贝叶斯聚类在基因表达谱知识挖掘中的应用

在大规模基因表达谱的数据分析中引入了一种全新的基于贝叶斯模型的聚类算法,从生物学背景出发,研究了该算法应用在大规模基因表达谱中的理论基础和算法优越性,并应用该算法对两个公共的基因表达数据集进行了知识再挖掘。结果表明,与其他聚类算法相比,该算法在知识发现方面具有显著的优越性。挖掘出的生物学知识对该领域研究人员的实验设计也有一定的启发性。     

模糊聚类分析法及其在群落聚类分析中的应用

以不同种类蔬菜菜地节肢动物群落作为研究对象,介绍模糊聚类分析法在群落相似性分析中的应用。得到如下的结果：在建立模糊等价关系的基础上,根据不同的需要,当λ取０．８９９时,聚合成３个类群,当λ为０．８５２时,聚合成２个类群。而Ｆ－ＰＦＳ聚类分析法则根据群落在聚类类群之间或其中的散布情况,从所有的聚类结果中,选择最佳的聚类方案。本文材料的最佳的聚类结果为：把群落聚合成２类。类群１包括：菜心、小白菜、奶白菜、芥菜、芥兰、通菜、豇豆、四季豆上的节肢动物群落;类群２包括：藤菜与苋菜上节肢动物群落。     

组学时代下机器学习方法在临床决策支持中的应用

随着组学技术的不断发展,对于不同层次和类型的生物数据的获取方法日益成熟。在疾病诊治过程中会产生大量数据,通过机器学习等人工智能方法解析复杂、多维、多尺度的疾病大数据,构建临床决策支持工具,辅助医生寻找快速且有效的疾病诊疗方案是非常必要的。在此过程中,机器学习等人工智能方法的选择显得尤为重要。基于此,本文首先从类型和算法角度对临床决策支持领域中常用的机器学习等方法进行简要综述,分别介绍了支持向量机、逻辑回归、聚类算法、Bagging、随机森林和深度学习,对机器学习等方法在临床决策支持中的应用做了相应总结和分类,并对它们的优势和不足分别进行讨论和阐述,为临床决策支持中机器学习等人工智能方法的选择提供有效参考。     

模糊聚类分析法在昆虫杆状病毒分类中的应用

采用一种新的数学方法——模糊聚类分析法,对已知包涵体蛋白N-末端序列的10种昆虫杆状病毒进行分类,其结果与Fitch方法的结果完全一致。这种分类方法并不只是对各序列进行简单的类别划分,而且也表示了各类间相似的程度及在某一类内各序列间的进化关系。     

素质教育在临床检验实习教学中的应用体会

目的探讨素质教育在临床检验实习教学中的应用体会。方法在临床检验实习教学过程中,加强临床检验实习生素质教育。结果强化素质教育,可提升检验系学生的培养质量。结论做好临床检验实习生的素质教育培养不仅可以提升个人修养,还可以提高实习生分析问题、解决问题的能力,为培养出德能兼备的检验医师打下良好的基础。     

基于c-均值聚类的粗糙集神经网络在心脏病诊断中的应用

采用c-均值聚类法将决策表中的连续条件属性进行离散化,用粗糙集处理离散化后的决策表系统得到简化规则,然后将规则集输入BP神经网络进行训练,并对测试集进行预测.以此模型对一组有关心脏病诊断的数据进行处理,得到的预测判准率达85%,而单独使用粗糙集或BPNN进行预测,则判准率分别为76%和82%;若在粗糙集和BPNN联用模型中,对原始数据采用传统的等距离离散化和等频率离散化等离散化方法,预测判准率则分别只有53%和77%.     

粗糙集模糊聚类分析法在昆虫分类研究中的应用

本文根据昆虫图像,对半翅目、鳞翅目、鞘翅目的28种昆虫提取的形状参数、叶状性、球状性等7项数学形态特征进行了粗糙集模糊聚类分析。在粗糙集处理的基础上,分别进行7指标和3指标（相对约简）两种不同的模糊聚类分析法相比较。结果显示,在作为目级阶元分类指标时,各项特征的重要性依次为：(似圆度、偏心率)＞（亮斑数、球状性、圆形性）＞（叶状性、形状参数）;粗糙集分类正确率优于模糊聚类分析法;粗糙集处理后的3指标分类正确率优于未处理的7指标分类正确率。结论认为,粗糙集理论在昆虫依据数学形态特征进行分类方面与统计分析方法相比更有优势,粗糙集滤过指标后再进行模糊聚类法分析在昆虫分类研究上具有重要意义。     

案例教学法在临床检验技师资格考试辅导中的应用

正临床检验技师是临床工作不可缺少的部分,他们负责检验人体体液、血液、排泄物、感染微生物等标本,通过客观准确的化验指标,为临床医生提供治疗依据。医学检验技师资格考试的内容范围广泛,不仅包括临床检验的专业课程,还和医学的基础课程及临床医学的其它课程也密切相关,这就要求检验人员的临床知识必须非常丰富,知识涉及面必须广泛。检验技师资格考试大纲要求,检验技师考试分为基础知识"、"相关专业知识"、"专业知识"、"专业实践能力"等4个科目[1]。通过对     

直接模糊聚类的截取水平选择及其在植被数量分析中的应用

植被生态研究中常用的聚类法,是着眼于研究区域中植被和环境因子呈间断分布或变化梯度较大的一类情况,对原始数据中各个体按其属性进行归类。直接模糊聚类法则以各个体间的属性相近程度来定义一模糊关系矩阵,然后对矩阵取不同的水平截集,从而得出一等级分类。当模糊关系确定以后,截取水平的选择就成了聚类结果的决定性因素。至目前为止,直接模糊聚类中的截取水平通常由分析者主观给定,或者是以逐步试验,逐步修改的方法确定的。这样,聚类结果就不可避免地带有较大的主观和任意性。笔者认为截取水平应选在模糊关系变化较大之处,使聚类结果尽可能地反映原始数据的结构特征。这一原理已被实施于一通用软件中,实例分析表明,如此选择的截取水平确能比较客观地反映原始数据的特征,从而得出较为合理的聚类结果。     

Assisted Knowledge Discovery for the Maintenance of Clinical Guidelines

Background

Improving antibiotic prescribing practices is an important public-health priority given the widespread antimicrobial resistance. Establishing clinical practice guidelines is crucial to this effort, but their development is a complex task and their quality is directly related to the methodology and source of knowledge used.

Objective

We present the design and the evaluation of a tool (KART) that aims to facilitate the creation and maintenance of clinical practice guidelines based on information retrieval techniques.

Methods

KART consists of three main modules 1) a literature-based medical knowledge extraction module, which is built upon a specialized question-answering engine; 2) a module to normalize clinical recommendations based on automatic text categorizers; and 3) a module to manage clinical knowledge, which formalizes and stores clinical recommendations for further use. The evaluation of the usability and utility of KART followed the methodology of the cognitive walkthrough.

Results

KART was designed and implemented as a standalone web application. The quantitative evaluation of the medical knowledge extraction module showed that 53% of the clinical recommendations generated by KART are consistent with existing clinical guidelines. The user-based evaluation confirmed this result by showing that KART was able to find a relevant antibiotic for half of the clinical scenarios tested. The automatic normalization of the recommendation produced mixed results among end-users.

Conclusions

We have developed an innovative approach for the process of clinical guidelines development and maintenance in a context where available knowledge is increasing at a rate that cannot be sustained by humans. In contrast to existing knowledge authoring tools, KART not only provides assistance to normalize, formalize and store clinical recommendations, but also aims to facilitate knowledge building.     

基因芯片数据的监督聚类分析

随着后基因组时代的到来,基因芯片技术越来越多地被应用到功能基因组的研究当中。如何快速有效地分析基因芯片实验所获得的大量生物学数据,成为当前一项具有重要意义的研究工作。监督聚类(supervised clustering analysis)是聚类分析的一种,它根据样本的先验信息或假设来决定样本的分类,并据此建立判别模型,继而利用该判别模型对未知对象进行分类。该方法已经成功应用到生物医学研究中的许多领域,成为分析基因芯片数据的重要手段。     

临床实验室检验差错的测量研究

实验室对患者安全有重大的影响,因为所有诊断的80~90%是基于实验室检验作出的。据报道,实验室差错发生率为所有试验结果的0.012-0.6%。患者安全是一个管理问题,其可通过实施积极的系统来识别及监控质量故障得到提高。这可通过包括事件报告随后的根本原因分析的反应方法得到促进。这可导致识别及纠正该系统中方针及程序的不足之处。另一种方法是前瞻性方法,如失效模式和效应分析(或故障模式和影响分析)。本文的重点是整个检验过程,预测主要不良事件以及先发制人防止它们的发生。它可用于高风险过程的前瞻性的风险分析以减少实验室及其他患者医疗领域内出差错的可能性。     

Data-Fusion in Clustering Microarray Data: Balancing Discovery and Interpretability

While clustering genes remains one of the most popular exploratory tools for expression data, it often results in a highly variable and biologically uninformative clusters. This paper explores a data fusion approach to clustering microarray data. Our method, which combined expression data and Gene Ontology (GO)-derived information, is applied on a real data set to perform genome-wide clustering. A set of novel tools is proposed to validate the clustering results and pick a fair value of infusion coefficient. These tools measure stability, biological relevance, and distance from the expression-only clustering solution. Our results indicate that a data-fusion clustering leads to more stable, biologically relevant clusters that are still representative of the experimental data.     

Diagnostic Key to Mycobacteria Encountered in Clinical Laboratories

A diagnostic key has been developed which will permit identification of most mycobacteria encountered in clinical laboratories. The key is based on performance of a few simple tests. The efficiency and accuracy of the key was evaluated in terms of correlation between identifications based on the few tests and those arrived at through application of the techniques of numerical taxonomy, which involves a large battery of tests. Of 679 cultures of mycobacteria other than Mycobacterium tuberculosis, 86.5% were correctly identified by use of the key, and only 1.8% of the cultures were erroneously identified. The remaining cultures required further examination.     

How to Improve Postgenomic Knowledge Discovery Using Imputation

While microarrays make it feasible to rapidly investigate many complex biological problems, their multistep fabrication has the proclivity for error at every stage. The standard tactic has been to either ignore or regard erroneous gene readings as missing values, though this assumption can exert a major influence upon postgenomic knowledge discovery methods like gene selection and gene regulatory network (GRN) reconstruction. This has been the catalyst for a raft of new flexible imputation algorithms including local least square impute and the recent heuristic collateral missing value imputation, which exploit the biological transactional behaviour of functionally correlated genes to afford accurate missing value estimation. This paper examines the influence of missing value imputation techniques upon postgenomic knowledge inference methods with results for various algorithms consistently corroborating that instead of ignoring missing values, recycling microarray data by flexible and robust imputation can provide substantial performance benefits for subsequent downstream procedures.     

Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study

Background  

A method to evaluate and analyze the massive data generated by series of microarray experiments is of utmost importance to reveal the hidden patterns of gene expression. Because of the complexity and the high dimensionality of microarray gene expression profiles, the dimensional reduction of raw expression data and the feature selections necessary for, for example, classification of disease samples remains a challenge. To solve the problem we propose a two-level analysis. First self-organizing map (SOM) is used. SOM is a vector quantization method that simplifies and reduces the dimensionality of original measurements and visualizes individual tumor sample in a SOM component plane. Next, hierarchical clustering and K-means clustering is used to identify patterns of gene expression useful for classification of samples.     

A New-Fangled FES-k-Means Clustering Algorithm for Disease Discovery and Visual Analytics

The central purpose of this study is to further evaluate the quality of the performance of a new algorithm. The study provides additional evidence on this algorithm that was designed to increase the overall efficiency of the original k-means clustering technique—the Fast, Efficient, and Scalable k-means algorithm (FES-k-means). The FES-k-means algorithm uses a hybrid approach that comprises the k-d tree data structure that enhances the nearest neighbor query, the original k-means algorithm, and an adaptation rate proposed by Mashor. This algorithm was tested using two real datasets and one synthetic dataset. It was employed twice on all three datasets: once on data trained by the innovative MIL-SOM method and then on the actual untrained data in order to evaluate its competence. This two-step approach of data training prior to clustering provides a solid foundation for knowledge discovery and data mining, otherwise unclaimed by clustering methods alone. The benefits of this method are that it produces clusters similar to the original k-means method at a much faster rate as shown by runtime comparison data; and it provides efficient analysis of large geospatial data with implications for disease mechanism discovery. From a disease mechanism discovery perspective, it is hypothesized that the linear-like pattern of elevated blood lead levels discovered in the city of Chicago may be spatially linked to the city''s water service lines.