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PubMed2006

Robustness testing of a chiral NACE method for R-timolol determination in S-timolol maleate and uncertainty assessment from quantitative data.

A robustness test of a capillary electrophoresis method for the chiral separation of timolol in nonaqueous acidified media was performed. A two-level Plackett-Burman design was applied in which one qualitative and six quantitative factors were examined. Resolution, migration times and relative migration times to pyridoxine (selected as internal standard) were examined as qualitative responses to evaluate electrophoretic performance. A quantitative response, the content of R-timolol in S-timolol maleate sample, was also considered. Even though some significant factor effects were observed on the qualitative responses, it was still possible to quantify the R-timolol in the S-timolol maleate samples properly. The quantitative response was not significantly affected by the selected factors, demonstrating the robustness of the procedure. However, the use of different HDMS-beta-CD batches seemed to affect both types of responses necessitating to introduce a warning in the procedure. Since the experiments of the Plackett-Burman design can be assimilated to laboratories in an interlaboratory study, uncertainty can be evaluated using the robustness test data. The robustness test was set-up in such a way that the required variances could be estimated.

Adrenergic beta-Antagonists↗

PubMed2006

Robustness and evolvability in genetic regulatory networks.

Living organisms are robust to a great variety of genetic changes. Gene regulation networks and metabolic pathways self-organize and reaccommodate to make the organism perform with stability and reliability under many point mutations, gene duplications and gene deletions. At the same time, living organisms are evolvable, which means that these kind of genetic perturbations can eventually make the organism acquire new functions and adapt to new environments. It is still an open problem to determine how robustness and evolvability blend together at the genetic level to produce stable organisms that yet can change and evolve. Here we address this problem by studying the robustness and evolvability of the attractor landscape of genetic regulatory network models under the process of gene duplication followed by divergence. We show that an intrinsic property of this kind of networks is that, after the divergence of the parent and duplicate genes, with a high probability the previous phenotypes, encoded in the attractor landscape of the network, are preserved and new ones might appear. The above is true in a variety of network topologies and even for the case of extreme divergence in which the duplicate gene bears almost no relation with its parent. Our results indicate that networks operating close to the so-called "critical regime" exhibit the maximum robustness and evolvability simultaneously.

Animals↗

PubMedDate not supplied

Robust growing neural gas algorithm with application in cluster analysis.

We propose a novel robust clustering algorithm within the Growing Neural Gas (GNG) framework, called Robust Growing Neural Gas (RGNG) network.The Matlab codes are available from . By incorporating several robust strategies, such as outlier resistant scheme, adaptive modulation of learning rates and cluster repulsion method into the traditional GNG framework, the proposed RGNG network possesses better robustness properties. The RGNG is insensitive to initialization, input sequence ordering and the presence of outliers. Furthermore, the RGNG network can automatically determine the optimal number of clusters by seeking the extreme value of the Minimum Description Length (MDL) measure during network growing process. The resulting center positions of the optimal number of clusters represented by prototype vectors are close to the actual ones irrespective of the existence of outliers. Topology relationships among these prototypes can also be established. Experimental results have shown the superior performance of our proposed method over the original GNG incorporating MDL method, called GNG-M, in static data clustering tasks on both artificial and UCI data sets.

Algorithms↗

PubMed2005

Increased sensitivity in neuroimaging analyses using robust regression.

Robust regression techniques are a class of estimators that are relatively insensitive to the presence of one or more outliers in the data. They are especially well suited to data that require large numbers of statistical tests and may contain outliers due to factors not of experimental interest. Both these issues apply particularly to neuroimaging data analysis. We use simulations to compare several robust techniques against ordinary least squares (OLS) regression, and we apply robust regression to second-level (group "random effects") analyses in three fMRI datasets. Our results show that robust iteratively reweighted least squares (IRLS) at the 2nd level is a computationally efficient technique that both increases statistical power and decreases false positive rates in the presence of outliers. The benefits of IRLS are apparent with small samples (n = 10) and increase with larger sample sizes (n = 40) in the typical range of group neuroimaging experiments. When no true effects are present, IRLS controls false positive rates at an appropriate level. We show that IRLS can have substantial benefits in analysis of group data and in estimating hemodynamic response shapes from time series data. We provide software to implement IRLS in group neuroimaging analyses.

Algorithms↗

PubMed2006

Robustness: predicting the effects of life history perturbations on stage-structured population dynamics.

Matrix-based models lie at the core of many applications across the physical, engineering and life sciences. In ecology, matrix models arise naturally via population projection matrices (PPM). The eigendata of PPMs provide detailed quantitative and qualitative information on the dynamic behaviour of model populations, especially their asymptotic rates of growth or decline. A fundamental task in modern ecology is to assess the effect that perturbations to life-cycle transition rates of individuals have on such eigendata. The prevailing assessment tools in ecological applications of PPMs are direct matrix simulations of eigendata and linearised extrapolations to the typically non-linear relationship between perturbation magnitude and the resulting matrix eigenvalues. In recent years, mathematical systems theory has developed an analytical framework, called 'Robustness Analysis and Robust Control', encompassing also algorithms and numerical tools. This framework provides a systematic and precise approach to studying perturbations and uncertainty in systems represented by matrices. Here we lay down the foundations and concepts for a 'robustness' inspired approach to predictive analyses in population ecology. We treat a number of application-specific perturbation problems and show how they can be formulated and analysed using these robustness methodologies.

Animals↗

PubMed2005

Development of characteristic equations and robust stability analysis for MIMO move suppressed and shifted DMC.

Discrete-time controller and closed-loop transfer functions were developed for move suppressed lambda and the recently formulated m-shifted multiple-input-multiple-output (MIMO) dynamic matrix control (DMC). Using these transfer functions, robust analyses were conducted for MIMO plants by varying corresponding delay and gain ratios of the system. In all instances, robust plots indicate that the shifted DMC is less sensitive and hence more robust to variations in the plant parameters than move suppressed DMC. It was shown that the design of these MIMO DMC controllers depends on the plant closed-loop performance and overall stability, since the selection of lambda and m directly influences the plant robustness and closed-loop dynamics.

Algorithms↗

PubMed2006

Development of characteristic equations and robust stability analysis for SISO move suppressed and shifted DMC.

New controller and closed loop transfer functions for move suppressed and shifted dynamic matrix control were derived in order to compare the controller robustness on several plants as a function of tuning parameters lambda and m. The derivation of these transfer functions are for any order plant requiring its open loop step or impulse response. A generic control design algorithm was developed for selecting the controller tuning parameters using controller robustness as a performance index, in the presence of plant parameter variations and uncertainties. Shifted dynamic matrix control (DMC) was found to be more robust with respect to all plant parameter variations, and therefore more suited than move suppressed DMC to control plants with wide ranging parameters. This result was demonstrated on an experimental direct current servomotor system, and further verified on a plant having a cascade control structure with the (m , m) being the most robust to plant variations.

Journal Article↗

PubMed2003

Robustness testing of chiral separations by capillary electrophoresis using highly-sulfated cyclodextrins.

The robustness of a generic method for chiral separation in capillary electrophoresis using highly-sulfated cyclodextrins in a low pH phosphate buffer and the "short-end injection technique" was studied. In this study, we focused on the robustness of the separations and not of the quantitative analysis of the enantiomers. The robustness was evaluated for the enantiomeric separation of a basic (propranolol), a neutral (praziquantel) and an acidic (warfarin) compound. The influence of eight factors which were believed to affect significantly the separations was studied using a 11-factor, 12-experiment Plackett-design. Statistical interpretation of the factor effects on different analytical responses (selectivity and resolution) was performed. The separations of the three compounds could be considered as rather robust as the factor effects were generally not significant (alpha = 0.05) and small.

Cyclodextrins↗

PubMed2000

Scrutiny of the ASTRO consensus definition of biochemical failure in irradiated prostate cancer patients demonstrates its usefulness and robustness. American Society for Therapeutic Radiology and Oncology.

PURPOSE: The goals of this study are: (1) to establish the robustness of the Fox Chase Cancer Center (FCCC) and the American Society for Therapeutic Radiology and Oncology (ASTRO) consensus definitions of failure by comparing biochemical estimates under various modifications of the censoring and failure time components to their respective unaltered definitions; (2) to isolate the source of variation between the two definitions of failure; and (3) to describe the hazard of failure over time for each definition. METHODS: Between May 1989 and May 1997, 670 men were treated at Fox Chase Cancer Center for localized prostate cancer using three-dimensional conformal radiation therapy (3DCRT). These men were stratified into three groups for analysis: 111 men treated with adjuvant hormones; 204 men treated with radiation therapy alone and presenting with more favorable prognosis tumor characteristics; 255 men treated with radiation therapy alone and presenting with less favorable prognosis tumor characteristics. For each group, biochemical failure was estimated and compared using the FCCC and ASTRO definitions of failure. The robustness of each definition was evaluated by comparing estimates under the definition as stated to those under various modifications of the censoring and failure components. Analyses were also performed while excluding slow-progressing patients. To isolate the source of variation between the two failure definitions, estimates were compared for patients with agreement in failure status. Estimates of biochemical failure, and thus hazard rates, were made using Kaplan-Meier methodology. RESULTS: ASTRO biochemical failure estimates were higher than the FCCC failure estimates in the first 5 years post-treatment. Beyond 5 years, ASTRO estimates level off, while the FCCC failure estimates continued to increase. These failure patterns were similar in all patient groups; however, patients treated with adjuvant hormones had a much higher risk of failure immediately following treatment under the ASTRO definition. Modifying the censoring pattern had little effect on failure estimates in any patient group, regardless of definition used. The exclusion of patients with slow prostate-specific antigen (PSA) doubling time did not result in biochemical estimates that differed significantly from those for all patients. The analysis of patients with agreement in failure status continued to demonstrate significant differences in estimates between the two definitions, and thus differences may be attributed to the specification of time to failure. For all patient groups, hazard rates were dependent upon failure definition: under the FCCC failure definition, patients were at constant risk of failure over the observation period; under the ASTRO failure definition, patients were at risk of failure during the first 4 years following treatment, and then at low risk of failure beyond 5 years. CONCLUSIONS: Both FCCC and ASTRO failure definitions were robust to modifications in censoring and the inclusion of patients with long doubling times. The ASTRO failure definition was robust to specifying the time to failure at first rise, as opposed to midway between nadir and first rise. Similarities in estimates for all patients versus patients with agreeing failure status suggest that differences in failure definition lie in the specification of time to failure. The ASTRO definition of failure is more appropriate because it does not impose an empirical failure marker but is based on the initiation of biochemical rise. The use of the ASTRO consensus definition demonstrated little risk of biochemical failure 4 years beyond treatment. The ASTRO failure definition should be adopted in all research involving biochemical failure analysis of men treated with radiation therapy.

Aged↗

PubMed2003

Impact of number of isoenzyme loci on the robustness of intraspecific phylogenies using multilocus enzyme electrophoresis: consequences for typing of Trypanosoma cruzi.

Thirty-one stocks of Trypanosoma cruzi, the agent of Chagas disease, representative of the genetic variability of the 2 principal lineages, that subdivide T. cruzi, were selected on the basis of previous multilocus enzyme electrophoresis analysis using 21 loci. Analyses were performed with lower numbers of loci to explore the impact of the number of loci on the robustness of the phylogenies obtained, and to identify the loci that have more impact on the phylogeny. Analyses were performed with numerical (UPGMA) and cladistical (Wagner parsimony analysis) methods for all sets of loci. Robustness of the phylogenies obtained was estimated by bootstrap analysis. Low numbers of randomly selected loci (6) were sufficient to demonstrate genetic heterogeneity among the stocks studied. However, they were unable to give reliable phylogenetic information. A higher number of randomly selected loci (15 and more) were required to reach this goal. All loci did not convey equivalent information. The more variable loci detected a greater genetic heterogeneity among the stocks, whereas the least variable loci were better for robust clustering. Finally, analysis was performed with only 5 and 9 loci bearing synapomorphic allozyme characters previously identified among larger samples of stocks. A set of 9 such loci was able to uncover both genetic heterogeneity among the stocks and to build robust phylogenies. It can therefore be recommended as a minimum set of isoenzyme loci that bring maximal information for all studies aiming to explore the phylogenetic diversity of a new set of T. cruzi stocks and for any preliminary genetic typing. Moreover, our results show that bootstrap analysis, like any statistics, is highly dependent upon the information available and that absolute bootstrap figures should be cautiously interpreted.

Animals↗

PubMed2000

Robustness against mutations in genetic networks of yeast.

There are two principal mechanisms that are responsible for the ability of an organism's physiological and developmental processes to compensate for mutations. In the first, genes have overlapping functions, and loss-of-function mutations in one gene will have little phenotypic effect if there are one or more additional genes with similar functions. The second mechanism has its origin in interactions between genes with unrelated functions, and has been documented in metabolic and regulatory gene networks. Here I analyse, on a genome-wide scale, which of these mechanisms of robustness against mutations is more prevalent. I used functional genomics data from the yeast Saccharomyces cerevisiae to test hypotheses related to the following: if gene duplications are mostly responsible for robustness, then a correlation is expected between the similarity of two duplicated genes and the effect of mutations in one of these genes. My results demonstrate that interactions among unrelated genes are the major cause of robustness against mutations. This type of robustness is probably an evolved response of genetic networks to stabilizing selection.

Computational Biology↗

PubMed2002

Robustness of the BMP morphogen gradient in Drosophila embryonic patterning.

Developmental patterning relies on morphogen gradients, which generally involve feedback loops to buffer against perturbations caused by fluctuations in gene dosage and expression. Although many gene components involved in such feedback loops have been identified, how they work together to generate a robust pattern remains unclear. Here we study the network of extracellular proteins that patterns the dorsal region of the Drosophila embryo by establishing a graded activation of the bone morphogenic protein (BMP) pathway. We find that the BMP activation gradient itself is robust to changes in gene dosage. Computational search for networks that support robustness shows that transport of the BMP class ligands (Scw and Dpp) into the dorsal midline by the BMP inhibitor Sog is the key event in this patterning process. The mechanism underlying robustness relies on the ability to store an excess of signalling molecules in a restricted spatial domain where Sog is largely absent. It requires extensive diffusion of the BMP-Sog complexes, coupled with restricted diffusion of the free ligands. We show experimentally that Dpp is widely diffusible in the presence of Sog but tightly localized in its absence, thus validating a central prediction of our theoretical study.

Animals↗

PubMed2000

On power and efficiency robust linkage tests for affected sibs.

For diseases that do not follow a clear Mendelian pattern of inheritance nonparametric tests applied to affected sibs have been shown to be robust to the inherent uncertainty about the precise underlying genetic model. It is known that the weights optimizing the power of tests using IBD alleles shared by affected sib pairs or triples depend on the underlying model. We show how efficiency robustness techniques, used in other areas of statistics, provide a systematic approach for constructing a robust linear combination of the statistics that are optimal for the individual members of a family of plausible genetic models. The method depends on the correlation matrix of the optimal tests as these correlations reflect how different the models are. When the minimal correlation is less than 0.5, an alternate robust procedure is proposed. The methods apply to combining data from sibships of different sizes.

Automation↗

PubMed2004

Analysis of structural robustness of metabolic networks.

We study the structural robustness of metabolic networks on the basis of the concept of elementary flux modes. It is shown that the number of elementary modes itself is not an appropriate measure of structural robustness. Instead, we introduce three new robustness measures. These are based on the relative number of elementary modes remaining after the knockout of enzymes. We discuss the relevance of these measures with the help of simple examples, as well as with larger, realistic metabolic networks. Thereby we demonstrate quantitatively that the metabolism of Escherichia coli, which must be able to adapt to varying conditions, is more robust than the metabolism of the human erythrocyte, which lives under much more homeostatic conditions.

Adaptation, Physiological↗

PubMed2002

Correction of analytical results for recovery: a comparison of the method performance characteristics from recent collaborative trials studies for aflatoxin quantification using conventional and robust statistics.

Results from recently conducted collaborative trials on the determination of aflatoxin B(1) in various matrices have been evaluated to establish whether the use of recovery data would result in a distinct change of the relative between-laboratory standard deviation (RSD(R)) of the corrected data compared with the uncorrected data. In addition, we applied conventional and robust statistics to evaluate whether the impact of the use of recovery data on the estimation of RSD(R) depended on the statistical method applied for data analysis. This investigation was based on means before and after correction for recovery. The method performance characteristics were calculated using results from naturally contaminated test materials, while the results from test materials fortified with the target analytes were used to estimate the recovery. The study revealed that applying conventional and robust statistics in general led to comparable estimates for RSD(R). The comparison about the use of recovery data showed that in most cases, the RSD(R) obtained from the analysis of aflatoxin B(1) decreased after correction of the results for recovery. This tendency was similar when the comparison was done using robust or conventional statistics. However, in three cases, conventional statistics yielded a higher RSD(R) for the corrected data, whereas robust statistics showed the opposite. Looking carefully at the data, the treatment of conventional statistics indicated that the way outliers are detected and removed could result in an under- or overestimation of RSD(R). Applying the law of error propagation revealed that most likely the correlation between the uncorrected data and the recovery rate led to a reduced variability of the data corrected for recovery.

Aflatoxin B1↗

PubMed1998

Simple, robust linkage tests for affected sibs.

Parametric-linkage analysis applied to large pedigrees with many affected individuals has helped in the identification of highly penetrant genes; but, for diseases lacking a clear Mendelian inheritance pattern or caused by several genes of low to moderate penetrance, a more robust strategy is nonparametric analysis applied to small sets of affected relatives, such as affected sib pairs. Here we show that the robustness of affected-sib-pair tests is related to the shape of the constraint set for the sibs' identity-by-descent (IBD) probabilities. We also derive a set of constraints for the IBD probabilities of affected sib triples and use common features of the shapes of the two constrain sets to introduce new nonparametric tests (called "minmax" tests) that are more robust than those in current use. Asymptotic-power computations support the robustness of the proposed minmax tests.

Automation↗

PubMed2005

Info-gap robust-satisficing model of foraging behavior: do foragers optimize or satisfice?

In this note we compare two mathematical models of foraging that reflect two competing theories of animal behavior: optimizing and robust satisficing. The optimal-foraging model is based on the marginal value theorem (MVT). The robust-satisficing model developed here is an application of info-gap decision theory. The info-gap robust-satisficing model relates to the same circumstances described by the MVT. We show how these two alternatives translate into specific predictions that at some points are quite disparate. We test these alternative predictions against available data collected in numerous field studies with a large number of species from diverse taxonomic groups. We show that a large majority of studies appear to support the robust-satisficing model and reject the optimal-foraging model.

Animal Feed↗

PubMed2026

Robust prioritization of genomic features with stability selection.

MOTIVATION: The heterogeneity of complex diseases including cancer leads to heavy-tailed distributions in the disease traits. In such settings, non-robust variable selection methods are inherently susceptible to data contamination and can yield unstable or misleading results. This vulnerability becomes more severe for recently proposed approaches that introduce pseudo-features as negative controls, as these methods further amplify the curse of dimensionality by expanding the genotype matrix in the presence of outliers and high-dimensional genomic features. RESULTS: We develop a robust variable selection framework with stability selection to prioritize genomic features in the presence of contamination. In contrast to existing approaches that rely on pseudo-features for error control, the proposed method achieves double robustness. First, it adopts least absolute deviation (LAD) LASSO to ensure robustness against outliers and heavy-tailed errors in disease traits. Second, it avoids augmenting the genotype matrix with pseudo-features, thereby mitigating the curse of dimensionality that is particularly problematic in high-dimensional genomic data. The proposed method has been extensively evaluated in simulation studies to demonstrate its effectiveness over multiple competing methods for variable selection. In addition, we have applied the proposed method and competing approaches to two real-data case studies: the The Cancer Genome Atlas (TCGA) Skin Cutaneous Melanoma (SKCM) dataset and an eQTL dataset. The results demonstrate that the proposed method achieves superior performance by identifying genomic features with higher reproducibility. AVAILABILITY AND IMPLEMENTATION: The source code for implementing the proposed methods is publicly available at https://github.com/cenwu/RSS with an archival DOI https://doi.org/10.6084/m9.figshare.32306883.

Genomics↗