Efficient selection of discriminative genes from microarray gene expression data for cancer diagnosis

A new mutual information (MI)-based feature-selection method to solve the so-called large p and small n problem experienced in a microarray gene expression-based data is presented. First, a grid-based feature clustering algorithm is introduced to eliminate redundant features. A huge gene set is then greatly reduced in a very efficient way. As a result, the computational efficiency of the whole feature-selection process is substantially enhanced. Second, MI is directly estimated using quadratic MI together with Parzen window density estimators. This approach is able to deliver reliable results even when only a small pattern set is available. Also, a new MI-based criterion is proposed to avoid the highly redundant selection results in a systematic way. At last, attributed to the direct estimation of MI, the appropriate selected feature subsets can be reasonably determined. © 2005 IEEE

Three little pieces for computer and relativity

Numerical relativity has made big strides over the last decade. A number of problems that have plagued the field for years have now been mostly solved. This progress has transformed numerical relativity into a powerful tool to explore fundamental problems in physics and astrophysics, and I present here three representative examples. These "three little pieces" reflect a personal choice and describe work that I am particularly familiar with. However, many more examples could be made.Comment: 42 pages, 11 figures. Plenary talk at "Relativity and Gravitation: 100 Years after Einstein in Prague", June 25 - 29, 2012, Prague, Czech Republic. To appear in the Proceedings (Edition Open Access). Collects results appeared in journal articles [72,73, 122-124