title: Mining the structural knowledge of high-dimensional medical data using isomap.
abstract: The paper describes an application of a new, non-linear dimensionality reduction method, named Isomap, for mining the structural knowledge from high-dimensional medical data. The algorithm was evaluated on two publicly available medical datasets: the pathological dataset of breast cancer (241 malignant samples) and the gene expression dataset from the lung (186 tumours). It was found by Isomap that the approximate intrinsic dimensionalities of these two datasets were as low as three. The spatial structures of both datasets were presented in low-dimensional space. Isomap, as a general tool for dimensionality reduction analysis, is helpful in revealing the nonlinear structural knowledge of high-dimensional medical data.
authors: State Key Laboratory of Intelligent Technology Systems, Department of Automation, Tsinghua University, Beijing, China.
authors: S
authors: S
authors: Weng
authors: C
authors: C
authors: Zhang
authors: Z
authors: Z
authors: Lin
authors: X
authors: X
authors: Zhang
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology*; D019295:Computational Biology / Q000379:methods*; D003626:Data Display; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics*; D017711:Nonlinear Dynamics