Computational methods for probabilistic decision trees.

D E Clark

doi:10.1006/cbmr.1997.1438

Home/Archive/PubMed

PubMed · 9134304

Computational methods for probabilistic decision trees.

D E Clark

View original source ↗Export metadata ↓

Abstract

Decision tree models may be more realistic if branching probabilities (and possibly utilities) are represented as distributions rather than point estimates. However, numerical analysis of such "probabilistic" trees is more difficult. This study employed the Mathematica computer algebra system to implement and verify previously described probabilistic methods. Both algebraic approximations and Monte Carlo simulation methods were used; in particular, simulations with beta, logistic-normal, and triangular distributions for branching probabilities were compared. Algebraic and simulation methods of sensitivity analysis were also implemented and compared. Computation required minimal programming and was reasonably fast using Mathematica on a standard personal computer. This study verified previously published results, including methods of sensitivity analysis. Changing the input distributional form had little effect. Computation is no longer a significant barrier to the use of probabilistic methods for analysis of decision trees.

Explore related subjects

Computer Simulation Decision Trees Evaluation Studies as Topic Mathematics Models, Statistical Monte Carlo Method Software Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

D E Clark. 1997. Computational methods for probabilistic decision trees.. https://doi.org/10.1006/cbmr.1997.1438

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Generating correlated data for omics simulation.

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

Computer Simulation↗

PubMed2017

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Computer Simulation↗

PubMed1998

Reversible peptide folding in solution by molecular dynamics simulation.

Long-standing questions on how peptides fold are addressed by the simulation at different temperatures of the reversible folding of a peptide in solution in atomic detail. Molecular dynamics simulations correctly predict the structure that is thermodynamically stable at 298 K, irrespective of the initial peptide conformation. The rate of folding and the free energy of folding at different temperatures are estimated. Although the conformational space potentially accessible to the peptide is extremely large, very few conformers (10(1) to 10(2)) are significantly populated at 20 K above the melting temperature. This implies that the search problem in peptide (or even protein) folding is surmountable using dynamics simulations.

Computer Simulation↗