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Start Over Author "Miroslava Cuperlovic-Culf" Journal bmc bioinformatics
2 results on '"Miroslava Cuperlovic-Culf"'

1. SMILE: systems metabolomics using interpretable learning and evolution

Author

Chengyuan Sha, Miroslava Cuperlovic-Culf, and Ting Hu

Subjects
Metabolomics, Alzheimer’s disease, Interpretable machine learning, Feature interaction, Evolutionary algorithm, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Direct link between metabolism and cell and organism phenotype in health and disease makes metabolomics, a high throughput study of small molecular metabolites, an essential methodology for understanding and diagnosing disease development and progression. Machine learning methods have seen increasing adoptions in metabolomics thanks to their powerful prediction abilities. However, the “black-box” nature of many machine learning models remains a major challenge for wide acceptance and utility as it makes the interpretation of decision process difficult. This challenge is particularly predominant in biomedical research where understanding of the underlying decision making mechanism is essential for insuring safety and gaining new knowledge. Results In this article, we proposed a novel computational framework, Systems Metabolomics using Interpretable Learning and Evolution (SMILE), for supervised metabolomics data analysis. Our methodology uses an evolutionary algorithm to learn interpretable predictive models and to identify the most influential metabolites and their interactions in association with disease. Moreover, we have developed a web application with a graphical user interface that can be used for easy analysis, interpretation and visualization of the results. Performance of the method and utilization of the web interface is shown using metabolomics data for Alzheimer’s disease. Conclusions SMILE was able to identify several influential metabolites on AD and to provide interpretable predictive models that can be further used for a better understanding of the metabolic background of AD. SMILE addresses the emerging issue of interpretability and explainability in machine learning, and contributes to more transparent and powerful applications of machine learning in bioinformatics.

Published
2021
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2. MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures

Author

Adrian S. Culf, Dan Tulpan, Miroslava Cuperlovic-Culf, Serge Léger, and Luc Belliveau

Subjects
Magnetic Resonance Spectroscopy, Computer science, Metabolite, lcsh:Computer applications to medicine. Medical informatics, computer.software_genre, Biochemistry, chemistry.chemical_compound, Metabolomics, Structural Biology, medicine, lcsh:QH301-705.5, Molecular Biology, Internet, medicine.diagnostic_test, Applied Mathematics, Magnetic resonance imaging, Nuclear magnetic resonance spectroscopy, Magnetic Resonance Imaging, Computer Science Applications, Task (computing), Identification (information), lcsh:Biology (General), chemistry, Proton NMR, lcsh:R858-859.7, Data mining, DNA microarray, computer, Software, Research Article
Abstract

Background One-dimensional 1H-NMR spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, the accurate identification of individual compounds is still a challenging task, particularly in spectral regions with higher peak densities. The need for automatic tools to facilitate and further improve the accuracy of such tasks, while using increasingly larger reference spectral libraries becomes a priority of current metabolomics research. Results We introduce a web server application, called MetaboHunter, which can be used for automatic assignment of 1H-NMR spectra of metabolites. MetaboHunter provides methods for automatic metabolite identification based on spectra or peak lists with three different search methods and with possibility for peak drift in a user defined spectral range. The assignment is performed using as reference libraries manually curated data from two major publicly available databases of NMR metabolite standard measurements (HMDB and MMCD). Tests using a variety of synthetic and experimental spectra of single and multi metabolite mixtures show that MetaboHunter is able to identify, in average, more than 80% of detectable metabolites from spectra of synthetic mixtures and more than 50% from spectra corresponding to experimental mixtures. This work also suggests that better scoring functions improve by more than 30% the performance of MetaboHunter's metabolite identification methods. Conclusions MetaboHunter is a freely accessible, easy to use and user friendly 1H-NMR-based web server application that provides efficient data input and pre-processing, flexible parameter settings, fast and automatic metabolite fingerprinting and results visualization via intuitive plotting and compound peak hit maps. Compared to other published and freely accessible metabolomics tools, MetaboHunter implements three efficient methods to search for metabolites in manually curated data from two reference libraries. Availability http://www.nrcbioinformatics.ca/metabohunter/

Published
2011

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