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GeneSelectR: 'GeneSelectR' - Comprehensive Feature Selection Workflow for Bulk RNAseq Datasets

The workflow is a versatile R package designed for comprehensive feature selection in bulk RNAseq datasets. Its key innovation lies in the seamless integration of the 'Python' 'scikit-learn' (&lt;<a href="https://scikit-learn.org/stable/index.html" target="_top">https://scikit-learn.org/stable/index.html</a>&gt;) machine learning framework with R-based bioinformatics tools. 'GeneSelectR' performs robust Machine Learning-driven (ML) feature selection while leveraging 'Gene Ontology' (GO) enrichment analysis as described by Thomas PD et al. (2022) &lt;<a href="https://doi.org/10.1002%2Fpro.4218" target="_top">doi:10.1002/pro.4218</a>&gt;, using 'clusterProfiler' (Wu et al., 2021) &lt;<a href="https://doi.org/10.1016%2Fj.xinn.2021.100141" target="_top">doi:10.1016/j.xinn.2021.100141</a>&gt; and semantic similarity analysis powered by 'simplifyEnrichment' (Gu, Huebschmann, 2021) &lt;<a href="https://doi.org/10.1016%2Fj.gpb.2022.04.008" target="_top">doi:10.1016/j.gpb.2022.04.008</a>&gt;. This combination of methodologies optimizes computational and biological insights for analyzing complex RNAseq datasets.



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GeneSelectR: 'GeneSelectR' - Comprehensive Feature Selection Workflow for Bulk RNAseq Datasets

https://cran.rstudio.com/web/packages/GeneSelectR/index.html

The workflow is a versatile R package designed for comprehensive feature selection in bulk RNAseq datasets. Its key innovation lies in the seamless integration of the 'Python' 'scikit-learn' (&lt;<a href="https://scikit-learn.org/stable/index.html" target="_top">https://scikit-learn.org/stable/index.html</a>&gt;) machine learning framework with R-based bioinformatics tools. 'GeneSelectR' performs robust Machine Learning-driven (ML) feature selection while leveraging 'Gene Ontology' (GO) enrichment analysis as described by Thomas PD et al. (2022) &lt;<a href="https://doi.org/10.1002%2Fpro.4218" target="_top">doi:10.1002/pro.4218</a>&gt;, using 'clusterProfiler' (Wu et al., 2021) &lt;<a href="https://doi.org/10.1016%2Fj.xinn.2021.100141" target="_top">doi:10.1016/j.xinn.2021.100141</a>&gt; and semantic similarity analysis powered by 'simplifyEnrichment' (Gu, Huebschmann, 2021) &lt;<a href="https://doi.org/10.1016%2Fj.gpb.2022.04.008" target="_top">doi:10.1016/j.gpb.2022.04.008</a>&gt;. This combination of methodologies optimizes computational and biological insights for analyzing complex RNAseq datasets.



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https://cran.rstudio.com/web/packages/GeneSelectR/index.html

GeneSelectR: 'GeneSelectR' - Comprehensive Feature Selection Workflow for Bulk RNAseq Datasets

The workflow is a versatile R package designed for comprehensive feature selection in bulk RNAseq datasets. Its key innovation lies in the seamless integration of the 'Python' 'scikit-learn' (&lt;<a href="https://scikit-learn.org/stable/index.html" target="_top">https://scikit-learn.org/stable/index.html</a>&gt;) machine learning framework with R-based bioinformatics tools. 'GeneSelectR' performs robust Machine Learning-driven (ML) feature selection while leveraging 'Gene Ontology' (GO) enrichment analysis as described by Thomas PD et al. (2022) &lt;<a href="https://doi.org/10.1002%2Fpro.4218" target="_top">doi:10.1002/pro.4218</a>&gt;, using 'clusterProfiler' (Wu et al., 2021) &lt;<a href="https://doi.org/10.1016%2Fj.xinn.2021.100141" target="_top">doi:10.1016/j.xinn.2021.100141</a>&gt; and semantic similarity analysis powered by 'simplifyEnrichment' (Gu, Huebschmann, 2021) &lt;<a href="https://doi.org/10.1016%2Fj.gpb.2022.04.008" target="_top">doi:10.1016/j.gpb.2022.04.008</a>&gt;. This combination of methodologies optimizes computational and biological insights for analyzing complex RNAseq datasets.

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      'GeneSelectR' - Comprehensive Feature Selection Workflow for Bulk RNAseq Datasets [R package GeneSelectR version 1.0.1]
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      Damir Zhakparov

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      The workflow is a versatile R package designed for comprehensive feature selection in bulk RNAseq datasets. Its key innovation lies in the seamless integration of the 'Python' 'scikit-learn' (&lt;<a href="https://scikit-learn.org/stable/index.html" target="_top">https://scikit-learn.org/stable/index.html</a>&gt;) machine learning framework with R-based bioinformatics tools. 'GeneSelectR' performs robust Machine Learning-driven (ML) feature selection while leveraging 'Gene Ontology' (GO) enrichment analysis as described by Thomas PD et al. (2022) &lt;<a href="https://doi.org/10.1002%2Fpro.4218" target="_top">doi:10.1002/pro.4218</a>&gt;, using 'clusterProfiler' (Wu et al., 2021) &lt;<a href="https://doi.org/10.1016%2Fj.xinn.2021.100141" target="_top">doi:10.1016/j.xinn.2021.100141</a>&gt; and semantic similarity analysis powered by 'simplifyEnrichment' (Gu, Huebschmann, 2021) &lt;<a href="https://doi.org/10.1016%2Fj.gpb.2022.04.008" target="_top">doi:10.1016/j.gpb.2022.04.008</a>&gt;. This combination of methodologies optimizes computational and biological insights for analyzing complex RNAseq datasets.
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