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PubMed2002

Robust regression with asymmetric heavy-tail noise distributions.

In the presence of a heavy-tail noise distribution, regression becomes much more difficult. Traditional robust regression methods assume that the noise distribution is symmetric, and they downweight the influence of so-called outliers. When the noise distribution is asymmetric, these methods yield biased regression estimators. Motivated by data-mining problems for the insurance industry, we propose a new approach to robust regression tailored to deal with asymmetric noise distribution. The main idea is to learn most of the parameters of the model using conditional quantile estimators (which are biased but robust estimators of the regression) and to learn a few remaining parameters to combine and correct these estimators, to minimize the average squared error in an unbiased way. Theoretical analysis and experiments show the clear advantages of the approach. Results are on artificial data as well as insurance data, using both linear and neural network predictors.

Algorithms↗

PubMed2005

Robust reinforcement learning.

This letter proposes a new reinforcement learning (RL) paradigm that explicitly takes into account input disturbance as well as modeling errors. The use of environmental models in RL is quite popular for both offline learning using simulations and for online action planning. However, the difference between the model and the real environment can lead to unpredictable, and often unwanted, results. Based on the theory of H(infinity) control, we consider a differential game in which a "disturbing" agent tries to make the worst possible disturbance while a "control" agent tries to make the best control input. The problem is formulated as finding a min-max solution of a value function that takes into account the amount of the reward and the norm of the disturbance. We derive online learning algorithms for estimating the value function and for calculating the worst disturbance and the best control in reference to the value function. We tested the paradigm, which we call robust reinforcement learning (RRL), on the control task of an inverted pendulum. In the linear domain, the policy and the value function learned by online algorithms coincided with those derived analytically by the linear H(infinity) control theory. For a fully nonlinear swing-up task, RRL achieved robust performance with changes in the pendulum weight and friction, while a standard reinforcement learning algorithm could not deal with these changes. We also applied RRL to the cart-pole swing-up task, and a robust swing-up policy was acquired.

Learning↗

PubMed2004

Evolution of robustness in digital organisms.

We study the evolution of robustness in digital organisms adapting to a high mutation rate. As genomes adjust to the harsh mutational environment, the mean effect of single mutations decreases, up until the point where a sizable fraction (up to 30% in many cases) of the mutations are neutral. We correlate the changes in robustness along the line of descent to changes in directional epistasis, and find that increased robustness is achieved by moving from antagonistic epistasis between mutations towards codes where mutations are, on average, independent. We interpret this recoding as a breakup of linkage between vital sections of the genome, up to the point where instructions are maximally independent of each other. While such a recoding often requires sacrificing some replication speed, it is the best strategy for withstanding high rates of mutation.

Biological Evolution↗

PubMed2004

A sensitive, robust high-throughput electrochemiluminescence assay for rat insulin.

In diabetes research, mouse and rat models are used for in vivo experiments, and quantification of insulin in serum samples under different pathophysiological conditions and after treatment with compounds is essential. There are few commercial radioimmunoassay and enzyme-linked immunosorbent assay (ELISA) assay kits to determine the rat/mouse plasma levels of insulin. However, reliability in insulin measurements using the available biological assays is a great concern. The authors report a robust, extremely sensitive electrochemiluminescence (ECL) insulin assay using the Origen technology platform. The assay performance, as judged by the Z' value of 0.82+/-0.03 and the signal-to-background (S/B) ratio of 133, suggests that this is a robust and reliable assay. The intra-assay and interassay variation is less than 5%. The dynamic range of detection for insulin is 5 pg to 5 ng in the ECL assays. Recovery of insulin was about 100% when different volumes of serum were spiked with exogenous insulin. These results suggest that the ECL insulin assay is an extremely sensitive, robust, nonradioactive homogeneous assay and can be used successfully to determine the insulin levels in rodent serum samples.

Animals↗

PubMed2005

Are scale-free networks robust to measurement errors?

BACKGROUND: Many complex random networks have been found to be scale-free. Existing literature on scale-free networks has rarely considered potential false positive and false negative links in the observed networks, especially in biological networks inferred from high-throughput experiments. Therefore, it is important to study the impact of these measurement errors on the topology of the observed networks. RESULTS: This article addresses the impact of erroneous links on network topological inference and explores possible error mechanisms for scale-free networks with an emphasis on Saccharomyces cerevisiae protein interaction networks. We study this issue by both theoretical derivations and simulations. We show that the ignorance of erroneous links in network analysis may lead to biased estimates of the scale parameter and recommend robust estimators in such scenarios. Possible error mechanisms of yeast protein interaction networks are explored by comparisons between real data and simulated data. CONCLUSION: Our studies show that, in the presence of erroneous links, the connectivity distribution of scale-free networks is still scale-free for the middle range connectivities, but can be greatly distorted for low and high connecitivities. It is more appropriate to use robust estimators such as the least trimmed mean squares estimator to estimate the scale parameter gamma under such circumstances. Moreover, we show by simulation studies that the scale-free property is robust to some error mechanisms but untenable to others. The simulation results also suggest that different error mechanisms may be operating in the yeast protein interaction networks produced from different data sources. In the MIPS gold standard protein interaction data, there appears to be a high rate of false negative links, and the false negative and false positive rates are more or less constant across proteins with different connectivities. However, the error mechanism of yeast two-hybrid data may be very different, where the overall false negative rate is low and the false negative rates tend to be higher for links involving proteins with more interacting partners.

Algorithms↗

PubMed2005

Towards precise classification of cancers based on robust gene functional expression profiles.

BACKGROUND: Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level. RESULTS: Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s) for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles. CONCLUSION: This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge has facilitated the interpretation of the underlying molecular mechanisms for complex human diseases at the modular level.

Algorithms↗

PubMed2005

Robust trend tests for genetic association in case-control studies using family data.

We studied a trend test for genetic association between disease and the number of risk alleles using case-control data. When the data are sampled from families, this trend test can be adjusted to take into account the correlations among family members in complex pedigrees. However, the test depends on the scores based on the underlying genetic model and thus it may have substantial loss of power when the model is misspecified. Since the mode of inheritance will be unknown for complex diseases, we have developed two robust trend tests for case-control studies using family data. These robust tests have relatively good power for a class of possible genetic models. The trend tests and robust trend tests were applied to a dataset of Genetic Analysis Workshop 14 from the Collaborative Study on the Genetics of Alcoholism.

Alcoholism↗

PubMed2002

Hepatitis C virus whole genome position weight matrix and robust primer design.

BACKGROUND: The high degree of sequence heterogeneity found in Hepatitis C virus (HCV) isolates, makes robust nucleic acid-based assays difficult to generate. Polymerase chain reaction based techniques, require efficient and specific sequence recognition. Generation of robust primers capable of recognizing a wide range of isolates is a difficult task. RESULTS: A position weight matrix (PWM) and a consensus sequence were built for each region of HCV and subsequently assembled into a whole genome consensus sequence and PWM. For each of the 10 regions, the number of occurrences of each base at a given position was compiled. These counts were converted to frequencies that were used to calculate log odds scores. Using over 100 complete and 14,000 partial HCV genomes from GenBank, a consensus HCV genome sequence was generated along with a PWM reflecting heterogeneity at each position. The PWM was used to identify the most conserved regions for primer design. CONCLUSIONS: This approach allows rapid identification of conserved regions for robust primer design and is broadly applicable to sets of genomes with all levels of genetic heterogeneity.

Conserved Sequence↗

PubMed2006

Longitudinal robustness of variables predicting independent gait following severe middle cerebral artery stroke: a prospective cohort study.

OBJECTIVE: To determine within the first 10 weeks post onset the most robust variables in the prediction of recovery of independent gait at six months post stroke. DESIGN: A prospective cohort study. SUBJECTS: One hundred and one first ever ischaemic middle cerebral artery stroke patients. None of these patients were able to walk at onset and all suffered from a marked hemiplegia. SETTING: Twenty-four determinants, possibly related to recovery of gait at six months, were measured within 14 days following stroke onset. Based on Functional Ambulation Categories (FAC) independent gait was classified into present (FAC > or = 4) or absent (FAC < 4). Bivariate logistic regression analysis was used to select determinants. Only significant determinants during the entire 10-week period were used for further weekly multivariate logistic prediction modelling of independent gait at six months post stroke. RESULTS: After six months post onset 62% (N = 63) regained independent gait. Age, Barthel Index, Trunk Control Test, Motricity Index of arm and leg, Brunnstrom Fugl-Meyer stage of leg motor recovery, and type of intervention were significant determinants in bivariate analysis, but age of patient and Barthel Index were the most robust determinants in the final prediction model. Weekly re-evaluation produced sensitivity values between 89% and 96% and specificity values between 53% and 62%. CONCLUSION: In initially non-ambulatory stroke patients age and Barthel Index were the most robust variables during the first 10-week poststroke period in the prediction of independent walking at six months. However, prediction of non-ambulation at six months proved to be less accurate.

Adult↗

PubMed1998

Drilling of bone: a robust automatic method for the detection of drill bit break-through.

The aim of this investigation is to devise a robust detection method for drill bit break-through when drilling into long bones using an automated drilling system that is associated with mechatronic assisted surgery. This investigation looks into the effects of system compliance and inherent drilling force fluctuation on the profiles of drilling force, drilling force, drilling between successive samples and drill bit rotational speed. It is shown that these effects have significant influences on the bone drilling related profiles and thus on the detection of drill bit break-through. A robust method, based on a Kalman filter, has been proposed. Using a modified Kalman filter, it is possible to convert the profiles of drilling force difference between successive samples and/or the drill bit rotational speed into easily recognizable and more consistent profiles, allowing a robust and repeatable detection of drill bit break-through.

Algorithms↗

PubMed2000

Robust transformation with applications to structural equation modelling.

Data sets in social and behavioural sciences are seldom normal. Influential cases or outliers can lead to inappropriate solutions and problematic conclusions in structural equation modelling. By giving a proper weight to each case, the influence of outliers on a robust procedure can be minimized. We propose using a robust procedure as a transformation technique, generating a new data matrix that can be analysed by a variety of multivariate methods. Mardia's multivariate skewness and kurtosis statistics are used to measure the effect of the transformation in achieving approximate normality. Since the transformation makes the data approximately normal, applying a classical normal theory based procedure to the transformed data gives more efficient parameter estimates. Three procedures for parameter evaluation and model testing are discussed. Six examples illustrate the various aspects with the robust transformation.

Humans↗

PubMed2006

Module-based analysis of robustness tradeoffs in the heat shock response system.

Biological systems have evolved complex regulatory mechanisms, even in situations where much simpler designs seem to be sufficient for generating nominal functionality. Using module-based analysis coupled with rigorous mathematical comparisons, we propose that in analogy to control engineering architectures, the complexity of cellular systems and the presence of hierarchical modular structures can be attributed to the necessity of achieving robustness. We employ the Escherichia coli heat shock response system, a strongly conserved cellular mechanism, as an example to explore the design principles of such modular architectures. In the heat shock response system, the sigma-factor sigma32 is a central regulator that integrates multiple feedforward and feedback modules. Each of these modules provides a different type of robustness with its inherent tradeoffs in terms of transient response and efficiency. We demonstrate how the overall architecture of the system balances such tradeoffs. An extensive mathematical exploration nevertheless points to the existence of an array of alternative strategies for the existing heat shock response that could exhibit similar behavior. We therefore deduce that the evolutionary constraints facing the system might have steered its architecture toward one of many robustly functional solutions.

Computer Simulation↗

PubMed2006

Funneled landscape leads to robustness of cell networks: yeast cell cycle.

We uncovered the underlying energy landscape for a cellular network. We discovered that the energy landscape of the yeast cell-cycle network is funneled towards the global minimum (G0/G1 phase) from the experimentally measured or inferred inherent chemical reaction rates. The funneled landscape is quite robust against random perturbations. This naturally explains robustness from a physical point of view. The ratio of slope versus roughness of the landscape becomes a quantitative measure of robustness of the network. The funneled landscape can be seen as a possible realization of the Darwinian principle of natural selection at the cellular network level. It provides an optimal criterion for network connections and design. Our approach is general and can be applied to other cellular networks.

Cell Cycle↗

PubMed2025

A novel and robust feature selection method with FDR control for omics-wide association analysis.

Omics-wide association analysis is a very important tool for medicine and human health study. However, the modern omics data sets collected often exhibit the high-dimensionality, unknown distribution response, unknown distribution features and unknown complex association relationships between the response and its explanatory features. Reliable association analysis results depend on an accurate modeling for such data sets. Most of the existing association analysis methods rely on the specific model assumptions and lack effective false discovery rate (FDR) control. To address these limitations, the paper firstly applies a single index model for omics data. The model shows robust performance in allowing the relationships between the response variable and linear combination of covariates to be connected by any unknown monotonic link function, and both the random error and the covariates can follow any unknown distribution. Then based on this model, the paper combines rank-based approach and symmetrized data aggregation approach to develop a novel and robust feature selection method for achieving fine-mapping of risk features while controlling the false positive rate of selection. The theoretical results support the proposed method and the analysis results of simulated data show the new method possesses effective and robust performance for all the scenarios. The new method is also used to analyze the two real datasets and identifies some risk features unreported by the existing finds.

Humans↗

PubMed2005

Robustness of burst firing in dissociated purkinje neurons with acute or long-term reductions in sodium conductance.

Cerebellar Purkinje neurons often generate all-or-none burst firing in response to depolarizing stimuli. Voltage-clamp experiments using action potential waveforms show that burst firing depends on small net inward currents that flow after spikes and reflect the net balance between multiple large currents. Given this, burst firing is surprisingly robust in the face of changes in the magnitude of the underlying currents from cell to cell. We explored the basis of this robustness by examining the effects of reducing the sodium current, the major contributor to the postspike inward current. Burst firing persisted in concentrations of tetrodotoxin that produced half-block of sodium current. This robustness of bursting reflects an acute feedback mechanism whereby waveform changes from the reduced sodium current (reduced spike height and a hyperpolarizing shift in postspike voltage) cause compensatory decreases in postspike potassium currents. In particular, reduced spike height reduces calcium entry and subsequent calcium-activated potassium current, and the hyperpolarizing shift in postspike voltage speeds deactivation of Kv3-like potassium channels. Other experiments examined bursting in Na(v)1.6-/- mice, in which sodium current density is reduced in the long term. Under these circumstances, there was upregulation of both T-type and P-type calcium current and a change in the balance of calcium current and calcium-activated potassium current such that their net influence shifted from being inhibitory during bursts in wild-type neurons to excitatory during bursts from Na(v)1.6-/- mutant neurons. Thus, Purkinje neurons have both acute and long-term feedback mechanisms that serve to maintain burst firing when voltage-dependent sodium conductance is reduced.

Action Potentials↗

PubMed2005

Robust growth of human immunodeficiency virus type 1 (HIV-1).

The persistence of human immunodeficiency virus type-1 (HIV-1) has long been attributed to its high mutation rate and the capacity of its resulting heterogeneous virus populations to evade host immune responses and antiviral drugs. However, this view is incomplete because it does not explain how the virus persists in light of the adverse effects mutations in the viral genome and variations in host functions can potentially have on viral functions and growth. Here we show that the resilience of HIV-1 can be credited, at least in part, to a robust response to perturbations that emerges as an intrinsic property of its intracellular development. Specifically, robustness in HIV-1 arises through the coupling of two feedback loops: a Rev-mediated negative feedback and a Tat-mediated positive feedback. By employing a mechanistic kinetic model for its growth we found that HIV-1 buffers the effects of many potentially detrimental variations in essential viral and cellular functions, including the binding of Rev to mRNA; the level of rev mRNA in the pool of fully spliced mRNA; the splicing of mRNA; the Rev-mediated nuclear export of incompletely-spliced mRNAs; and the nuclear import of Tat and Rev. The virus did not, however, perform robustly to perturbations in all functions. Notably, HIV-1 tended to amplify rather than buffer adverse effects of variations in the interaction of Tat with viral mRNA. This result shows how targeting therapeutics against molecular components of the viral positive-feedback loop open new possibilities and potential in the effective treatment of HIV-1.

Cell Proliferation↗

PubMed2006

Funneled landscape leads to robustness of cellular networks: MAPK signal transduction.

We uncover the underlying potential energy landscape for a cellular network. We find that the potential energy landscape of the mitogen-activated protein-kinase signal transduction network is funneled toward the global minimum. The funneled landscape is quite robust against random perturbations. This naturally explains robustness from a physical point of view. The ratio of slope versus roughness of the landscape becomes a quantitative measure of robustness of the network. Funneled landscape is a realization of the Darwinian principle of natural selection at the cellular network level. It provides an optimal criterion for network connections and design. Our approach is general and can be applied to other cellular networks.

Animals↗

PubMed2006

Population genetics of translational robustness.

Recent work has shown that expression level is the main predictor of a gene's evolutionary rate and that more highly expressed genes evolve slower. A possible explanation for this observation is selection for proteins that fold properly despite mistranslation, in short selection for translational robustness. Translational robustness leads to the somewhat paradoxical prediction that highly expressed genes are extremely tolerant to missense substitutions but nevertheless evolve very slowly. Here, we study a simple theoretical model of translational robustness that allows us to gain analytic insight into how this paradoxical behavior arises.

Evolution, Molecular↗