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15. Constructing bi-plots for random forest: Tutorial

Author

Blanchet, Lionel and Blanchet, Lionel

Abstract

Current technological developments have allowed for a significant increase and availability of data. Consequently, this has opened enormous opportunities for the machine learning and data science field, translating into the development of new algorithms in a wide range of applications in medical, biomedical, daily-life, and national security areas. Ensemble techniques are among the pillars of the machine learning field, and they can be defined as approaches in which multiple, complex, independent/uncorrelated, predictive models are subsequently combined by either averaging or voting to yield a higher model performance. Random forest (RF), a popular ensemble method, has been successfully applied in various domains due to its ability to build predictive models with high certainty and little necessity of model optimization. RF provides both a predictive model and an estimation of the variable importance. However, the estimation of the variable importance is based on thousands of trees, and therefore, it does not specify which variable is important for which sample group.The present study demonstrates an approach based on the pseudo-sample principle that allows for construction of bi-plots (i.e. spin plots) associated with RF models. The pseudo-sample principle for RF. is explained and demonstrated by using two simulated datasets, and three different types of real data, which include political sciences, food chemistry and the human microbiome data. The pseudo-sample bi plots, associated with RF and its unsupervised version, allow for a versatile visualization of multivariate models, and the variable importance and the relation among them. (c) 2020 Elsevier B.V. All rights reserved.

Published
2020

18. Constructing bi-plots for random forest: Tutorial

Author

Blanchet, Lionel, primary, Vitale, Raffaele, additional, van Vorstenbosch, Robert, additional, Stavropoulos, George, additional, Pender, John, additional, Jonkers, Daisy, additional, Schooten, Frederik-Jan van, additional, and Smolinska, Agnieszka, additional

Published
2020
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19. Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis

Author

Wijmenga Sybren S, Tuinstra Tinka, Suidgeest Ernst, van Aken Hans, Ampt Kirsten AM, Stoop Marcel P, Attali Amos, Smolinska Agnieszka, Blanchet Lionel, Luider Theo, and Buydens Lutgarde MC

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Analysis of Cerebrospinal Fluid (CSF) samples holds great promise to diagnose neurological pathologies and gain insight into the molecular background of these pathologies. Proteomics and metabolomics methods provide invaluable information on the biomolecular content of CSF and thereby on the possible status of the central nervous system, including neurological pathologies. The combined information provides a more complete description of CSF content. Extracting the full combined information requires a combined analysis of different datasets i.e. fusion of the data. Results A novel fusion method is presented and applied to proteomics and metabolomics data from a pre-clinical model of multiple sclerosis: an Experimental Autoimmune Encephalomyelitis (EAE) model in rats. The method follows a mid-level fusion architecture. The relevant information is extracted per platform using extended canonical variates analysis. The results are subsequently merged in order to be analyzed jointly. We find that the combined proteome and metabolome data allow for the efficient and reliable discrimination between healthy, peripherally inflamed rats, and rats at the onset of the EAE. The predicted accuracy reaches 89% on a test set. The important variables (metabolites and proteins) in this model are known to be linked to EAE and/or multiple sclerosis. Conclusions Fusion of proteomics and metabolomics data is possible. The main issues of high-dimensionality and missing values are overcome. The outcome leads to higher accuracy in prediction and more exhaustive description of the disease profile. The biological interpretation of the involved variables validates our fusion approach.

Published
2011
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20. Identification of Mycobacterium tuberculosis using volatile biomarkers in culture and exhaled breath

Author

Mellors, Theodore R, primary, Nasir, Mavra, additional, Franchina, Flavio A, additional, Smolinska, Agnieszka, additional, Blanchet, Lionel, additional, Flynn, JoAnne L, additional, Tomko, Jaime, additional, O’Malley, Melanie, additional, Scanga, Charles A, additional, Lin, Philana Ling, additional, Wagner, Jeff, additional, and Hill, Jane E, additional

Published
2018
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22. The benzophenone [S.sub.1](n,[pi]*) -> [T.sub.1](n,[pi]*) states intersystem crossing reinvestigated by ultrafast absorption spectroscopy and multivariate curve resolution

Author

Aloise, Stephane, Ruckebusch, Cyril, Blanchet, Lionel, Rehault, Julien, Buntinx, Guy, and Huvenne, Jean-Pierre

Subjects
Benzophenone -- Analysis, Absorption spectra -- Observations, Relaxation phenomena -- Observations, Chemicals, plastics and rubber industries
Abstract

The benzophenone intersystem crossing from [S.sub.1](n,[pi]*) to [T.sub.1](n,[pi]*) states is reexamined by subpicosecond time-resolved absorption spectroscopy and effective data analysis for different excitation wavelengths and solvents. Multivariate curve resolution alternating least-squares analysis has suggested the implication of an intermediate (IS) in the relaxation process of the [S.sub.1] state.

Published
2008

30. Quantifying small molecule phenotypic effects using mitochondrial morpho-functional fingerprinting and machine learning

Author

Blanchet, Lionel, primary, Smeitink, Jan A. M., additional, van Emst - de Vries, Sjenet E., additional, Vogels, Caroline, additional, Pellegrini, Mina, additional, Jonckheere, An I., additional, Rodenburg, Richard J. T., additional, Buydens, Lutgarde M. C., additional, Beyrath, Julien, additional, Willems, Peter H. G. M., additional, and Koopman, Werner J. H., additional

Published
2015
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34. A new method to evaluate macaque health using exhaled breath: A case study of M. tuberculosis in a BSL-3 setting.

Author

Mellors, Theodore R., Blanchet, Lionel, Flynn, JoAnne L., Tomko, Jaime, O’Malley, Melanie, Scanga, Charles A., Lin, Philana L., and Hill, Jane E.

Abstract

Breath is hypothesized to contain clinically relevant information, useful for the diagnosis and monitoring of disease, as well as understanding underlying pathogenesis. Nonhuman primates, such as the cynomolgus macaque, serve as an important model for the study of human disease, including over 70 different human infections. In this feasibility study, exhaled breath was successfully collected in less than 5 min under Bio-safety Level 3 conditions from five anesthetized, intubated cynomolgus and rhesus macaques, before and after lung infection with M. tuberculosis. The breath was subsequently analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry. A total of 384 macaque breath features were detected, with hydrocarbons being the most abundant. We provide putative identification for 19 breath molecules and report on overlap between the identified macaque breath compounds and those identified in previous human studies. NEW & NOTEWORTHY To the best of our knowledge, this is the first time the volatile molecule content of macaque breath has been comprehensively sampled and analyzed. We do so here in a Bio-safety Level 3 setting in the context of M. tuberculosis lung infection. The breath of nonhuman primates represents a novel fluid that could provide insight into disease pathogenesis. [ABSTRACT FROM AUTHOR]

Published
2017
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37. Breaking with trends in pre-processing?

Author

Engel, Jasper, primary, Gerretzen, Jan, additional, Szymańska, Ewa, additional, Jansen, Jeroen J., additional, Downey, Gerard, additional, Blanchet, Lionel, additional, and Buydens, Lutgarde M.C., additional

Published
2013
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41. Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis

Author

Blanchet, Lionel, primary, Smolinska, Agnieszka, additional, Attali, Amos, additional, Stoop, Marcel P, additional, Ampt, Kirsten AM, additional, van Aken, Hans, additional, Suidgeest, Ernst, additional, Tuinstra, Tinka, additional, Wijmenga, Sybren S, additional, Luider, Theo, additional, and Buydens, Lutgarde MC, additional

Published
2011
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45. Data Fusion in Metabolomics and Proteomics for Biomarker Discovery.

Author

Blanchet L and Smolinska A

Subjects
Biomarkers, Metabolomics methods, Models, Statistical, Proteomics methods
Abstract

Proteomics and metabolomics provide key insights into status and dynamics of biological systems. These molecular studies reveal the complex mechanisms involved in disease or aging processes. Invaluable information can be obtained using various analytical techniques such as nuclear magnetic resonance, liquid chromatography, or gas chromatography coupled to mass spectrometry. Each method has inherent advantages and drawbacks, but they are complementary in terms of biological information.The fusion of different measurements is a complex topic. We describe here a framework allowing combining multiple data sets, provided by different analytical platforms. For each platform, the relevant information is extracted in the first step. The obtained latent variables are then fused and further analyzed. The influence of the original variables is then calculated back and interpreted.

Published
2016
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