Predicting correlated outcomes from molecular data

machine learning; prediction; classification; correlated outcomes; model interpretability; multivariate; stacking; ensemble learning; software; Parkinson's disease; lasso; ridge regression

Abstract :

[en] Motivation: Multivariate (multi-target) regression has the potential to outperform univariate (single-target) regression at predicting correlated outcomes, which frequently occur in biomedical and clinical research. Here we implement multivariate lasso and ridge regression using stacked generalisation. Results: Our flexible approach leads to predictive and interpretable models in high-dimensional settings, with a single estimate for each input-output effect. In the simulation, we compare the predictive performance of several state-of-the-art methods for multivariate regression. In the application, we use clinical and genomic data to predict multiple motor and non-motor symptoms in Parkinson’s disease patients. We conclude that stacked multivariate regression, with our adaptations, is a competitive method for predicting correlated outcomes. Availability and Implementation: The R package joinet is available on GitHub (https://github.com/rauschenberger/joinet) and CRAN (https://CRAN.R-project.org/package=joinet).

Research center :

- Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)

Disciplines :

Biotechnology
Human health sciences: Multidisciplinary, general & others
Computer science
Life sciences: Multidisciplinary, general & others

Author, co-author :

Rauschenberger, Armin ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science

Glaab, Enrico ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science

External co-authors :

Language :

English

Title :

Predicting correlated outcomes from molecular data

Publication date :

2021

Journal title :

Bioinformatics

Volume :

Issue :

Pages :

3889–3895

Peer reviewed :

Peer reviewed

Focus Area :

Systems Biomedicine

Additional URL :

https://doi.org/10.1093/bioinformatics/btab576

European Projects :

H2020 - 874825 - PERMIT - PERsonalised MedicIne Trials

FnR Project :

FNR11651464 - Multi-dimensional Stratification Of Parkinson'S Disease Patients For Personalised Interventions, 2017 (01/07/2018-30/06/2021) - Enrico Glaab

Name of the research project :

FNR11651464 > PD-Strat (Dr. Glaab) > 01/07/2018 > 30/06/2021 > GLAAB Enrico

Funders :

FNR - Fonds National de la Recherche [LU]
CE - Commission Européenne [BE]

Available on ORBilu :

since 05 August 2021

Statistics

Number of views

116 (6 by Unilu)

Number of downloads

67 (8 by Unilu)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

WoS citations^™

Bibliography

Biesheuvel, C.J. et al. (2008) Polytomous logistic regression analysis could be applied more often in diagnostic research. J. Clin. Epidemiol., 61, 125-134.
Bostanabad, R. et al. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. Int. J. Numer. Methods Eng., 114, 501-516.
Breiman, L. (1996) Stacked regressions. Mach. Learn., 24, 49-64.
Breiman, L. and Friedman, J.H. (1997) Predicting multivariate responses in multiple linear regression. J. R. Stat. Soc. Ser. B (Stat. Methodol.), 59, 3-54.
Cao, H. et al. (2019) RMTL: an R library for multi-task learning. Bioinformatics, 35, 1797-1798.
Christie, S.A. et al. (2019) Dynamic multi-outcome prediction after injury: applying adaptive machine learning for precision medicine in trauma. PLoS One, 14, e0213836.
Chung, D. and Keles, S. (2010) Sparse partial least squares classification for high dimensional data. Stat. Appl. Genet. Mol. Biol., 9, Article 17.
de Jong, V.M. et al. (2019) Sample size considerations and predictive performance of multinomial logistic prediction models. Stat. Med., 38, 1601-1619.
Dudbridge, F. (2020) Criteria for evaluating risk prediction of multiple outcomes. Stat. Methods Med. Res., 29, 3492-3510.
Friedman, J.H. (1991) Multivariate adaptive regression splines. Ann. Stat., 19, 1-67.
Friedman, J.H. et al. (2010) Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw., 33, 1-22.
Luo, R. and Qi, X. (2017) Signal extraction approach for sparse multivariate response regression. J. Multivar. Stat., 153, 83-97.
Lutz, R.W. and Bühlmann, P. (2006) Boosting for high-multivariate responses in high-dimensional linear regression. Stat. Sin., 16, 471.
Marek, K. et al.; Parkinson Progression Marker Initiative. (2011) The Parkinson Progression Marker Initiative (PPMI). Progress Neurobiol., 95, 629-635.
Martin, G.P. et al. (2021) Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches. Stat. Med., 40, 498-517.
Morris, T.P. et al. (2019) Using simulation studies to evaluate statistical methods. Stat. Med., 38, 2074-2102.
Peng, J. et al. (2010) Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer. Ann. Appl. Stat., 4, 53-77.
Price, B.S. and Sherwood, B. (2017) A cluster elastic net for multivariate regression. J. Mach. Learn. Res., 18, 1-39.
Rauschenberger, A. et al. (2021) Predictive and interpretable models via the stacked elastic net. Bioinformatics, 37, 2012-2016.
Rosellini, A.J. et al. (2017) Using self-report surveys at the beginning of service to develop multi-outcome risk models for new soldiers in the US Army. Psychol. Med., 47, 2275-2287.
Rothman, A.J. et al. (2010) Sparse multivariate regression with covariance estimation. J. Comput. Graph. Stat., 19, 947-962.
Segal, M. and Xiao, Y. (2011) Multivariate random forests. Wiley Interdiscip. Rev. Data Min. Knowledge Discov., 1, 80-87.
Teixeira-Pinto, A. et al. (2009) Statistical approaches to modeling multiple outcomes in psychiatric studies. Psychiatric Ann., 39, 729-735.
Tibshirani, R. and Hinton, G. (1998) Coaching variables for regression and classification. Stat. Comput., 8, 25-33.
van Buuren, S. and Groothuis-Oudshoorn, K. (2011) mice: multivariate imputation by chained equations in R. J. Stat. Softw., 45, 1-67.
Vega, C. (2021) From Hume to Wuhan: an epistemological journey on the problem of induction in COVID-19 machine learning models and its impact upon medical research. IEEE Access, 9, 97243-97250.
Waegeman, W. et al. (2019) Multi-target prediction: a unifying view on problems and methods. Data Min. Knowledge Discov., 33, 293-324.
Wang, L.Y. et al. (2019) Multi-outcome predictive modelling of anesthesia patients. J. Biomed. Res., 33, 430.
Wilkinson, D.P. et al. (2021) Defining and evaluating predictions of joint species distribution models. Methods Ecol. Evol., 12, 394-404.
Wolpert, D.H. (1992) Stacked generalization. Neural Netw., 5, 241-259.
Xing, L. et al. (2020) Simultaneous prediction of multiple outcomes using revised stacking algorithms. Bioinformatics, 36, 65-72.
Zou, H. and Hastie, T. (2005) Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B (Stat. Methodol.), 67, 301-320.