15 papers · ranked by Valyu relevance
Damjan Krstajic, Ljubomir J Buturovic, David E Leahy, Simon Thomas
Background We address the problem of selecting and assessing classification and regression models using cross-validation. Current state-of-the-art methods can yield models with high variance, rendering them unsuitable for a number of practical applications including QSAR. In this paper we describe and evaluate best…
Baidu Li, Xinhai Li
Linear models, including t-test, ANOVA, regression, ANCOVA, and generalized linear models, are foundational tools in statistical analysis. For large datasets, such as those involving tens of thousands of genes and millions of records, numerous advanced methods have been developed to improve both computational…
Riccardo Rossi, Andrea Murari, Pasquale Gaudio, Michela Gelfusa
The Bayesian information criterion (BIC), the Akaike information criterion (AIC), and some other indicators derived from them are widely used for model selection. In their original form, they contain the likelihood of the data given the models. Unfortunately, in many applications, it is practically impossible to…
Sanjay Basu, Jason Andrews
Sanjay Basu and colleagues explain how models are increasingly used to inform public health policy yet readers may struggle to evaluate the quality of models. All models require simplifying assumptions, and there are tradeoffs between creating models that are more “realistic” versus those that are grounded in more…
Xinnong Li, Mark Sale, Keith Nieforth, James Craig + 6 more
Forward addition/backward elimination (FABE) has been the standard for population pharmacokinetic model selection (PPK) since NONMEM® was introduced. We investigated five machine learning (ML) algorithms (Genetic algorithm [GA], Gaussian process [GP], random forest [RF], gradient boosted random tree [GBRT], and…
Nicolas Sundqvist, Nina Grankvist, Jeramie Watrous, Jain Mohit + 3 more
'Roland Nilsson' 'Gunnar Cedersund' 'Vassily Hatzimanikatis'] Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data…
Carlos A. Duchanoy, Hiram Calvo, Marco A. Moreno-Armendáriz
Surrogate Modeling (SM) is often used to reduce the computational burden of time-consuming system simulations. However, continuous advances in Artificial Intelligence (AI) and the spread of embedded sensors have led to the creation of Digital Twins (DT), Design Mining (DM), and Soft Sensors (SS). These methodologies…
Daniel Silk, Paul D. W. Kirk, Chris P. Barnes, Tina Toni + 2 more
'Michael P. H. Stumpf' 'Burkhard Rost'] Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models…
Zihao Wen, David L. Dowe, Abhijit Mandal, Suneel Babu Chatla
Species distribution modeling is fundamental to biodiversity, evolution, conservation science, and the study of invasive species. Given environmental data and species distribution data, model selection techniques are frequently used to help identify relevant features. Existing studies aim to find the relevant features…
Joseph Beyene, Eshetu G Atenafu, Jemila S Hamid, Teresa To + 1 more
'Lillian Sung'] Background Multiple regression models are used in a wide range of scientific disciplines and automated model selection procedures are frequently used to identify independent predictors. However, determination of relative importance of potential predictors and validating the fitted models for their…
Hyemin Han
In the present study, I developed and tested an R module to explore the best models within the context of multilevel modeling in research in public health. The module that I developed, explore.models, compares all possible candidate models generated from a set of candidate predictors with information criteria, Akaike…
Keita Yoshii, Hiroshi Nishiura, Kaoru Inoue, Takayuki Yamaguchi + 1 more
'Akihiko Hirose'] Background To employ the benchmark dose (BMD) method in toxicological risk assessment, it is critical to understand how the BMD lower bound for reference dose calculation is selected following statistical fitting procedures of multiple mathematical models. The purpose of this study was to compare the…
Xavier Rubio-Campillo, John P. Hart
Standard Bayesian inference updates a set of prior beliefs considering new evidence and a given likelihood function. Prior beliefs aggregate the existing knowledge of a given topic, and the degree of credibility of this knowledge. These beliefs are translated into parameters of the model. The possible values for each…
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…
Annette Spooner, Gelareh Mohammadi, Perminder S. Sachdev, Henry Brodaty + 1 more
'Henry Brodaty' 'Arcot Sowmya' ''] Background Feature selection is often used to identify the important features in a dataset but can produce unstable results when applied to high-dimensional data. The stability of feature selection can be improved with the use of feature selection ensembles, which aggregate the…