Supervised Dimension Reduction for Large-Scale “Omics” Data With Censored Survival Outcomes Under Possible Non-Proportional Hazards

Abstract

The past two decades have witnessed significant advances in high-throughput "omics" technologies such as genomics, proteomics, metabolomics, transcriptomics and radiomics. These technologies have enabled simultaneous measurement of the expression levels of tens of thousands of features from individual patient samples and have generated enormous amounts of data that require analysis and interpretation. One specific area of interest has been in studying the relationship between these features and patient outcomes, such as overall and recurrencefree survival, with the goal of developing a predictive "omics" profile. Large-scale studies often suffer from the presence of a large fraction of censored observations and potential time-varying effects of features, and methods for handling them have been lacking. In this paper, we propose supervised methods for feature selection and survival prediction that simultaneously deal with both issues. Our approach utilizes continuum power regression (CPR)-a framework that includes a variety of regression methods-in conjunction with the parametric or semi-parametric accelerated failure time (AFT) model. Both CPR and AFT fall within the linear models framework and, unlike black-box models, the proposed prognostic index has a simple yet useful interpretation. We demonstrate the utility of our methods using simulated and publicly available cancer genomics data. Supervised dimension reduction for large-scale "omics" data with censored survival outcomes under possible non-proportional hazards

Improving ISOMAP Efficiency with RKS: A Comparative Study with t-Distributed Stochastic Neighbor Embedding on Protein Sequences

“…t-SNE has been widely adopted in various domains, including image analysis, natural language processing, and bioinformatics. Many studies have explored the effectiveness of t-SNE in visualizing high-dimensional biological data, such as gene expression profiles [25], single-cell RNA sequencing data [26], and protein structures [27]. t-SNE has demonstrated its ability to reveal meaningful patterns and clusters in complex biological datasets.…”