EFS: an ensemble feature selection tool implemented as R-package and web-application

Neumann, Ursula; Genze, Nikita; Heider, Dominik

doi:10.1186/s13040-017-0142-8

Cited by 78 publications

(61 citation statements)

References 17 publications

(20 reference statements)

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“…In order to find features in the output of SEDE-GPS that are predictive for lake microbial biodiversity, we used the R package EFS (Ensemble Feature Selection) and the eight alpha diversity metrics as target variable in separate analyses [13, 14]. EFS is an ensemble feature selection method that assigns weights to the features in an unbiased manner according to their predictiveness for the target value.…”

Section: Resultsmentioning

confidence: 99%

“…Next, we used the R package EFS (Ensemble Feature Selection) in order to rank the remaining features according to their importance. EFS is an ensemble learning feature selection method, that corrects for biases of the single methods when weighting features [13, 14]. Although EFS has been developed for feature selection in classification studies, we used an adapted version of EFS, which can be used for regression studies.…”

Section: Methodsmentioning

confidence: 99%

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SEDE-GPS: socio-economic data enrichment based on GPS information

Sperlea¹,

Füser²,

Boenigk³

et al. 2018

BMC Bioinformatics

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BackgroundMicrobes are essentail components of all ecosystems because they drive many biochemical processes and act as primary producers. In freshwater ecosystems, the biodiversity in and the composition of microbial communities can be used as indicators for environmental quality. Recently, some environmental features have been identified that influence microbial ecosystems. However, the impact of human action on lake microbiomes is not well understood. This is, in part, due to the fact that environmental data is, albeit theoretically accessible, not easily available.ResultsIn this work, we present SEDE-GPS, a tool that gathers data that are relevant to the environment of an user-provided GPS coordinate. To this end, it accesses a list of public and corporate databases and aggregates the information in a single file, which can be used for further analysis. To showcase the use of SEDE-GPS, we enriched a lake microbial ecology sequencing dataset with around 18,000 socio-economic, climate, and geographic features. The sources of SEDE-GPS are public databases such as Eurostat, the Climate Data Center, and OpenStreetMap, as well as corporate sources such as Twitter. Using machine learning and feature selection methods, we were able to identify features in the data provided by SEDE-GPS that can be used to predict lake microbiome alpha diversity.ConclusionThe results presented in this study show that SEDE-GPS is a handy and easy-to-use tool for comprehensive data enrichment for studies of ecology and other processes that are affected by environmental features. Furthermore, we present lists of environmental, socio-economic, and climate features that are predictive for microbial biodiversity in lake ecosystems. These lists indicate that human action has a major impact on lake microbiomes. SEDE-GPS and its source code is available for download at http://SEDE-GPS.heiderlab.deElectronic supplementary materialThe online version of this article (10.1186/s12859-018-2419-4) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

SEDE-GPS: socio-economic data enrichment based on GPS information

Sperlea¹,

Füser²,

Boenigk³

et al. 2018

BMC Bioinformatics

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“…Alternatives to the Boruta method are discussed and evaluated in [22], whereby Boruta was identified as best performing technology among the tested ones if used for different dimensionalities of the data. In 2017 Neumann et al presented the Ensemble Feature Selection (EFS) method which combines multiple FS methods to remove individual biases and give aggregated feature relevance ranges for all of them [23], [24].…”

Section: Introductionmentioning

confidence: 99%

FRI-Feature Relevance Intervals for Interpretable and Interactive Data Exploration

Pfannschmidt

Göpfert

Neumann

et al. 2019

2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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Most existing feature selection methods are insufficient for analytic purposes as soon as high dimensional data or redundant sensor signals are dealt with since features can be selected due to spurious effects or correlations rather than causal effects. To support the finding of causal features in biomedical experiments, we hereby present FRI, an open source Python library that can be used to identify all-relevant variables in linear classification and (ordinal) regression problems. Using the recently proposed feature relevance interval method, FRI is able to provide the base for further general experimentation or in specific can facilitate the search for alternative biomarkers. It can be used in an interactive context, by providing model manipulation and visualization methods, or in a batch process as a filter method.

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“…The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble (Neumann et al, 2017). What's more, several evolutionary based methods are proposed for dimensionality reduction (Chuang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

FCTP-WSRC: Protein–Protein Interactions Prediction via Weighted Sparse Representation Based Classification

Kong

Zhang

et al. 2020

Front. Genet.

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The task of predicting protein-protein interactions (PPIs) has been essential in the context of understanding biological processes. This paper proposes a novel computational model namely FCTP-WSRC to predict PPIs effectively. Initially, combinations of the F-vector, composition (C) and transition (T) are used to map each protein sequence onto numeric feature vectors. Afterwards, an effective feature extraction method PCA (principal component analysis) is employed to reconstruct the most discriminative feature subspaces, which is subsequently used as input in weighted sparse representation based classification (WSRC) for prediction. The FCTP-WSRC model achieves accuracies of 96.67%, 99.82%, and 98.09% for H. pylori, Human and Yeast datasets respectively. Furthermore, the FCTP-WSRC model performs well when predicting three significant PPIs networks: the single-core network (CD9), the multiple-core network (Ras-Raf-Mek-Erk-Elk-Srf pathway), and the cross-connection network (Wnt-related Network). Consequently, the promising results show that the proposed method can be a powerful tool for PPIs prediction with excellent performance and less time.

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EFS: an ensemble feature selection tool implemented as R-package and web-application

Cited by 78 publications

References 17 publications

SEDE-GPS: socio-economic data enrichment based on GPS information

SEDE-GPS: socio-economic data enrichment based on GPS information

FRI-Feature Relevance Intervals for Interpretable and Interactive Data Exploration

FCTP-WSRC: Protein–Protein Interactions Prediction via Weighted Sparse Representation Based Classification

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