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EnsInfer: a simple ensemble approach to network inference outperforms any single method.

ABSTRACT: This study evaluates both a variety of existing base causal inference methods and a variety of ensemble methods. We show that: (i) base network inference methods vary in their performance across different datasets, so a method that works poorly on one dataset may work well on another; (ii) a non-homogeneous ensemble method in the form of a Naive Bayes classifier leads overall to as good or better results than using the best single base method or any other ensemble method; (iii) for the best results, the ensemble method should integrate all methods that satisfy a statistical test of normality on training data. The resulting ensemble model EnsInfer easily integrates all kinds of RNA-seq data as well as new and existing inference methods. The paper categorizes and reviews state-of-the-art underlying methods, describes the EnsInfer ensemble approach in detail, and presents experimental results. The source code and data used will be made available to the community upon publication.

SUBMITTER: Shen B

PROVIDER: S-EPMC10037858 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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EnsInfer: a simple ensemble approach to network inference outperforms any single method.

Shen Bingran B Coruzzi Gloria G Shasha Dennis D

BMC bioinformatics 20230324 1

This study evaluates both a variety of existing base causal inference methods and a variety of ensemble methods. We show that: (i) base network inference methods vary in their performance across different datasets, so a method that works poorly on one dataset may work well on another; (ii) a non-homogeneous ensemble method in the form of a Naive Bayes classifier leads overall to as good or better results than using the best single base method or any other ensemble method; (iii) for the best resu ...[more]

PMID: 36964499

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