KnowSeq

KnowSeq performs automated gene expression analysis and biomarker selection from transcriptomic data as an R/Bioconductor package to identify disease-associated gene signatures.

Key Features:

• R/Bioconductor implementation: Implemented as an R/Bioconductor package for programmatic analysis within the Bioconductor ecosystem.
• Raw data processing: Supports preprocessing of raw files originating from multiple transcriptomic platforms and diverse sequencing technologies.
• Gene selection algorithms: Employs advanced algorithms, including a novel procedure for selecting representative genes in multiclass problems.
• Classification and feature selection: Integrates an intelligent classification system and a range of established classification and feature-selection methods for assigning new samples to classes.
• Biological knowledge integration: Links gene expression results to Gene Ontologies, pathways, and related diseases for downstream interpretation.

Scientific Applications:

• Disease-associated signature identification: Processes differentially expressed genes (DEGs) to identify gene signatures associated with diseases.
• Cancer classification studies: Achieved high classification rates in biclass breast cancer and multiclass lung cancer studies.
• Biological knowledge extraction: Facilitates mapping of expression-based findings to Gene Ontologies, pathways, and disease associations to support biological interpretation.

Methodology:

Data preprocessing from multiple sources; processing of differentially expressed genes (DEGs); gene selection using advanced algorithms tailored for multiclass problems; and classification of new samples using established classification and feature-selection methods.