Cancer classification based on direction-basis-function neural networks

Automatic molecular classification of cancer based on DNA microarray has many advantages over conventional classification based on morphological appearance of the tumor. Using artificial neural networks is a general approach for automatic classification. In this paper, Direction-Basis-Function neuron and Priority-Ordered algorithm are applied to neural networks. And the leukemia gene expression dataset is used as an example to testify the classifier. The result of our method is compared to that of SVM. It shows that our method makes a better performance than SVM.

基于双层ID空间的Peer-to-Peer文件系统设计与实现

提出并实现了一种建立在Peer-to-Peer 搜索策略上的自组织、自适应、高效和可靠的文件系统DISPFS(Double ID Space basedPeer-to-peer File System)。它在双层ID 空间中构造虚拟存储节点,不仅有效地取得了文件系统内的负载均衡、提高系统利用率,而且保证了动态环境中文件的可靠、快速获取。试验数据表明,DISPFS 在系统接近满负荷运行和文件插入/删除操作频繁的双重压力下依然保持优良的性能。