Rankgene

Rankgene computes the predictive power of individual genes from gene expression data to identify and rank diagnostic and classifier-relevant genes.

Key Features:

• Integration of Ranking Criteria: Incorporates multiple ranking criteria including the t-statistic and one-dimensional support vector machines (SVMs) for gene ranking.
• Diagnostic Gene Identification: Evaluates and ranks genes by their ability to distinguish between sample types to identify diagnostic genes.
• Feature Selection: Produces ranked gene lists to enable feature selection for classification tasks and downstream analysis.

Scientific Applications:

• Disease Classification: Identifies genes with high predictive power to support development of diagnostic markers and disease classification models.
• Functional Genomics: Highlights genes that distinguish biological states, aiding studies of gene function and condition-specific expression.

Methodology:

Computes gene-level predictive power using statistical and machine learning techniques and ranks genes via integrated criteria such as the t-statistic and one-dimensional support vector machines (SVMs).