MLSeq

MLSeq applies Support Vector Machines (SVM), bagged SVMs, Random Forest, and Classification And Regression Trees (CART) to analyze RNA-Seq data for classification, regression, and discovery of genomic features.

Key Features:

• Machine Learning Integration: Integrates Support Vector Machines (SVM), bagged SVMs, Random Forest, and Classification And Regression Trees (CART) for classification and regression of RNA-Seq datasets.
• Interoperability with Bioconductor: Integrates with the Bioconductor ecosystem in R to enable interaction with other Bioconductor packages.
• Community-Driven Development: Part of the community-driven Bioconductor project comprising 934 interoperable packages and subject to formal review and automated testing.

Scientific Applications:

• Genomic Data Analysis: Performs classification and pattern discovery on high-throughput RNA-Seq genomic datasets.
• Interdisciplinary Research: Applies machine learning methods to genomics to support basic biological questions and applied biomedical research.

Methodology:

Applies Support Vector Machines (SVM), bagged SVMs, Random Forest, and Classification And Regression Trees (CART) to RNA-Seq data for classification and regression analyses.