HDMAC

"HDMAC" (High-Dimensional Analysis of Molecular Alterations in Cancer) is a web-based interactive platform designed to assist in identifying molecular changes serving as potential prognostic biomarkers or targets for personalized precision medicine. Recognizing the complexity of high-throughput genomic technologies and their statistical challenges, HDMAC offers a user-friendly solution for the rigorous analysis of genomic data, particularly in the context of cancer research.

Key Features and Functionalities:

- Penalized Regression Models: The platform incorporates several penalized regression models suitable for high-dimensional data analysis, including Ridge, Lasso, and adaptive Lasso. These models are tailored for survival analysis using Cox regression and for analyzing binary outcomes through logistic regression.

- First-Step Screening Option: To address the multiple-comparison issue common in large-volume genomic data analysis, HDMAC provides an option for first-step screening, facilitating more accurate identification of significant molecular alterations.

- Hazard Ratio and Estimated Coefficients: HDMAC provides a hazard ratio or an estimated coefficient for each selected gene, enabling users to build a multivariate regression model based on the genes selected through the analysis.

- Cross-Validation for Prediction Power Estimation: The platform employs cross-validation to estimate each regression model's predictive power, ensuring that the analysis results are robust and reliable.

Integration with TCGA Data: HDMAC also offers R codes for downloading complete sets of molecular variables from The Cancer Genome Atlas (TCGA), streamlining the acquisition and analysis of genomic data from this valuable resource.