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关键词： TRANSFERRIN RECEPTOR EXPRESSION   NON-HODGKINS-LYMPHOMAS   PARTIAL LEAST-SQUARES   PROSTATE-CANCER   FEATURE-SELECTION   MICROARRAY DATA   BREAST-CANCER   OUTCOME PREDICTION   COA RACEMASE   HEPSIN  

摘要： Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes.

关键词： Structural magnetic resonance imaging   Schizophrenia   Voxel-based morphometry   Support vector machine   Pattern recognition   Classification   VOXEL-BASED MORPHOMETRY   BRAIN VOLUMES   METAANALYSIS   DISTINGUISH   PATTERNS  

摘要： The purpose of this study is to create a model that can classify schizophrenia patients and healthy controls based on whole brain gray matter densities (voxel-based morphometry, VBM) from structural magnetic resonance imaging (MRI) scans. In addition, we investigated the stability of the accuracy of the models, when built with different sample sizes. Using a support vector machine, we built a model from 239 subjects (128 patients and 111 healthy controls) and classified 71.4% correct (leave-one-out). We replicated and validated this result by testing the unaltered model on a completely independent sample of 277 subjects (155 patients and 122 healthy controls), scanned with a different scanner. The classification rate of the validation sample was 70.4%. The model's discriminative pattern showed, amongst other differences, gray matter density decreases in frontal and superior temporal lobes and hippocampus in schizophrenia patients with respect to healthy controls and increases in gray matter density in basal ganglia and left occipital lobe and. Larger training samples gave more reliable models: Models based on sample sizes smaller than N = 130 should be considered unstable and can even score below chance. (c) 2012 Elsevier Inc. All rights reserved.

关键词： 构成要素   信息要素   使命   编排规律  

摘要： 包装设计中的信息要素是包装设计的重要组成部分。它在商品促销过程中起着传达商品信息的作用,是商品属性直接的体现。本文以包装设计的构成要素为切入点,从包装设计信息要素的构成展开,阐述包装设计中信息要素的作用及其编排规律。

关键词： 检验检测机构   经验交流会   平台建设   共建共享   创新服务   科技厅   浙江省   创新型  

摘要： 7月18日，全省科技创新服务平台建设经验交流会暨检验检测机构共建共享推进会在杭州召开。省科技厅厅长蒋泰维、省质监局局长瞿索芬、科技部平台中心运行监督处处长袁伟等领导出席会议并讲话。省科技厅副厅长丁康生主持会议，各市科技局、质监局，有关公共检验检测机构，厅有关直属单位，高校科技管理部门等单位负责人参加会议。

关键词： 劳动合同   案例   劳动者   用人单位   工作岗位   工资差额   工资标准   加班工资   市场部   销售部  

摘要： 您有何法律问题需要咨询,请发电子邮件至本刊编辑部bjb@***或北京长安律师事务所资深律师梁学军lawyer591@***签订劳动合同是用工单位与劳动者之间确立劳动关系,明确双方权利和义务的一种重要手段。然而许多劳动争议的出现往往也源于劳动合同。7月,就业高峰即将到来,本刊特整理一些与劳动合同有关的案例,供广大经营者参考。案例:2011年2月,赵同(化名)与扬州一家服装公司签订了为期3年的劳动合同,在公司销售部工作,月薪9000元。7月1日,因为公司市场部人员紧张,公司书面通知赵同,将其暂时调入市场部工作。后公司按市场部工资标准支付赵同7月份工资7000元。

关键词： 实验室检验   检验结果   实时监控   室内质控   水   卫生资源   检验工作   看病难  

摘要： 进行检验结果互认是合理有效利用卫生资源,解决看病难与看病贵的一项重要措施,势在必行;检验结果互认,对检验工作而言是一个新课题,也是一个新的机遇和挑战[1,2],它对同一区域内的实验室是一个普遍提高水平的过程,

关键词： 小讲课   医学检验   临床带教  

摘要： 小讲课是医学检验学生在实习阶段理论和实践相结合的重要教学环节。武警上海市总队医院检验科通过对学生临床检验知识、质控、科研和模拟考核等方面的培训,实施由学生和带教教师分别主讲的小讲课,实践证明小讲课可以激发学生的兴趣,开拓学生的思路,提高学生制作幻灯、学术演讲等综合素质以及带教教师的授课水平。

关键词： Bartlett's decomposition   Cholesky's decomposition   FDC   Hypothesis testing   Small sample   CONTROL CHART   MULTIVARIATE   VARIABILITY  

摘要： The variability and complex relationships among process and equipment variables in the hi-tech manufacturing processes, such as semiconductor fabrication, can be characterized by the sample covariance matrix. Test of covariance changes becomes critical for effective fault detection and classification (FDC). However, in modern made-to-order manufacturing, the product-mix is usually high while the order size is becoming smaller. The sample size in such a manufacturing situation becomes an issue diminishing the applicability of existing methods. In view of this point, this paper proposes statistical inference procedures pertaining to detecting change of covariance matrix without demanding a large sample. We apply Bartlett's decomposition and Cholesky's decomposition theories to obtain a matrix T with nice distribution property. Statistical hypothesis testing procedures for possible changes of the covariance matrix are then proposed via aggregating entries of T. The change pattern of T is also studied to construct fault classification rules. Monte Carlo simulation and a dataset collected from an actual semiconductor manufacturing tool are further used to demonstrate the usefulness of our method in FDC. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： MICROARRAY DATA   MOLECULAR CLASSIFICATION   UP-REGULATION   CANCER   PROFILES   ENRICHMENT   SIGNATURES   SELECTION   SURVIVAL   DISEASE  

摘要： Background: Analysis of gene expression data in terms of a priori-defined gene sets has recently received significant attention as this approach typically yields more compact and interpretable results than those produced by traditional methods that rely on individual genes. The set-level strategy can also be adopted with similar benefits in predictive classification tasks accomplished with machine learning algorithms. Initial studies into the predictive performance of set-level classifiers have yielded rather controversial results. The goal of this study is to provide a more conclusive evaluation by testing various components of the set-level framework within a large collection of machine learning experiments. Results: Genuine curated gene sets constitute better features for classification than sets assembled without biological relevance. For identifying the best gene sets for classification, the Global test outperforms the gene-set methods GSEA and SAM-GS as well as two generic feature selection methods. To aggregate expressions of genes into a feature value, the singular value decomposition (SVD) method as well as the SetSig technique improve on simple arithmetic averaging. Set-level classifiers learned with 10 features constituted by the Global test slightly outperform baseline gene-level classifiers learned with all original data features although they are slightly less accurate than gene-level classifiers learned with a prior feature-selection step. Conclusion: Set-level classifiers do not boost predictive accuracy, however, they do achieve competitive accuracy if learned with the right combination of ingredients. Availability: Open-source, publicly available software was used for classifier learning and testing. The gene expression datasets and the gene set database used are also publicly available. The full tabulation of experimental results is available at http://***/CESLT.

关键词： RNA-SEQ   TRANSCRIPTS   METASTASIS   GENOMES  

摘要： Motivation: Shotgun sequence read data derived from xenograft material contains a mixture of reads arising from the host and reads arising from the graft. Classifying the read mixture to separate the two allows for more precise analysis to be performed. Results: We present a technique, with an associated tool Xenome, which performs fast, accurate and specific classification of xenograft-derived sequence read data. We have evaluated it on RNA-Seq data from human, mouse and human-in-mouse xenograft datasets.