Scalable estimator of the diversity for de novo molecular generation resulting in a more robust QM dataset (OD9) and a more efficient molecular optimization.
Ontology highlight
ABSTRACT:
SUBMITTER: Leguy J
PROVIDER: S-EPMC8487551 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Multidimensional datapoint clouds representing large datasets are frequently characterized by non-trivial low-dimensional geometry and topology which can be recovered by unsupervised machine learning approaches, in particular, by principal graphs. Principal graphs approximate the multivariate data by a graph injected into the data space with some constraints imposed on the node mapping. Here we present ElPiGraph, a scalable and robust method for constructing principal graphs. ElPiGraph exploits and further develops the concept of elastic energy, the topological graph grammar approach, and a gradient descent-like optimization of the graph topology. The method is able to withstand high levels of noise and is capable of approximating data point clouds via principal graph ensembles. This strategy can be used to estimate the statistical significance of complex data features and to summarize them into a single consensus principal graph. ElPiGraph deals efficiently with large datasets in various fields such as biology, where it can be used for example with single-cell transcriptomic or epigenomic datasets to infer gene expression dynamics and recover differentiation landscapes.
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Project description:MotivationAccurate orthology inference is a fundamental step in many phylogenetics and comparative analysis. Many methods have been proposed, including OMA (Orthologous MAtrix). Yet substantial challenges remain, in particular in coping with fragmented genes or genes evolving at different rates after duplication, and in scaling to large datasets. With more and more genomes available, it is necessary to improve the scalability and robustness of orthology inference methods.ResultsWe present improvements in the OMA algorithm: (i) refining the pairwise orthology inference step to account for same-species paralogs evolving at different rates, and (ii) minimizing errors in the pairwise orthology verification step by testing the consistency of pairwise distance estimates, which can be problematic in the presence of fragmentary sequences. In addition we introduce a more scalable procedure for hierarchical orthologous group (HOG) clustering, which are several orders of magnitude faster on large datasets. Using the Quest for Orthologs consortium orthology benchmark service, we show that these changes translate into substantial improvement on multiple empirical datasets.Availability and implementationThis new OMA 2.0 algorithm is used in the OMA database ( http://omabrowser.org ) from the March 2017 release onwards, and can be run on custom genomes using OMA standalone version 2.0 and above ( http://omabrowser.org/standalone ).Contactchristophe.dessimoz@unil.ch or adrian.altenhoff@inf.ethz.ch.
| S-EPMC5870696 | biostudies-literature
Project description:The Two-Sample Two-Stage Least Squares (TS2SLS) data combination estimator is a popular estimator for the parameters in linear models when not all variables are observed jointly in one single data set. Although the limiting normal distribution has been established, the asymptotic variance formula has only been stated explicitly in the literature for the case of conditional homoskedasticity. By using the fact that the TS2SLS estimator is a function of reduced form and first-stage OLS estimators, we derive the variance of the limiting normal distribution under conditional heteroskedasticity. A robust variance estimator is obtained, which generalises to cases with more general patterns of variable (non-)availability. Stata code and some Monte Carlo results are provided in an Appendix. Stata code for a nonlinear GMM estimator that is identical to the TS2SLS estimator in just identified models and asymptotically equivalent to the TS2SLS estimator in overidentified models is also provided there.
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Project description:BackgroundDistance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.ResultsWe propose a correction-based protein sequence estimator called Scoredist. It uses a logarithmic correction of observed divergence based on the alignment score according to the BLOSUM62 score matrix. We evaluated Scoredist and a number of optimal matrix methods using three evolutionary models for both training and testing Dayhoff, Jones-Taylor-Thornton, and Muller-Vingron, as well as Whelan and Goldman solely for testing. Test alignments with known distances between 0.01 and 2 substitutions per position (1-200 PAM) were simulated using ROSE. Scoredist proved as accurate as the optimal matrix methods, yet substantially more robust. When trained on one model but tested on another one, Scoredist was nearly always more accurate. The Jukes-Cantor and Kimura correction methods were also tested, but were substantially less accurate.ConclusionThe Scoredist distance estimator is fast to implement and run, and combines robustness with accuracy. Scoredist has been incorporated into the Belvu alignment viewer, which is available at ftp://ftp.cgb.ki.se/pub/prog/belvu/.
| S-EPMC1131889 | biostudies-literature
Project description:Born-Oppenheimer molecular dynamics (BOMD) is a promising simulation method for exploring the possible reaction pathways of a chemical system, but one significant challenge is the increased difficulty of converging the self-consistent field (SCF) calculation that often accompanies the breaking and forming of chemical bonds. To address this challenge, we developed an enhancement to the BOMD simulation method called the Car-Parrinello monitor (CPMonitor) that uses Car-Parrinello molecular dynamics (CPMD) to recover from SCF convergence failures. CPMonitor works by detecting SCF convergence failures in BOMD and switching to a CPMD Hamiltonian to propagate through the region of configuration space where the SCF calculation is unable to converge, then switching back to BOMD when good convergence behavior is re-established. We present a series of simulation studies that use CPMonitor, including detailed studies of the thermodynamic and dynamical properties of simple systems, as well as ab initio nanoreactor simulations containing transition metal atoms that were previously not possible to simulate using standard BOMD methods. Our studies show that CPMonitor can make BOMD simulations robust to SCF convergence difficulties and improve simulation performance and stability in reaction discovery applications.
| S-EPMC9749491 | biostudies-literature
Project description:BackgroundAnti-malarial efficacy needs to be monitored continually to ensure optimal dosing in the face of emerging anti-malarial drug resistance. The efficacy of artemisinin based combination therapies (ACT) is assessed by repeated measurements of parasite density in the blood of patients following treatment. Parasite density is measured from a capillary or venous blood sample, but this can be logistically and ethically challenging if multiple samples are required within a short time period. The aim of this work was to apply optimal design theory to derive clinically feasible blood sampling schedules from which parasite clearance could be defined using the Parasite Clearance Estimator (PCE), a recently developed tool to identify and quantify artemisinin resistance.MethodsRobust T-optimal design methodology was applied to offer a sampling schedule that allows for discrimination across models that best describe an individual patient's parasite-time profile. The design was based on typical parasite-time profiles derived from the literature combined with key sampling constraints of no more than six samples per patient within 48 hours of initial treatment. The design was evaluated with a simulation-estimation procedure that implemented the PCE.ResultsThe optimal sampling times (sampling windows) were: 0 (0 to 1.1), 5.8 (4.0 to 6.0), 9.9 (8.4 to 11.5), 24.8 (24.0 to 24.9), 36.3 (34.8 to 37.2) and 48 (47.3, 48.0) hours post initial treatment. The simulation-estimation procedure showed that the design supported identification of the appropriate method by the PCE to determine an individual's parasite clearance rate constant (the main output calculation from the PCE).ConclusionsThe proposed sampling design requires six samples per patient within the first 48 hours. The derived design requires validation in a real world setting, but should be considered for future studies that intend to employ the PCE.
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Project description:Recurrent events are common in medical research for subjects who are followed for the duration of a study. For example, cardiovascular patients with an implantable cardioverter defibrillator (ICD) experience recurrent arrhythmic events that are terminated by shocks or antitachycardia pacing delivered by the device. In a published randomized clinical trial, a recurrent-event model was used to study the effect of a drug therapy in subjects with ICDs, who were experiencing recurrent symptomatic arrhythmic events. Under this model, one expects the robust variance for the estimated treatment effect to diminish when the duration of the trial is extended, due to the additional events observed. However, as shown in this article, that is not always the case. We investigate this phenomenon using large datasets from this arrhythmia trial and from a diabetes study, with some analytical results, as well as through simulations. Some insights are also provided on existing sample size formulae using our results.
| S-EPMC3657073 | biostudies-literature
Project description:BackgroundIn observational studies, double robust or multiply robust (MR) approaches provide more protection from model misspecification than the inverse probability weighting and g-computation for estimating the average treatment effect (ATE). However, the approaches are based on parametric models, leading to biased estimates when all models are incorrectly specified. Nonparametric methods, such as machine learning or nonparametric double robust approaches, are robust to model misspecification, but the efficiency of nonparametric methods is low.MethodIn the study, we proposed an improved MR method combining parametric and nonparametric models based on the previous MR method (Han, JASA 109(507):1159-73, 2014) to improve the robustness to model misspecification and the efficiency. We performed comprehensive simulations to evaluate the performance of the proposed method.ResultsOur simulation study showed that the MR estimators with only outcome regression (OR) models, where one of the models was a nonparametric model, were the most recommended because of the robustness to model misspecification and the lowest root mean square error (RMSE) when including a correct parametric OR model. And the performance of the recommended estimators was comparative, even if all parametric models were misspecified. As an application, the proposed method was used to estimate the effect of social activity on depression levels in the China Health and Retirement Longitudinal Study dataset.ConclusionsThe proposed estimator with nonparametric and parametric models is more robust to model misspecification.
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