Several biplot methods applied to gene expression data

DNA microarray experiments result in enormous amount of data, which need careful interpretation. Biplot approaches show simultaneous display of genes and samples in low-dimensional graphs and thus can be used to represent the relationships between genes and samples. There are several different types of biplots, and these methods need to be evaluated because each plot provides different result.     

金·史密斯先生与西方藏学研究

纽约国际藏传佛教文献中心的创始人——藏学家金·史密斯（E．GeneSmith）先生毕生致力于藏学文献的收集、整理、研究工作,身后更是慷慨地将其所收藏的藏学文献赠予了西南民族大学。金·史密斯先生对当代藏学的贡献一方面表现在他所建立的藏学文献数据库,打开了西方藏学研究者经典阅读和运用的新视野;另一方面是金·史密斯先生对藏文典籍的渊博知识和深度研究,启发了一批又一批的藏学家和藏学爱好者。国际藏学家认为“金之后”藏学发展应以文献数字化开拓西方藏学研究新起点,发展西方传统经文分析方法,更注重文献的解读与文献形制方面的研究。     

基因脉冲导入技术及应用

本文首先介绍了细胞电穿孔的基本原理和天津理工大学自行研制的基因脉冲导入仪,重点介绍了细胞电穿孔诱导基因转移在动物细胞、植物细胞、细菌、酵母上的应用及电穿孔技术的一些特殊应用。基因脉冲导入技术应用前景十分广泛。     

A new approach: Interrelated two-way clustering of gene expression data

The paper presents a new approach to interrelated two-way clustering of gene expression data. Clustering of genes has been effected using entropy and a correlation measure, whereas the samples have been clustered using the fuzzy C-means. The efficiency of this approach has been tested on two well known data sets: the colon cancer data set and the leukemia data set. Using this approach, we were able to identify the important co-regulated genes and group the samples efficiently at the same time.     

Evaluating the performance of sparse principal component analysis methods in high-dimensional data scenarios

High-dimensional datasets have exploded into many fields of research, challenging our interpretation of the classic dimension reduction technique, Principal Component Analysis (PCA). Recently proposed Sparse PCA methods offer useful insight into understanding complex data structures. This article compares three Sparse PCA methods through extensive simulations, with the aim of providing guidelines as to which method to choose under a variety of data structures, as dictated by the variance-covariance matrix. A real gene expression dataset is used to illustrate an application of Sparse PCA in practice and show how to link simulation results with real-world problems.     

Clustering objects on subsets of attributes (with discussion)

Summary.  A new procedure is proposed for clustering attribute value data. When used in conjunction with conventional distance-based clustering algorithms this procedure encourages those algorithms to detect automatically subgroups of objects that preferentially cluster on subsets of the attribute variables rather than on all of them simultaneously. The relevant attribute subsets for each individual cluster can be different and partially (or completely) overlap with those of other clusters. Enhancements for increasing sensitivity for detecting especially low cardinality groups clustering on a small subset of variables are discussed. Applications in different domains, including gene expression arrays, are presented.     

中国教育分布与地区经济增长差异——基于29省（市）2000—2011年数据分析

基于29省（市）2000—2011年的基础数据,对中国教育分布及其与经济增长的关系进行实证研究。结果表明：从总体上看,教育基尼系数存在逐年下降的趋势,教育分布日趋平等;中国教育分布与经济增长存在倒＂U＂型关系。分地区研究发现,中西部地区的经济增长更多地依赖物质资本,而东部地区则更多地依赖人力资本。因此,中西部地区应大力发展基础教育,缩小教育分布差距以实现人力资本与实物资本的有效匹配,从而避免实物资本积累回报率的大幅下降;东部地区由于已经具备较高的基础教育水平,则需要增加高等教育投入培养高端人才,加大教育分布差距以促进经济增长。     

基因与人性——生命科学与社会学理论的分析

基因结构与功能的发现是20世纪自然科学的一项重大成就,它也使得我们能够对人性作进一步的认识与阐释。基因可以说是主宰人性的原动力,其特性具有本性(自私和复制)以及习性(与环境的合作交流)两个层面。这些特性也表现在人类身上:人类的本性即动物性是由天赋决定,通过遗传代代相继;习性即文化性则可随环境改变,为个体后天学习得来。孟子的"性善论"与荀子的"性恶论"各执其一端,而孔子"性相近,习相远"的观点,则更为合理地概括了人性之双重性的善恶两面。     

A new partitioning around medoids algorithm

Kaufman and Rousseeuw (1990) proposed a clustering algorithm Partitioning Around Medoids (PAM) which maps a distance matrix into a specified number of clusters. A particularly nice property is that PAM allows clustering with respect to any specified distance metric. In addition, the medoids are robust representations of the cluster centers, which is particularly important in the common context that many elements do not belong well to any cluster. Based on our experience in clustering gene expression data, we have noticed that PAM does have problems recognizing relatively small clusters in situations where good partitions around medoids clearly exist. In this paper, we propose to partition around medoids by maximizing a criteria "Average Silhouette" defined by Kaufman and Rousseeuw (1990). We also propose a fast-to-compute approximation of "Average Silhouette". We implement these two new partitioning around medoids algorithms and illustrate their performance relative to existing partitioning methods in simulations.     

基因技术的伦理困惑初探

基因技术的伦理问题是一个热门话题.本文初步分析了基因科技给人类带来的四大伦理困惑,以期引起基因技术研究者和科技伦理工作者的关注与探索.