NetRD:一种利用Bing搜索结果补充文献挖掘证据集的工具

目的当前生物文献挖掘工作的重心是改进各挖掘模块性能,以提升挖掘结果的可信度,但有很大比例的挖掘结果其文献证据很少,为此本文提出一个利用Bing搜索引擎从海量Web数据中为文献挖掘得到的生物实体关联对提供补充证据的工具系统。方法利用现有文本挖掘技术从PubMed文献中挖掘一批生物实体关联对,引入BingWeb搜索模块,以生物实体名作为关键词从Web中利用Bing开放搜索API得到一批搜索结果,将这些结果整理成新的数据源,最终从该新的数据源中挖掘得到一批来自Web的补充证据。结果本系统(http://bioinfo.ustc.edu.cn/NetRD)对文献证据较少的生物实体关联对提供了有效的补充证据支持,丰富了文献挖掘结果最终的证据集。结论以Web数据作为补充数据源,能够有效地为文献证据很少的生物实体对提供证据补充,为相关研究者确认两个生物实体之间的关联提供重要参考。     

雷达图多元评价针刺治疗腰椎间盘突出症的系统评价/Meta分析

文题释义：雷达图：是以从同一点开始的轴上表示的3个或更多个定量变量二维图表的形式显示多变量数据的图形方法。轴的相对位置和角度通常是无信息的。雷达图也称为网络图、蜘蛛图、星图、星图、蜘蛛网图、不规则多边形、极坐标图或Kiviat图,它相当于平行坐标图,轴径向排列。 背景：目前国内外在通过雷达图对针刺治疗腰椎间盘突出症的系统评价/Meta再评价分析其质量证据等方面的研究报道较少,不足以为临床决策提供参考。 目的：运用雷达图多元评价思维定性定量再评价针刺治疗腰椎间盘突出症的系统评价/Meta分析,为更多的临床实践与医学研究提供可视化、科学化的决策手段。 方法：运用计算机检索国内外8大中英文数据库,收集针刺治疗腰椎间盘突出症的系统评价/Meta分析资料,检索时间为从数据库建库至2019年8月。从发表年份、研究类型、AMSTAR 2方法学质量评分、PRISMA报告学质量评分、同质性及发表偏倚风险等6个方面多元评价文献质量,计算其秩数平均分。通过运用GRADE证据质量评价工具对所有纳入文献的结局指标进行质量分级。采用Excel 2013绘制并优化雷达图以供数据的直观分析。 结果与结论：共纳入11篇合格文献,根据对雷达图数据提取,结果显示所有纳入文献的质量秩数平均得分为6.95分。主要存在项目注册、结构式摘要、检索策略、排除文献清单、发表偏倚等方面的问题。GRADE证据质量评分偏低,以极低质量为主。针刺治疗腰椎间盘突出症的系统评价/Meta分析文献质量不高,且需从方法学质量和报告学质量方面着手,不断加强以期进一步提高文献质量。雷达标绘图作为一种图形评价手段,能够直观简洁地展现所需数据的分布情况。 ORCID: 0000-0001-8260-0381(丘明旺);0000-0001-7655-5086(方婉仪) 中国组织工程研究杂志出版内容重点：人工关节;骨植入物;脊柱;骨折;内固定;数字化骨科;组织工程     

常用医学全文数据库的比较介绍

由于全文检索及数据库技术的快速发展,一种独立的电子资源-全文数据库逐渐受到广大用户的普遍关注.全文数据库是将文献全文以机读形式存储,用自然语言表达检索课题,借助于截词、逻辑等匹配方法,直接对文献正文进行查找,以检出所需文献的一种方式.由于具有检索直接、使用方便、专指性好等优点,给用户查找和获取全文带来了极大方便.我馆以建立镜像系统及内部用户等形式引进几种外文全文数据库,针对用户在选择数据库和使用上的问题,本文主要介绍几种常用的中外文医学全文数据库及检索方法,对用户迅速准确获取原始文献提供指导和帮助.     

关联规则挖掘在中医证候信息数据库系统中的应用

关联规则挖掘是数据挖掘的一个重要研究方向,它可以揭示数据之间有趣的关联或者相关联系.在此基础上结合特定系统的用户行为数据,还可以进行用户行为分析.本文以一个开发的中医证候信息数据库系统为基础,结合中医数据挖掘实际,论述了在此系统架构下关联规则挖掘算法的成功运用,并提出了一个高效的中医数据挖掘模型.系统运行状况表明,在该模型指导下的系统在智能和用户使用人性化方面都比原系统有明显的优势.     

职业紧张与抑郁相关的meta分析:基于付出—回报模型

目的:探讨我国人群职业紧张与抑郁之间的关系.方法:系统检索中外数据库中有关我国人群职业紧张与抑郁关系的文献,采用meta分析方法合并分析职业紧张与抑郁的相关关系.分析工作类型、抑郁测量工具对二者关系的调节效应.采用漏斗图和Egger检验分析出版偏误.结果:本研究最终纳入22篇文献.合并的职业紧张付出、回报、内在投入与抑...     

Carba NP试验在产碳青霉烯酶肠杆菌科细菌检测价值的meta分析

目的:系统评价Carba NP试验对产碳青霉烯酶菌株的诊断价值.方法:检索PubMed数据库、EMBASE数据库、Cochrane图书馆、维普中文科技期刊数据库、中国知网、万方数据库、中国生物医学文献数据库(CBM),并辅以文献追溯、手工检索,检索时间为2001年1月1日至2016年6月30日.严格按照纳入和排除标准进行文献筛选.参照诊断准确性研究质量评价工具(Quality Assessment of Diagnostic Accuracy Studies 2,UADAS-2)对纳入文献进行质量评价.应用STATA14.0软件进行数据提取及质量评价、统计和数据分析,绘制森林图分析结果,用漏斗图和Deek检验评价纳入文献的发表偏倚情况.结果:纳入的29项研究的合并敏感性和特异性分别是0.97(95％CI:0.93～0.98)和1.0(95％CI:1.0～1.0),说明敏感性及特异性较高.经异质性分析检验,敏感性和特异性的I2分别是96.49(95％CI:95.79～97.18)和69.88(95％CI:58.69～81.08),说明纳入数据存在异质性.结论:Carba NP试验对于产碳青霉烯酶肠杆菌科细菌(Carbapenem-resistant Enterobacteriaceae,CRE)的检测具有很高的敏感性和特异性.     

血清胱抑素C水平与冠心病关系的Meta分析

目的 综合评价血清胱抑素C(CysC)与冠心病的关系,旨在为冠心病的预防和诊治提供循证医学证据.方法 计算机检索SinoMed、ScienceDirect、Embase、Pubmed关于CysC与CHD相关性的研究,搜索时间为自建库至2015年.由两名独立的评价人员根据纳入和排除标准,筛选文献,提取数据.采用Review Manager 5.3软件进行Meta分析.绘制倒漏斗图,行Begg's和Egg's检验,判定发表偏倚的可能性.逐一剔除纳入研究,绘制敏感性分析图,进行敏感性分析.结果 纳入研究10个,患者977例.Meta分析结果:以队列研究组为对象,与对照组比较,冠心病组[SMD=1.51,95％CI(1.20,1.82)]的CysC水平明显升高,差异显著有统计学意义(Z=9.51,P＜0.05);以病例对照研究组为对象,与对照组比较,冠心病组[SMD=3.27,95％ CI(2.95,3.60)]的CysC水平明显升高,差异显著有统计学意义(Z=19.92,P＜0.05);纳入研究倒漏斗图呈对称分布,Begg's和Egg's检验P值分别为0.72和0.88,提示研究间存在发表的可能性小.结论 高水平的血清CysC与CHD密切相关.     

儿童及青少年间变性大细胞淋巴瘤预后因素的Meta分析

目的:应用Meta分析方法,对儿童及青少年间变性大细胞淋巴瘤(ALCL)一些有争议的临床预后因素进行定量分析,以客观评价这些因素对患者生存率的影响.方法:计算机检索中英文数据库,搜索从建库起至2020年12月31日间,儿童及青少年ALCL回顾性队列分析的文献.筛选文献并提取其中的生存数据,应用Review Manage...     

基于VTK的磁源性影像三维可视化

目的 改善磁源性影像的三维可视化效果.方法 对MRI数据进行图像分割,利用可视化工具(vTK)包对分割后的图像进行三维重建,获得大脑皮层的三维模型;以患者做脑磁图检查时设置的3个外部标志点(左耳窝、右耳窝和鼻根)为基准,通过坐标转换,将脑磁图源分析软件输出的偶极子信息形象化地表示在三维显示窗口中.结果 在三维图像上可以...     

基于Pearson相关系数的血清YKL-40水平与哮喘指标相关性研究的Meta分析

目的 利用Meta分析的方法,基于Pearson相关系数评估血清YKL-40水平与支气管哮喘相关实验室指标、哮喘患者基线特征的相关性.方法 通过搜索Pubmed、OVID、Cochrane数据库,检索时间为建库至2020年10月,最终根据纳入标准筛选文献.评估纳入文献质量及提取必要数据后,采用Revman 5.3软件进行Meta分析.结果 最终纳入14篇文献,参与项目的受试者共4125例.文献质量较高.Meta分析结果提示血清YKL-40水平与哮喘患者年龄、BMI、吸烟史、肺功能指标存在相关性,与血嗜酸性粒细胞绝对值、IgE、FeNO、血中性粒细胞绝对值无关.结论 血清YKL-40可能一定程度上反映哮喘患者肺损伤程度,却与IL-13以外的Th2免疫反应机制无关.     

High-density rat radiation hybrid maps containing over 24,000 SSLPs, genes, and ESTs provide a direct link to the rat genome sequence

The laboratory rat is a major model organism for systems biology. To complement the cornucopia of physiological and pharmacological data generated in the rat, a large genomic toolset has been developed, culminating in the release of the rat draft genome sequence. The rat draft sequence used a variety of assembly packages, as well as data from the Radiation Hybrid (RH) map of the rat as part of their validation. As part of the Rat Genome Project, we have been building a high-density RH map to facilitate data integration from multiple maps and now to help validate the genome assembly. By incorporating vectors from our lab and several other labs, we have doubled the number of simple sequence length polymorphisms (SSLPs), genes, expressed sequence tags (ESTs), and sequence-tagged sites (STSs) compared to any other genome-wide rat map, a total of 24,437 elements. During the process, we also identified a novel approach for integrating the RH placement results from multiple maps. This new integrated RH map contains approximately 10 RH-mapped elements per Mb on the genome assembly, enabling the RH maps to serve as a scaffold for a variety of data visualization tools.     

Data mining and visualization for decision support and modeling of public health-care resources

This paper proposes an innovative use of data mining and visualization techniques for decision support in planning and regional-level management of Slovenian public health-care. Data mining and statistical techniques were used to analyze databases collected by a regional Public Heath Institute. We also studied organizational aspects of public health resources in the selected Celje region with the objective to identify the areas that are atypical in terms of availability and accessibility of public health services for the population. The most important step was the detection of outliers and the analysis of availability and accessibility deviations. The results are applicable to health-care planning and support in decision making by local and regional health-care authorities. In addition to the practical results, which are directly useful for decision making in planning of the regional health-care system, the main methodological contribution of the paper are the developed visualization methods that can be used to facilitate knowledge management and decision making processes.     

一种基于语义模型的乳腺钙化病灶标注方法

为了实现对乳腺X线影像的医学语义标注,提出一种利用贝叶斯网络(BN)的多层乳腺影像钙化点语义建模方法。该方法首先用支持向量机(SVM)得到从图像底层视觉特征到中层特征语义的映射,然后再利用BN融合特征语义,最终提取出高层病症语义即恶性程度的概率表达,完成语义模型。将模型应用于乳腺图像的语义标注,本实验选用142幅图像作为训练集,50幅图像作为测试集,结果表明,样本标注诊断语义的准确率:恶性为81.48%,良性为73.91%。     

Deterministic projection by growing cell structure networks for visualization of high-dimensionality datasets

Recent advances in clinical proteomics data acquisition have led to the generation of datasets of high complexity and dimensionality. We present here a visualization method for high-dimensionality datasets that makes use of neuronal vectors of a trained growing cell structure (GCS) network for the projection of data points onto two dimensions. The use of a GCS network enables the generation of the projection matrix deterministically rather than randomly as in random projection. Three datasets were used to benchmark the performance and to demonstrate the use of this deterministic projection approach in real-life scientific applications. Comparisons are made to an existing self-organizing map projection method and random projection. The results suggest that deterministic projection outperforms existing methods and is suitable for the visualization of datasets of very high dimensionality.     

Multivariable network associated with cognitive decline and dementia

Data mining of a large data base from the population longitudinal study named "The Conselice Study" has been the focus of the present investigation. Initially, 65 years old or older participants were interviewed, underwent medical and cognitive examination, and were followed up for 5 years: 937 subjects completed the follow-up. Relationships of 35 genetic and/or phenotypic factors with incident cognitive decline and dementia were investigated. The new mathematical approach, called the Auto Contractive Map (AutoCM), was able to show the differential importance of each variables. This new variable processing created a semantic connectivity map that: (a) preserved non-linear associations; (b) showed connection schemes; (c) captured the complex dynamics of adaptive interactions. This method, based on an artificial adaptive system, was able to define the association strength of each variable with all the others. Few variables resulted to be aggregation points and were considered as major biological hubs. Three hubs were identified in the hydroxyl-methyl-gutaryl-CoA reductase (HMGCR) enzyme, plasma cholesterol levels and age. Gene variants and cognate phenotypic variables showed differential degrees of relevance to brain aging and dementia. This data analysis method was compared with another mathematical model called mutual information relevance network and results are presented and discussed.     

Terminology model discovery using natural language processing and visualization techniques

Medical terminologies are important for unambiguous encoding and exchange of clinical information. The traditional manual method of developing terminology models is time-consuming and limited in the number of phrases that a human developer can examine. In this paper, we present an automated method for developing medical terminology models based on natural language processing (NLP) and information visualization techniques. Surgical pathology reports were selected as the testing corpus for developing a pathology procedure terminology model. The use of a general NLP processor for the medical domain, MedLEE, provides an automated method for acquiring semantic structures from a free text corpus and sheds light on a new high-throughput method of medical terminology model development. The use of an information visualization technique supports the summarization and visualization of the large quantity of semantic structures generated from medical documents. We believe that a general method based on NLP and information visualization will facilitate the modeling of medical terminologies.     

An ordered comparative map of the cattle and human genomes

A cattle-human whole-genome comparative map was constructed using parallel radiation hybrid (RH) mapping in conjunction with EST sequencing, database mining for unmapped cattle genes, and a predictive bioinformatics approach (COMPASS) for targeting specific homologous regions. A total of 768 genes were placed on the RH map in addition to 319 microsatellites used as anchor markers. Of these, 638 had human orthologs with mapping data, thus permitting construction of an ordered comparative map. The large number of ordered loci revealed > or =105 conserved segments between the two genomes. The comparative map suggests that 41 translocation events, a minimum of 54 internal rearrangements, and repositioning of all but one centromere can account for the observed organizations of the cattle and human genomes. In addition, the COMPASS in silico mapping tool was shown to be 95% accurate in its ability to predict cattle chromosome location from random sequence data, demonstrating this tool to be valuable for efficient targeting of specific regions for detailed mapping. The comparative map generated will be a cornerstone for elucidating mammalian chromosome phylogeny and the identification of genes of agricultural importance."Ought we, for instance, to begin by discussing each separate species-in virtue of some common element of their nature, and proceed from this as a basis for the consideration of them separately?" from Aristotle, On the Parts of Animals, 350 B.C.E.     

Sequential patterns mining and gene sequence visualization to discover novelty from microarray data

Data mining allow users to discover novelty in huge amounts of data. Frequent pattern methods have proved to be efficient, but the extracted patterns are often too numerous and thus difficult to analyze by end users. In this paper, we focus on sequential pattern mining and propose a new visualization system to help end users analyze the extracted knowledge and to highlight novelty according to databases of referenced biological documents. Our system is based on three visualization techniques: clouds, solar systems, and treemaps. We show that these techniques are very helpful for identifying associations and hierarchical relationships between patterns among related documents. Sequential patterns extracted from gene data using our system were successfully evaluated by two biology laboratories working on Alzheimer’s disease and cancer.     

基于组织分割的医学体数据剖切方法

通过对医学体数据进行剖切,在方便观察其内部组织结构的同时,保留周围潜在的重要环境信息,可提供一个整体的情景图像。提出一种基于组织分割的体数据剖切方法,该方法关注于构成体数据的语义层,从医学诊断中的现实需求出发,以逻辑拆分来剖切体数据,以提供更加完整、直观的显示。将梯度矢量扩散法扩展到三维情形,同时提出三维梯度矢量扩散的体数据分割方法。在剖切方法中,首先运用三维梯度矢量扩散法来立体分割医学体数据中的组织,然后通过不同的空间传输函数来操作不同的组织,实现体数据的三维空间剖切体操作。实验结果表明,该方法能够有效去除遮挡数据以揭露内部信息,能保留相关环境信息来增加剖切的可理解性,可取得良好的虚拟剖切效果。