Kernelized information-theoretic metric learning for cancer diagnosis using high-dimensional molecular profiling data

摘要

The molecular gene expressions of tumor and blood samples are useful for detecting cancers. But the design of algorithms for diagnosing cancer using high-dimensional heterogeneous signatures of gene expression data is difficult. How should the high dimensions in molecular signatures of cancer data be scaled for effectively resolving the cancer prognosis problems? Xiong and colleagues present efficient low-storage and nonlinear machine learning algorithms for better understanding of the profiles and potential cures of cancer patients.