Simulation of behavioral profiles in the plus-maze: A Classification and Regression Tree approach

This article introduces a simulation model of rat behavior in the elevated plus-maze, designed through a Decision trees approach using Classification and Regression algorithms. Starting from the analysis of the behavior performed by a sample of 18 Sprague-Dawley male rats, probabilistic rules describing behavioral patterns of the animals were extracted, and were used as the basis of the model computations. The model adequacy was tested by contrasting a simulated sample against an independent sample of real animals. Statistical tests showed that the simulated sample exhibits similar behaviors to those displayed by the real animals, both in terms of the number of entries to open and close arms as well as in terms of the time spent by the animals in those arms. However, the performance of the model in parameters related to the behavioral patterns was partially satisfactory. Given that previous attempts in the literature have neither include this kind of patterns nor the time as a crucial model parameter, the present model offers a suitable alternative for the computational simulation of this paradigm. Compared with antecedent models, the present simulation produced similar or better results in all the considered parameters. Beyond the goal of establish an appropriate simulational model, extracted rules also reveal important regularities associated to the rat behavior previously ignored by other models, i.e. that specific rat behaviors in the elevated plus-maze are time dependent. These and other important considerations to improve the model performance are discussed.     

VPMCD: variable interaction modeling approach for class discrimination in biological systems

Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems.     

Forecasting Physicochemical Variables by a Classification Tree Method. Application to the Berre Lagoon (South France)

The dynamics of the "Etang de Berre", a brackish lagoon situated close to the French Mediterranean sea coast, is strongly disturbed by freshwater inputs coming from an hydroelectric power station. The system dynamics has been described as a sequence of daily typical states from a set of physicochemical variables such as temperature, salinity and dissolved oxygen rates collected over three years by an automatic sampling station. Each daily pattern summarizes the evolution, hour by hour of the physicochemical variables. This article presents results of forecasts of the states of the system subjected to the simultaneous effects of meteorological conditions and freshwater releases. We recall the main step of the classification tree method used to build up the predictive model (Classification and Regression Trees, Breiman et al., 1984) and we propose a transfer procedure in order to test the stability of the model. Results obtained on the Etang de Berre data set allow us to describe and predict the effects of the environmental variables on the system dynamics with a margin of error. The transfer procedure applied after the tree building process gives a maximum gain in prediction accuracy of about 15%.     

气候变化对我国7种植物潜在分布的影响

利用CART(分类和回归树)模型及A2和B2气候情景,模拟分析气候变化对瘿椒树、岩高兰、延龄草、星叶草、天麻、蝟实和秃杉分布范围及空间格局影响.结果显示:气候变化下,就目前适宜分布范围,瘿椒树呈增加趋势,其它植物呈缩小趋势;就新适宜及总适宜分布范围,蔚实、延龄草和瘿椒树呈增加趋势,星叶草和岩高兰呈减小趋势,天麻和秃杉在...     

Identification and siting of native vegetation types on disturbed land: demonstration of statistical methods

Question: How does one best choose native vegetation types and site them in reclamation of disturbed sites ranging from cropland and strip mines? Application: World‐wide, demonstrated in SE Montana. Methods: We assumed that pre‐disturbance native communities are the best targets for revegetation, and that the environmental facet each occupies naturally provides its optimal habitat. Given this assumption, we used pre‐strip‐mine data (800 points from a 88 km² site) to demonstrate statistical methods for identifying native communities, describing them, and determining their environments. Results and conclusions: Classification and pruning analysis provided an objective method for choosing the number of target community types to be used in reclamation. The composition of eight target types, identified with these analyses, was described with a relevé table to provide a species list, target cover levels and support the choice of species to be seeded. As a basis for siting communities, we modeled community presence as a function of topography, slope/aspect, and substrate. Logistic GLMs identified the optimal environment for each community. Classification and Regression Tree (CART) analysis identified the most probable community in each environmental facet. Topography and slope were generally the best predictors in these models. Because our analyses relate native vegetation to undisturbed environments, our results may apply best to sites with minimal substrate disturbance (i.e. better to abandoned cropland than to strip‐mined sites).     

Assessing the effect of prevalence on the predictive performance of species distribution models using simulated data

Aim The proportion of sampled sites where a species is present is known as prevalence. Empirical studies have shown that prevalence can affect the predictive performance of species distribution models. This paper uses simulated species data to examine how prevalence and the form of species environmental dependence affect the assessment of the predictive performance of models. Methods Simulated species data were based on various functions of simulated environmental data with differing degrees of spatial correlation. Seven model performance measures – sensitivity, specificity, class‐average (CA), overall prediction success, kappa (κ), normalized mutual information (NMI) and area under the receiver operating characteristic curve (AUC) – were applied to species models fitted by three regression methods. The response of the performance measures to prevalence was then assessed. Three probability threshold selection methods used to convert fitted logistic model values to presence or absence were also assessed. Results The study shows that the extent to which prevalence affects model performance depends on the modelling technique and its degree of success in capturing dominant environmental determinants. It also depends on the statistic used to measure model performance and the probability threshold method. The response based on κ generally preferred models with medium prevalence. All performance measures were least affected by prevalence when the probability threshold was chosen to maximize predictive performance or was based directly on prevalence. In these cases, the responses based on AUC, CA and NMI generally preferred models with small or large prevalence. Main conclusions The effect of prevalence on the predictive performance of species distribution models has a methodological basis. Relevant factors include the success of the fitted distribution model in capturing the dominant environmental determinant, the model performance measure and the probability threshold selection method. The fixed probability threshold method yields a marked response of model performance to prevalence and is therefore not recommended. The study explains previous empirical results obtained with real data.     

Associations between two novel rSNPs in 5'-flanking region of the bovine casein gene cluster and milk performance traits

Ten evolutionary conservative sequences with high identity level to homological sequences in other mammal species were revealed in 5'-flanking region of casein's genes cluster. Five novel SNPs located inside of the evolutionary conservative regions were identified. The binding sites were revealed to be present in one allelic variant of four detected SNPs. So these SNPs were considered as rSNPs. Significant differences of allelic frequencies were revealed between beef cow's group and dairy cow's group in two rSNPs (NCE4, NCE7, p<0.001). Different alleles of those two rSNPs were shown to be associated with some milk performance traits in Black-and-White Holstein dairy cows. Significant difference of protein percentage has been found between cows with G/G and A/A genotypes (P<0.05) and A/G and A/A genotypes (P<0.05) for NCE4 polymorphism. The groups of animals with genotypes G/G and A/G for NCE7 polymorphism were significantly different in milk yield at the first lactation (kg) (P<0.01), milk fat yield (kg) (P<0.05) and milk protein yield (kg) (P<0.01). For the last trait the difference was significant also between cows with genotypes G/G and A/A for rSNP NCE7 (P<0.05).     

Proteomic detection of cancer in asbestosis patients using SELDI-TOF discovered serum protein biomarkers

Objectives: To identify biomarkers for cancer in asbestosis patients.

Methods: SELDI-TOF and CART were used to identify serum biomarker profiles in 35 asbestosis patients who subsequently developed cancer and 35 did not develop cancer.

Results: Three polypeptide peaks (5707.01, 6598.10, and 20,780.70?Da) could predict the development of cancer with 87% sensitivity and 70% specificity. The first two peaks were identified as KIF18A and KIF5A, respectively, and are part of the Kinesin Superfamily of proteins.

Conclusions: We identified two Kinesin proteins that can be potentially used as blood biomarkers to identify asbestosis patients at risk of developing lung cancer.