Random Forests Are Able to Identify Differences in Clotting Dynamics from Kinetic Models of Thrombin Generation

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Abstract

Current methods for distinguishing acute coronary syndromes such as heart attack from stable coronary artery disease, based on the kinetics of thrombin formation, have been limited to evaluating sensitivity of well-established chemical species (e.g., thrombin) using simple quantifiers of their concentration profiles (e.g., maximum level of thrombin concentration, area under the thrombin concentration versus time curve). In order to get an improved classifier, we use a 34-protein factor clotting cascade model and convert the simulation data into a high-dimensional representation (about 19000 features) using a piecewise cubic polynomial fit. Then, we systematically find plausible assays to effectively gauge changes in acute coronary syndrome/coronary artery disease populations by introducing a statistical learning technique called Random Forests. We find that differences associated with acute coronary syndromes emerge in combinations of a handful of features. For instance, concentrations of 3 chemical species, namely, active alpha-thrombin, tissue factor-factor VIIa-factor Xa ternary complex, and intrinsic tenase complex with factor X, at specific time windows, could be used to classify acute coronary syndromes to an accuracy of about 87.2%. Such a combination could be used to efficiently assay the coagulation system.

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Random forest: a classification and regression tool for compound classification and QSAR modeling.

Vladimir Svetnik, Andy Liaw, Christopher Tong … (2003)

A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction. Prediction is made by aggregating (majority vote or averaging) the predictions of the ensemble. We built predictive models for six cheminformatics data sets. Our analysis demonstrates that Random Forest is a powerful tool capable of delivering performance that is among the most accurate methods to date. We also present three additional features of Random Forest: built-in performance assessment, a measure of relative importance of descriptors, and a measure of compound similarity that is weighted by the relative importance of descriptors. It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.

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Gene selection and classification of microarray data using random forest

Javier Díaz-Uriarte, Sara Alvarez de Andrés (2006)

Background Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of genes that can still achieve good predictive performance (for instance, for future use with diagnostic purposes in clinical practice). Many gene selection approaches use univariate (gene-by-gene) rankings of gene relevance and arbitrary thresholds to select the number of genes, can only be applied to two-class problems, and use gene selection ranking criteria unrelated to the classification algorithm. In contrast, random forest is a classification algorithm well suited for microarray data: it shows excellent performance even when most predictive variables are noise, can be used when the number of variables is much larger than the number of observations and in problems involving more than two classes, and returns measures of variable importance. Thus, it is important to understand the performance of random forest with microarray data and its possible use for gene selection. Results We investigate the use of random forest for classification of microarray data (including multi-class problems) and propose a new method of gene selection in classification problems based on random forest. Using simulated and nine microarray data sets we show that random forest has comparable performance to other classification methods, including DLDA, KNN, and SVM, and that the new gene selection procedure yields very small sets of genes (often smaller than alternative methods) while preserving predictive accuracy. Conclusion Because of its performance and features, random forest and gene selection using random forest should probably become part of the "standard tool-box" of methods for class prediction and gene selection with microarray data.

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Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG).

Angela John, Leslie S. Prichep, Yadolah Dodge … (2007)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

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Author and article information

Contributors

Yoshihiro Fukumoto: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date Collection: 2016

Publication date (Electronic): 12 May 2016

Volume: 11

Issue: 5

Electronic Location Identifier: e0153776

Affiliations

[1 ]Department of Mechanical Engineering, Texas A&M University, College Station, Texas, United States of America

[2 ]Department of Industrial and Systems Engineering, Texas A&M University, College Station, Texas, United States of America

[3 ]Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, United States of America

Kurume University School of Medicine, JAPAN

Author notes

Competing Interests: The authors have declared that no competing interests exist.

Analyzed the data: JA SB KN ARS. Wrote the paper: JA SB ARS.

* E-mail: arun.r.srinivasa@ 123456gmail.com

Article

Publisher ID: PONE-D-15-35489

DOI: 10.1371/journal.pone.0153776

PMC ID: 4865224

PubMed ID: 27171403

SO-VID: 1ba4aff0-d044-4266-a3ab-124b49dd7943

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 12 August 2015

Date accepted : 4 April 2016

Page count

Figures: 4, Tables: 3, Pages: 12

Funding

Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;

Award ID: 1028894

Award Recipient : arun srinivasa

This work was supported by National Science Foundation, Grant # 1028894 ( www.nsf.gov).

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Data Availability All relevant data are within the paper and its Supporting Information files.

Random Forests Are Able to Identify Differences in Clotting Dynamics from Kinetic Models of Thrombin Generation

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Most cited references 15

Random forest: a classification and regression tool for compound classification and QSAR modeling.

Gene selection and classification of microarray data using random forest

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG).

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