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PubMed2003

Automatic pre-segmentation of running speech improves the robustness of several acoustic voice measures.

In order to study vocal loading, we developed a speech analysis environment for continuous speech. The objective was to build a robust system capable of handling large amounts of data while minimizing the amount of user-intervention required. The current version of the system can analyze up to five-minute recordings of speech at a time. Through a semiautomatic process it will classify a speech signal into segments of silence, voiced speech and unvoiced speech. Parameters extracted from the input signal include fundamental frequency, sound pressure level, alpha-ratio and speech segment information such as the ratio of speech to silence. This paper presents results from the performance evaluation of the system, which shows that the analysis environment is able to perform robust and consistent measurements of continuous speech.

Algorithms↗

PubMed2002

Robust analysis of a mixed-effect model for a multicenter clinical trial.

We consider a multicenter clinical trial with treatments as fixed effects and centers and residuals as bivariate and univariate random effects, respectively. There exist situations where it is difficult to justify the conventional normality assumptions for the random components. Following Khatri and Patel (Commun. Stat.-Theory Methods 1992, 21, 21-39), we propose the weighted least-squares (WLS) method and two bootstrap methods, percentile and BCa, that are robust to the departure from normality. Through a simulation study, we compare WLS and bootstrap confidence intervals for the treatment difference. While all three methods give confidence intervals with desired coverage rates, the WLS method gives shorter intervals. We also propose a bootstrap method that is robust to outliers. A numerical example is given to illustrate the methodology.

Computer Simulation↗

PubMed2001

The effect that genotyping errors have on the robustness of common linkage-disequilibrium measures.

The rapid development of a dense single-nucleotide-polymorphism marker map has stimulated numerous studies attempting to characterize the magnitude and distribution of background linkage disequilibrium (LD) within and between human populations. Although genotyping errors are an inherent problem in all LD studies, there have been few systematic investigations documenting their consequences on estimates of background LD. Therefore, we derived simple deterministic formulas to investigate the effect that genotyping errors have on four commonly used LD measures-D', r, Q, and d-in studies of background LD. We have found that genotyping error rates as small as 3% can have serious affects on these LD measures, depending on the allele frequencies and the assumed error model. Furthermore, we compared the robustness of D', r, Q, and d, in the presence of genotyping errors. In general, Q and d are more robust than D' and r, although exceptions do exist. Finally, through stochastic simulations, we illustrate how genotyping errors can lead to erroneous inferences when measures of LD between two samples are compared.

Chromosome Mapping↗

PubMed2007

Robust fluoroscopic respiratory gating for lung cancer radiotherapy without implanted fiducial markers.

For gated lung cancer radiotherapy, it is difficult to generate accurate gating signals due to the large uncertainties when using external surrogates and the risk of pneumothorax when using implanted fiducial markers. We have previously investigated and demonstrated the feasibility of generating gating signals using the correlation scores between the reference template image and the fluoroscopic images acquired during the treatment. In this paper, we present an in-depth study, aiming at the improvement of robustness of the algorithm and its validation using multiple sets of patient data. Three different template generating and matching methods have been developed and evaluated: (1) single template method, (2) multiple template method, and (3) template clustering method. Using the fluoroscopic data acquired during patient setup before each fraction of treatment, reference templates are built that represent the tumour position and shape in the gating window, which is assumed to be at the end-of-exhale phase. For the single template method, all the setup images within the gating window are averaged to generate a composite template. For the multiple template method, each setup image in the gating window is considered as a reference template and used to generate an ensemble of correlation scores. All the scores are then combined to generate the gating signal. For the template clustering method, clustering (grouping of similar objects together) is performed to reduce the large number of reference templates into a few representative ones. Each of these methods has been evaluated against the reference gating signal as manually determined by a radiation oncologist. Five patient datasets were used for evaluation. In each case, gated treatments were simulated at both 35% and 50% duty cycles. False positive, negative and total error rates were computed. Experiments show that the single template method is sensitive to noise; the multiple template and clustering methods are more robust to noise due to the smoothing effect of aggregation of correlation scores; and the clustering method results in the best performance in terms of computational efficiency and accuracy.

Biophysical Phenomena↗

PubMed1997

Robust proofs of NP-hardness for protein folding: general lattices and energy potentials.

This paper addresses the robustness of intractability arguments for simplified models of protein folding that use lattices to discretize the space of conformations that a protein can assume. We present two generalized NP-hardness results. The first concerns the intractability of protein folding independent of the lattice used to define the discrete protein-folding model. We consider a previously studied model and prove that for any reasonable lattice the protein-structure prediction problem is NP-hard. The second hardness result concerns the intractability of protein folding for a class of energy formulas that contains a broad range of mean force potentials whose form is similar to commonly used pair potentials (e.g., the Lennard-Jones potential). We prove that protein-structure prediction is NP-hard for any energy formula in this class. These are the first robust intractability results that identify sources of computational complexity of protein-structure prediction that transcend particular problem formulations.

Models, Chemical↗

PubMed1999

Optimizing results of lithotripsy using robust electromagnetic probe.

BACKGROUND AND OBJECTIVE: A significant impediment to the measurement of the pressures and forces created by lithotripter shockwaves has been their destructive properties, which have rendered most measuring devices impractical. We have developed and tested a robust electromagnetic probe to measure cavitational forces in vitro in the focal zones of extracorporeal lithotripters. The probe responds to the pressure gradient generated by the radial motion of cavitation bubbles. MATERIALS AND METHODS: The effects of shockwaves from the Dornier MPL 9000 electrohydraulic lithotripter were measured over the lifetime of multiple electrodes. RESULTS: The pulse energy from the electrodes dropped off rapidly after approximately 50% of the lifetime quoted by the manufacturer. The electrodes were more efficient at higher power settings. As a result, we altered our protocol for the treatment of ureteral stones to use a higher kilovoltage and a second electrode whenever necessary. Stone-free rates after shockwave lithotripsy (SWL) in situ for stones < 11 mm have increased from 68.2% to 83.3%, and the retreatment rate has dropped from 23% to 15%. Despite significantly higher power settings (23.7 kV v 18.7 kV; P < 0.0001), the need for sedoanalgesia has remained relatively constant (26% v 31%). CONCLUSIONS: Measurement of cavitational forces from lithotripters using a robust electromagnetic probe is useful in planning treatment strategy. We have demonstrated a clinically measurable improvement since implementing our new treatment protocol. Because the probe responds directly to cavitational forces, it should also prove useful for the objective comparison of different SWL machines.

Electromagnetic Phenomena↗

PubMed2006

A robust approach for the quantitation of viral concentration in an adenoviral vector-based human immunodeficiency virus vaccine by real-time quantitative polymerase chain reaction.

A real-time quantitative polymerase chain reaction (PCR)-based method was developed to measure the concentration of recombinant adenoviral vector genomes in purified virus bulks and final container samples of monovalent and multivalent human immunodeficiency virus (HIV) adenoviral vector vaccine candidates. This method, referred to as the genome quantitation assay (GQA), was optimized through a rigorous approach for evaluating PCR detection chemistries, designing a robust assay format, and establishing a properly calibrated reference standard. In addition, the use of a simplified lysis procedure, automated liquid transfer system, and parallel-line data analysis contribute to an accurate, precise, reliable, and high-throughput assay procedure that can be used for process monitoring, final formulation, and release of vaccine products. A variance component analysis study indicated that the GQA typically produces results with an interassay precision of less than 10% relative standard deviation (RSD), allowing generation of final results (average of three runs) with associated interassay precision of 6% RSD or less. The precision, accuracy, specificity, and robustness of the GQA demonstrate its utility for analytical characterization of a wide variety of viral vector- and DNA plasmid- based vaccines or gene therapy products. In addition, we also evaluated the Adenovirus Reference Standard generated by the Adenovirus Reference Material Working Group in the GQA to provide a common point-of-reference for our analytical method.

AIDS Vaccines↗

PubMed2003

Cyclin aggregation and robustness of bio-switching.

During the cell cycle, Cdc2-cyclin B kinase abruptly becomes active and triggers the entry into mitosis/meiosis. Recently, it was found that inactive Cdc2-cyclin B is present in aggregates in immature starfish oocytes and becomes disaggregated at the time of its activation during maturation. We discuss a possible scenario in which aggregation of Cdc2-cyclin B dramatically enhances robustness of this activation. In this scenario, only inactive Cdc2-cyclin B can form aggregates, and the aggregates are in equilibrium with inactive Cdc2-cyclin B in solution. During maturation, the hormone-triggered inactivation of Myt1 depletes the soluble inactive Cdc2-cyclin B and the turnover leads to dissolution of the aggregates. This phase change, when coupled with the instability of the signaling network, provides a robust bio-switch.

Animals↗

PubMed2002

Robust estimators for expression analysis.

MOTIVATION: We consider the problem of estimating values associated with gene expression from oligonucleotide arrays. Such estimates should linearly track concentration, yield non-negative results, have statistical guarantees of robustness against outliers, and allow estimates of significance and variance. RESULTS: A hierarchy of simple models is used to design robust estimators meeting these goals for both stand alone and comparative experiments. This algorithm has been validated against an extensive panel of known spike experiments, and shows comparable performance to existing standards.

Algorithms↗

PubMed2025

PathoSeq-QC: a decision support bioinformatics workflow for robust genomic surveillance.

MOTIVATION: Recommendations on the use of genomics for pathogens surveillance are evidence that high-throughput genomic sequencing plays a key role to fight global health threats. Coupled with bioinformatics and other data types (e.g., epidemiological information), genomics is used to obtain knowledge on health pathogenic threats and insights on their evolution, to monitor pathogens spread, and to evaluate the effectiveness of countermeasures. From a decision-making policy perspective, it is essential to ensure the entire process's quality before relying on analysis results as evidence. Available workflows usually offer quality assessment tools that are primarily focused on the quality of raw NGS reads but often struggle to keep pace with new technologies and threats, and fail to provide a robust consensus on results, necessitating manual evaluation of multiple tool outputs. RESULTS: We present PathoSeq-QC, a bioinformatics decision support workflow developed to improve the trustworthiness of genomic surveillance analyses and conclusions. Designed for SARS-CoV-2, it is suitable for any viral threat. In the specific case of SARS-CoV-2, PathoSeq-QC: (i) evaluates the quality of the raw data; (ii) assesses whether the analysed sample is composed by single or multiple lineages; (iii) produces robust variant calling results via multi-tool comparison; (iv) reports whether the produced data are in support of a recombinant virus, a novel or an already known lineage. The tool is modular, which will allow easy functionalities extension. AVAILABILITY AND IMPLEMENTATION: PathoSeq-QC is a command-line tool written in Python and R. The code is available at https://code.europa.eu/dighealth/pathoseq-qc.

Genomics↗

PubMed2026

ProMeta: a meta-learning framework for robust disease diagnosis and prediction from plasma proteomics.

MOTIVATION: The plasma proteome offers a dynamic window of human health, capturing the real-time intersections between genetics and physiology. However, the application of deep learning to proteomics is currently hindered by a reliance on large-scale labeled datasets, rendering standard models ineffective for rare or novel diseases where patient samples are inherently scarce. RESULTS: Here, we present ProMeta, a meta-learning framework designed to enable robust disease modeling under extreme data restrictions. By integrating knowledge-guided pathway encoding with bi-level meta-optimization, ProMeta projects unstructured proteomic profiles into biologically interpretable functional tokens. This architecture allows the model to learn a global initialization containing transferable biological priors from biobank-scale data, facilitating rapid adaptation to novel tasks. Through comprehensive benchmark experiments, ProMeta consistently outperformed transfer learning and traditional machine learning baselines in both disease diagnosis and prediction tasks. In the most challenging 4-shot scenarios (utilizing only 2 cases and 2 controls), the model achieved robust generalization with an average AUROC of ∼0.69, representing a 24.6% relative improvement over the best-performing baseline methods. Mechanistic investigation revealed that ProMeta disentangles cases from controls in the latent space prior to task-specific adaptation, confirming the acquisition of universal biological rules rather than rote memorization. Furthermore, gradient-based interpretation identified disease-specific protein biomarkers and functional pathways consistent with known pathophysiology. Collectively, ProMeta overcomes the data-scarcity bottleneck in precision medicine, providing a scalable, interpretable framework for characterizing the full spectrum of human diseases, particularly for rare conditions lacking extensive clinical cohorts. AVAILABILITY AND IMPLEMENTATION: The source code of ProMeta is available at GitHub (https://github.com/lihan97/ProMeta).

Proteomics↗

PubMed2026

GT-Mamba: a Topology-Aware Graph-State space model for robust and interpretable epigenetic age prediction.

MOTIVATION: Current epigenetic clocks face a trade-off between predictive accuracy and biological interpretability, often relying on dataset-specific correction to generalize across cohorts. We propose GT-Mamba, a novel architecture that integrates a Structure-Aware Graph Transformer with the Mamba state space model. This design captures CpG topological correlations and genome-wide long-range dependencies. RESULTS: GT-Mamba demonstrates strong out-of-the-box robustness across heterogeneous independent validation cohorts, achieving a weighted average MAE of 4.43 years. Notably, it effectively generalizes to EPIC 850k arrays despite partial feature missingness, and maintains consistent performance across homologous age distribution shifts (MAE 2.94 years in a young cohort). Ablation studies confirm that graph topology contributes to improved robustness against noise. Mechanistic analysis suggests that the model captures methylation patterns associated with both developmental and functional processes. AVAILABILITY: Source code and pre-trained models are freely available at https://github.com/NENUBioCompute/GT-Mamba and archived on Zenodo (DOI: 10.5281/zenodo.19703155).

Epigenesis, Genetic↗

PubMed2004

A practical and robust sequence search strategy for structural genomics target selection.

MOTIVATION: Target selection strategies for structural genomic projects must be able to prioritize gene regions on the basis of significant sequence similarity with proteins that have already been structurally determined. With the rapid development of protein comparison software a robust prioritization scheme should be independent of the choice of algorithm and be able to incorporate different sequence similarity thresholds. RESULTS: A robust target selection strategy has been developed that can assign a priority level to all genes in any genome. Structural assignments to genome sequences are calculated at two thresholds and six levels (1-6) describe the prioritization of all whole genes and partial gene regions. This simple two-threshold approach can be implemented with any fold recognition or homology detection algorithms. The results for 10 genomes are presented using the SSEARCH and PSI-BLAST programs. AVAILABILITY: Programs are available on request from the authors.

Algorithms↗

PubMed2005

Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data.

MOTIVATION: Microarray data are used in a range of application areas in biology, although often it contains considerable numbers of missing values. These missing values can significantly affect subsequent statistical analysis and machine learning algorithms so there is a strong motivation to estimate these values as accurately as possible before using these algorithms. While many imputation algorithms have been proposed, more robust techniques need to be developed so that further analysis of biological data can be accurately undertaken. In this paper, an innovative missing value imputation algorithm called collateral missing value estimation (CMVE) is presented which uses multiple covariance-based imputation matrices for the final prediction of missing values. The matrices are computed and optimized using least square regression and linear programming methods. RESULTS: The new CMVE algorithm has been compared with existing estimation techniques including Bayesian principal component analysis imputation (BPCA), least square impute (LSImpute) and K-nearest neighbour (KNN). All these methods were rigorously tested to estimate missing values in three separate non-time series (ovarian cancer based) and one time series (yeast sporulation) dataset. Each method was quantitatively analyzed using the normalized root mean square (NRMS) error measure, covering a wide range of randomly introduced missing value probabilities from 0.01 to 0.2. Experiments were also undertaken on the yeast dataset, which comprised 1.7% actual missing values, to test the hypothesis that CMVE performed better not only for randomly occurring but also for a real distribution of missing values. The results confirmed that CMVE consistently demonstrated superior and robust estimation capability of missing values compared with other methods for both series types of data, for the same order of computational complexity. A concise theoretical framework has also been formulated to validate the improved performance of the CMVE algorithm. AVAILABILITY: The CMVE software is available upon request from the authors.

Algorithms↗

PubMed2005

REVCOM: a robust Bayesian method for evolutionary rate estimation.

MOTIVATION: Evolutionary conservation estimated from a multiple sequence alignment is a powerful indicator of the functional significance of a residue and helps to predict active sites, ligand binding sites, and protein interaction interfaces. Many algorithms that calculate conservation work well, provided an accurate and balanced alignment is used. However, such a strong dependence on the alignment makes the results highly variable. We attempted to improve the conservation prediction algorithm by making it more robust and less sensitive to (1) local alignment errors, (2) overrepresentation of sequences in some branches and (3) occasional presence of unrelated sequences. RESULTS: A novel method is presented for robust constrained Bayesian estimation of evolutionary rates that avoids overfitting independent rates and satisfies the above requirements. The method is evaluated and compared with an entropy-based conservation measure on a set of 1494 protein interfaces. We demonstrated that approximately 62% of the analyzed protein interfaces are more conserved than the remaining surface at the 5% significance level. A consistent method to incorporate alignment reliability is proposed and demonstrated to reduce arbitrary variation of calculated rates upon inclusion of distantly related or unrelated sequences into the alignment.

Algorithms↗

PubMed2005

Robust multi-scale clustering of large DNA microarray datasets with the consensus algorithm.

MOTIVATION: Hierarchical and relocation clustering (e.g. K-means and self-organizing maps) have been successful tools in the display and analysis of whole genome DNA microarray expression data. However, the results of hierarchical clustering are sensitive to outliers, and most relocation methods give results which are dependent on the initialization of the algorithm. Therefore, it is difficult to assess the significance of the results. We have developed a consensus clustering algorithm, where the final result is averaged over multiple clustering runs, giving a robust and reproducible clustering, capable of capturing small signal variations. The algorithm preserves valuable properties of hierarchical clustering, which is useful for visualization and interpretation of the results. RESULTS: We show for the first time that one can take advantage of multiple clustering runs in DNA microarray analysis by collecting re-occurring clustering patterns in a co-occurrence matrix. The results show that consensus clustering obtained from clustering multiple times with Variational Bayes Mixtures of Gaussians or K-means significantly reduces the classification error rate for a simulated dataset. The method is flexible and it is possible to find consensus clusters from different clustering algorithms. Thus, the algorithm can be used as a framework to test in a quantitative manner the homogeneity of different clustering algorithms. We compare the method with a number of state-of-the-art clustering methods. It is shown that the method is robust and gives low classification error rates for a realistic, simulated dataset. The algorithm is also demonstrated for real datasets. It is shown that more biological meaningful transcriptional patterns can be found without conservative statistical or fold-change exclusion of data. AVAILABILITY: Matlab source code for the clustering algorithm ClusterLustre, and the simulated dataset for testing are available upon request from T.G. and O.W.

Algorithms↗

PubMed1997

Linkage mapping in experimental crosses: the robustness of single-gene models.

The robustness of parametric linkage mapping against model misspecification is considered in experimental breeding designs, with a focus on localization of the gene. By examining the expected LOD across the genome, it is shown that single-gene models are quite robust, even for polygenic traits. However, when the marker map is of low resolution, linked polygenes can give rise to an apparent "ghost" gene, mapped to an incorrect interval. The results apply equally well to quantitative traits or qualitative (categorical) traits. The results are derived for backcross populations, with a discussion of extensions to intercross populations and relative-pair mapping in humans.

Crosses, Genetic↗

PubMed1989

The robustness of the terminal decline phenomenon: longitudinal data from the Digit-Span Memory Test.

The robustness of terminal decline was studied by analyzing longitudinal results from the simple Digit-Span Memory Test. The subjects participated in the Gothenburg longitudinal study and were first examined at the age of 70. Analyses were conducted for different groups according to subsequent survival. The findings provided support for the robustness of the terminal decline phenomenon.

Aged↗