomly constituted sample of individuals, drawn from the population of admissible values and expressed as binary strings, random mating brings about individuals of the next generation. Parents are chosen with a greater probability as the number of constraints violated by each individual becomes smaller. During the constitution of each generation the presence of some genetic operators causes the improvement of population diversity and its maintenance. Genetic operators are simple string transformation rules, generally independent of a specific context. We have developed the constant core of a minimal genetic algorithm, from which can be derived genetic problem-solvers in specific domains. An applicative example--a constrained matrix equation on signed integers--is also realized to show graphically the algorithm dynamics.

Algorithms↗