27 papers · ranked by Valyu relevance
Johannes Stöckelmaier, Chris Oostenbrink
To elucidate the connection between the structure and function of intrinsically disordered proteins (IDPs) a description of their conformational ensembles is crucial. These are typically characterized by an extremely large number of similarly low energy conformations, which can hardly be captured by either experimental…
Gerhard Hummer, Jürgen Köfinger
We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including…
Tom Lindström, Michael Tildesley, Colleen Webb, Sergei L. Kosakovsky Pond
'Sergei L. Kosakovsky Pond'] Mathematical models are powerful tools for epidemiology and can be used to compare control actions. However, different models and model parameterizations may provide different prediction of outcomes. In other fields of research, ensemble modeling has been used to combine multiple…
Samuel Bowerman, Joseph E. Curtis, Joseph Clayton, Emre H. Brookes + 1 more
Many biomolecular complexes exist in a flexible ensemble of states in solution which are necessary to perform their biological function. Small angle scattering (SAS) measurements are a popular method for characterizing these flexible molecules due to their relative ease of use and ability to simultaneously probe the…
Nick Hauptvogel, Christian Igel
Bayesian neural networks address epistemic uncertainty by learning a posterior distribution over model parameters. Sampling and weighting networks according to this posterior yields an ensemble model referred to as Bayes ensemble. Ensembles of neural networks (deep ensembles) can profit from the cancellation of errors…
Pin Lyu, Lawrence Fulton
Invasive species management demands predictive models that balance accuracy with ecological interpretability. Traditional approaches often fail to capture complex environmental interactions. We evaluated hybrid frameworks integrating biological and machine learning models for rainbow trout (Oncorhynchus mykiss) growth…
Edward H. Yu, Parth J. Parekh
Methods for unsupervised anomaly detection suffer from the fact that the data is unlabeled, making it difficult to assess the optimality of detection algorithms. Ensemble learning has shown exceptional results in classification and clustering problems, but has not seen as much research in the context of outlier…
Margrethe Kvale Loe, Håkon Tjelmeland
We propose a generalised framework for the updating of a prior ensemble to a posterior ensemble, an essential yet challenging part in ensemble-based filtering methods. The proposed framework is based on a generalised and fully Bayesian view on the traditional ensemble Kalman filter (EnKF). In the EnKF, the updating of…
Benjamin Eltzner, Julian Hofstadler, Daniel Rudolf, Michael Habeck + 1 more
The principle of maximum entropy provides a canonical way to include measurement results into a thermodynamic ensemble. Observable features of a thermodynamic system, which are measured as averages over an ensemble are included into the partition function by using Lagrange multipliers. Applying this principle to the…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Stephen Coleman, Paul D.W. Kirk, Chris Wallace
Cluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. However, problems such as choosing the number of clusters and issues with high dimensional data arise consistently. An ensemble approach, such as consensus clustering, can overcome some of…
Michael P. H. Stumpf
Recent progress in theoretical systems biology, applied mathematics and computational statistics allows us to compare the performance of different candidate models at describing a particular biological system quantitatively. Model selection has been applied with great success to problems where a small number-typically…
Long Ni, Alan A. Stocker
Ensemble coding creates compressed representations of a stimulus array. When discriminating the ensemble average against a reference, however, items in the ensemble with feature values closer to the reference are typically weighed stronger. We have recently shown that this “robust averaging” behavior can be explained…
Shunichiro Tomura, Owen Powell, Melanie J. Wilkinson, James Lefvre + 1 more
Ensembles of multiple genomic prediction models have demonstrated improved prediction performance over the individual models contributing to the ensemble. The outperformance of ensemble models is expected from the Diversity Prediction Theorem, which states that for ensembles constructed with diverse prediction models…
Mario Frei, Hans R. Künsch
In many applications of Monte Carlo nonlinear filtering, the propagation step is computationally expensive, and hence, the sample size is limited. With small sample sizes, the update step becomes crucial. Particle filtering suffers from the well-known problem of sample degeneracy. Ensemble Kalman filtering avoids this…
Amira Abbas, Maria Schuld, Francesco Petruccione
Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of…
Kuo-Wei Hsu
Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The…
Pierre‐Alexandre Mattei, Damien Garreau
Ensemble methods combine the predictions of several base models. We study whether or not including more models always improves their average performance. This question depends on the kind of ensemble considered, as well as the predictive metric chosen. We focus on situations where all members of the ensemble are a…
Susmita Datta, Vasyl Pihur, Somnath Datta
Background Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal in any given classification application. In addition, for most classification problems, selecting the best performing classification algorithm…
Xiaodong Zeng, Derek F. Wong, Lidia S. Chao
A weighted accuracy and diversity (WAD) method is presented, a novel measure used to evaluate the quality of the classifier ensemble, assisting in the ensemble selection task. The proposed measure is motivated by a commonly accepted hypothesis; that is, a robust classifier ensemble should not only be accurate but also…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Arjun Pakrashi, Brian Mac Namee
This paper introduces a new perspective on multi-class ensemble classification that considers training an ensemble as a state estimation problem. The new perspective considers the final ensemble classifier model as a static state, which can be estimated using a Kalman filter that combines noisy estimates made by…
Shehzad Khalid, Sannia Arshad, Sohail Jabbar, Seungmin Rho
We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to…
Mohammad H. Alobaidi, Mohamed A. Meguid, Fateh Chebana
The regional nature of liquefaction records and limited information available for a certain set of explanatories motivate the development of complex prediction techniques. Indirect methods are commonly applied to incidentally derive a hyperplane to this binary classification problem. Machine learning approaches offer…