模糊ＩＳＯＤＡＴＡ在草地植物群落分类上的应用

 模糊集合理论在元素与集合的关系中引入了连续变化的隶属度,因而能适应植物群落作连续和明显间断分布同时存在的情况。本文运用以模糊集合理论为基础的ISODATA聚类分析方法对松嫩平原草地植物群落进行了数量分类,结果说明模糊ISODATA聚类比传统的分类方法有更为客观、灵活的特点,是一种较为理想的植被数量分类方法。     

ILLUMINA Golden Gate DNA甲基化芯片的KL-FCM聚类分析

DNA甲基化作为一种重要的表观遗传修饰,其甲基化水平被发现与疾病的发生发展密切相关,对其进行聚类分析有希望发现新的疾病亚型并建立有效的疾病预测预后方法。传统的聚类分析方法之一模糊C-均值（FCM：Fuzzy C-means）适用于特征空间呈球形或椭球形分布的场景,缺乏普适性。而Illumina Golden Gate平台通过计算基因的各甲基化位点的甲基化百分比描述其甲基化程度,其值位于（0,1）之间,服从混合贝塔分布,不能直接采用FCM进行聚类分析。鉴于此,本文提出基于KL特征测度的KL-FCM聚类算法,采用各样本间的K-L距离作为样本划分时的度量准则。最后,本文基于KL-FCM算法实现IRIS测试数据集和基因的DNA甲基化水平数据的聚类分析。实验结果表明该方法可以以更低的计算负荷获得优于k-均值（k-means）和传统FCM的分类效果。     

用模糊聚类方法分析灰鼠种群数量年间变化与松籽产量的关系并预报灰鼠种群数量

小哺乳动物年间种群数量波动规律及其食物的影响,是人们早已关注的问题(May,1976;Finerty,1980;Xia Wuping等,1982)。研究这些问题一般使用的数学方法是确定性的非线性常微分方程(nonlinear ordinary differential equation),统计学的自相关分析(autocorrelation analysis)或谱分析(spectral analysis)和快速傅里哀变换(FFT)。     

A 23-gene prognostic classifier for prediction of recurrence and survival for Asian breast cancer patients

We report a 23- gene-classifier profiled from Asian women, with the primary purpose of assessing its clinical utility towards improved risk stratification for relapse for breast cancer patients from Asian cohorts within 10 years’ following mastectomy. Four hundred and twenty-two breast cancer patients underwent mastectomy and were used to train the classifier on a logistic regression model. A subset of 197 patients were chosen to be entered into the follow-up studies post mastectomy who were examined to determine the patterns of recurrence and survival analysis based on gene expression of the gene classifier, age at diagnosis, tumor stage and lymph node status, over a 5 and 10 years follow-up period. Metastasis to lymph node (N2-N3) with N0 as the reference (N2 vs. N0 hazard ratio: 2.02 (1.05–8.70), N3 vs. N0 hazard ratio: 4.32 (1.41–13.22) for 5 years) and gene expression of the 23-gene panel (P=0.06, 5 years and 0.02, 10 years, log-rank test) were found to have significant discriminatory effects on the risk of relapse (HR (95%CI):2.50 (0.95–6.50)). Furthermore, survival curves for subgroup analysis with N0-N1 and T1-T2 predicted patients with higher risk scores. The study provides robust evidence of the effectiveness of the 23-gene-classifier and could be used to determine the risk of relapse event (locoregional and distant recurrence) in Asian patients, leading to a meaningful reduction in chemotherapy recommendations.     

用模糊聚类探讨福建三明格氏栲自然保护区植被类型的划分

初步统计三明格氏栲自然保护区有种子植物１４５科,４３４属,１０１０种（变种）。植物区系古老,地理成分以泛热和热带分成分较多,我们运用模糊聚类分析了保护区群落的划分,结果表明,划分的类型与用植物所在群落层中重要值来确定群落乔木层的优势种划分群系的方法基本相一致,计自然植被３个群系,格氏栲林,米储林,栲树林;人工植被５个群系：马尾松林,杉木林,建柏林,楠木林,毛竹林。     

主要土壤肥力因素指标的筛选模型

根据主成分分析模型中的相关结构分析理论及模糊聚类分析的方法,构造了一个对主要土壤肥力因素指标进行筛选的数学模型,运用该模型可对土壤的主要盈力因素指标进行筛选,并对筛选结果的可靠性进行检验,文章的最后给出了模型的应用实例。     

不同灌溉条件下小麦冠层的反射光谱特征及其模糊聚类研究

应用傅立叶滤波和导数法等信号处理技术对获得的不同灌溉条件小麦冠层的反射光谱进行处理,选出最能有效区分小麦不同灌溉条件的波长,用这些波长对应的反射率为指标,用模糊聚类（ＦＣＡ）方法对不同灌溉条件的小麦进行区分。     

9种啮齿动物骨骼形态的模糊聚类分析

本文上关系数对９种啮齿动物的骨骼形态进行模糊聚类分析，所用公式，结果表明：聚类分析是一种适合于啮齿动物骨骼形态的数量分类方法。可用于探讨属，种间的亲缘系。     

Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition

The nucleus is the brain of eukaryotic cells that guides the life processes of the cell by issuing key instructions. For in-depth understanding of the biochemical process of the nucleus, the knowledge of localization of nuclear proteins is very important. With the avalanche of protein sequences generated in the post-genomic era, it is highly desired to develop an automated method for fast annotating the subnuclear locations for numerous newly found nuclear protein sequences so as to be able to timely utilize them for basic research and drug discovery. In view of this, a novel approach is developed for predicting the protein subnuclear location. It is featured by introducing a powerful classifier, the optimized evidence-theoretic K-nearest classifier, and using the pseudo amino acid composition [K.C. Chou, PROTEINS: Structure, Function, and Genetics, 43 (2001) 246], which can incorporate a considerable amount of sequence-order effects, to represent protein samples. As a demonstration, identifications were performed for 370 nuclear proteins among the following 9 subnuclear locations: (1) Cajal body, (2) chromatin, (3) heterochromatin, (4) nuclear diffuse, (5) nuclear pore, (6) nuclear speckle, (7) nucleolus, (8) PcG body, and (9) PML body. The overall success rates thus obtained by both the re-substitution test and jackknife cross-validation test are significantly higher than those by existing classifiers on the same working dataset. It is anticipated that the powerful approach may also become a useful high throughput vehicle to bridge the huge gap occurring in the post-genomic era between the number of gene sequences in databases and the number of gene products that have been functionally characterized. The OET-KNN classifier will be available at www.pami.sjtu.edu.cn/people/hbshen.     

A Method of Fuzzy-Theory-Based Computer-Aided Exploratory Data Analysis

When evaluating functional relationships from given data in an exploratory state of investigation, we only want to get an imagination of a possibly existing trend. Usually, methods of local or empirical regression are chosen to meet this purpose. In the paper we suggest an alternative using the possibilities of modern picture processing methods and computers. The data were transformed into a grey-tone picture representing a fuzzy observation set, the maximum trace of which yields the desired first impression of the trend and may serve as the starting point for further detailed evaluation of the functional relationship. The method is illustrated by a real-data numerical example.