Dataset Information

MAINE: a web tool for multi-omics feature selection and rule-based data exploration.

ABSTRACT:

Summary

Patient multi-omics datasets are often characterized by a high dimensionality; however, usually only a small fraction of the features is informative, that is change in their value is directly related to the disease outcome or patient survival. In medical sciences, in addition to a robust feature selection procedure, the ability to discover human-readable patterns in the analyzed data is also desirable. To address this need, we created MAINE-Multi-omics Analysis and Exploration. The unique functionality of MAINE is the ability to discover multidimensional dependencies between the selected multi-omics features and event outcome prediction as well as patient survival probability. Learned patterns are visualized in the form of interpretable decision/survival trees and rules.

Availability and implementation

MAINE is freely available at maine.ibemag.pl as an online web application.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Gruca A

PROVIDER: S-EPMC8896606 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Publications

MAINE: a web tool for multi-omics feature selection and rule-based data exploration.

Gruca Aleksandra A Henzel Joanna J Kostorz Iwona I Stęclik Tomasz T Wróbel Łukasz Ł Sikora Marek M

Bioinformatics (Oxford, England) 20220301 6

<h4>Summary</h4>Patient multi-omics datasets are often characterized by a high dimensionality; however, usually only a small fraction of the features is informative, that is change in their value is directly related to the disease outcome or patient survival. In medical sciences, in addition to a robust feature selection procedure, the ability to discover human-readable patterns in the analyzed data is also desirable. To address this need, we created MAINE-Multi-omics Analysis and Exploration. T ...[more]

PMID: 34954788

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Project description:BackgroundIn the last few years, multi-omics data, that is, datasets containing different types of high-dimensional molecular variables for the same samples, have become increasingly available. To date, several comparison studies focused on feature selection methods for omics data, but to our knowledge, none compared these methods for the special case of multi-omics data. Given that these data have specific structures that differentiate them from single-omics data, it is unclear whether different feature selection strategies may be optimal for such data. In this paper, using 15 cancer multi-omics datasets we compared four filter methods, two embedded methods, and two wrapper methods with respect to their performance in the prediction of a binary outcome in several situations that may affect the prediction results. As classifiers, we used support vector machines and random forests. The methods were compared using repeated fivefold cross-validation. The accuracy, the AUC, and the Brier score served as performance metrics.ResultsThe results suggested that, first, the chosen number of selected features affects the predictive performance for many feature selection methods but not all. Second, whether the features were selected by data type or from all data types concurrently did not considerably affect the predictive performance, but for some methods, concurrent selection took more time. Third, regardless of which performance measure was considered, the feature selection methods mRMR, the permutation importance of random forests, and the Lasso tended to outperform the other considered methods. Here, mRMR and the permutation importance of random forests already delivered strong predictive performance when considering only a few selected features. Finally, the wrapper methods were computationally much more expensive than the filter and embedded methods.ConclusionsWe recommend the permutation importance of random forests and the filter method mRMR for feature selection using multi-omics data, where, however, mRMR is considerably more computationally costly.

Dataset Information

MAINE: a web tool for multi-omics feature selection and rule-based data exploration.

Summary

Availability and implementation

Supplementary information

Publications

MAINE: a web tool for multi-omics feature selection and rule-based data exploration.

Similar Datasets

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