基于结构的虚拟筛选技术能更广泛应用于现代药物发现?

总结了计算机辅助药物设计目前的状况,重点讨论了基于结构类的虚拟筛选方法,特别是分子对接方法。在药物发现过程中,这些方法除了在早期的从数据库到命中阶段可节省费用外,还能提供其他有用信息吗?本综述试图通过探究大量现有对接方法的优缺点来回答这一问题。结果表明:基于结构的药物设计还没有实现其早期的所有目标,还需要广泛深入地进行研究,应用时也需谨慎。令人兴奋的是当与其他互补的药物设计途径,如基于配体的方法,相结合时基于结构的方法是最好的。从这一点上看,基于结构的药物设计方法在现代药物发现这一多学科的交叉领域中还应该发挥更多的作用。     

基于多方位、递进式的数据库实验教学研究

针对数据库系统课程实验目前存在的实验项目类型相对单一、未充分考虑学生能力的培养规律等问题,提出基于多方位、递进式的实验教学模式,介绍总体设计思路并分别阐述面向应用、面向底层、面向前沿的实验项目设计以及考核方法,最后通过实践应用总结,说明基于多方位、递进式的数据库实验教学模式对于教学质量的改善具有积极促进作用。     

面向计算机专业的递进式综合实验教学方案

提出一种面向计算机专业的递进式综合实验教学方案,阐述该实验方案设计思路和具体内容,提出递进式综合实验实现的平台。     

一种基于算法骨架的函数式程序变换技术

如何提高函数式程序设计语言在传统冯·诺依曼机器上的执行速度.及效率,一直是该领域中研究的主要论题,对此,并行图归约技术、并行闭包归约、并行编译、并行程序转换等等技术相继成为改善这种状况的措施。     

预制卡装式线圈骨架的制作

电子制作中常要用到各种各样的变压器、线圈,绕制线圈要用骨架。市售塑料骨架不但难买到合适的规格,而且一般塑料不耐温、易变形、不阻燃,机械强度也欠佳。要是能利用身边的废旧板料及板材边角料进行自制,那就方便多了。五十、六十年代,德国、比利时等欧洲国家仪器中的变压器、线圈都是绕在精致漂亮的预制卡装式骨架上,这些骨架均是用于层压电胶板做的,性能指标相当高,外观上也给电路增了不少“色”。下面就这种骨架谈谈制作方法。     

基于目标递进的Web服务组合方法

单个Web服务难以满足实际应用需求,如何组合已有服务形成新服务成为该领域的研究热点.现有的组合方法仅依靠输入和输出参数组合相关服务,很少考虑前提和后置条件的语义相关性.为此,提出一种基于目标递进的Web服务组合方法,采用前向链的搜索策略,建立基于条件的有向无环图,可有效利用输入、输出参数和前提、后置条件确定服务组合顺序...     

基于递进式特征增强聚合的伪装目标检测

伪装目标检测（COD）旨在检测隐藏在复杂环境中的目标。现有COD算法在结合多层次特征时,忽略了特征的表达和融合方式对检测性能的影响。为此,提出一种基于递进式特征增强聚合的COD算法。首先,通过主干网络提取多级特征;然后,为了提高特征的表达能力,使用由特征增强模块（FEM）构成的增强网络对多层次特征进行增强;最后,在聚合网络中设计邻近聚合模块（AAM）实现相邻特征之间的信息融合,以突显伪装目标区域的特征,并提出新的递进式聚合策略（PAS）通过渐进的方式聚合邻近特征,从而在实现多层特征有效融合的同时抑制噪声。在3个公开数据集上的实验表明,所提算法相较于12种最先进的算法在4个客观评价指标上均取得最优表现,尤其是在COD10K数据集上所提算法的加权的F测评法和平均绝对误差（MAE）分别达到了0.809和0.037。由此可见,所提算法在COD任务上拥有较优的性能。     

基于2D模型的药物小分子筛选方法

基于片段的药物设计(Fragment-Based Drug Design,FBDD)是药物研发的主流方法之一.如何高效从海量药物大数据中筛选出具有相似分子片段的药物小分子成为生物化学研究领域的挑战性问题.针对目前人工筛选耗时长、效率低、药物筛选周期长等问题,提出一种基于2D模型的药物小分子筛选方法(SMS-2D).利用...     

“操作系统”迭代递进式教学方案的探索

针对操作系统这一专业核心课程,探讨了一种新的课程教学方案,按照知识引入、拓展和综合提升的顺序迭代递进,以学生中心,以产出为导向,紧密围绕理论基础、编程实践、理论习题、科研和产业训练有序推进。试行后的课堂调查显示,新方案取得了良好的教学效果。     

基于三支决策的KNN渐进式文本分类方法

在传统的文本分类中,KNN算法以其简单、分类准确率高、非参数得到了广泛的应用。但是传统KNN算法在进行文本分类的过程中,需要计算待分类文本与每一个训练样本的相似度,当面对海量的文本时,分类的效果会明显降低。针对此问题,提出了一种基于三支决策的KNN渐进式文本分类方法用于提高其分类效率,结合三支决策在分类问题中的优势,将三支决策与KNN算法相结合,对标题、摘要、关键词等进行渐进式的分类处理,从而完成待分类文本的分类,提高文本分类的效率和性能。实验表明,该算法能够在确保KNN算法分类准确率的基础上,同时提高分类效率。     

无监督学习的无线胶囊内诊镜视频分类方法

近年来在对病人的消化道系统检查中,无线胶囊内诊镜(WCE)是一种最新技术,可以让医生直接观察到病人的病灶所在,但是对于消化道系统中的口腔、胃、小肠和大肠的WCE视频分类却是难点所在。相关研究中均采用通过人工标记的训练库的有监督学习方法。为了在WCE训练数据中获得高识别率,提出一种无监督学习方法,它利用融合颜色信息的尺度不变特征转换(SIFT)获取局部特征,再利用概率隐语义分析模型(pLSA)数据训练中进行语义内容分析。实验结果表明,在WCE图像分类中本方法与当前最新的监督分类方法一样可以获得高准确率。     

An expert model for self-care problems classification using probabilistic neural network and feature selection approach

Self-care problems classification is one of the important challenges for occupational therapists. Extent and variety of disorders make the self-care problems classification process complex and time-consuming. To overcome this challenge, an expert model is proposed innovatively in this research. The proposed model is based on Probabilistic Neural Network (PNN) and Genetic Algorithm (GA) for classifying self-care problems of children with physical and motor disability. In this model, PNN is employed as a classifier and GA is applied for feature selection. The PNN is trained by using a standard ICF-CY dataset. Based on ICF-CY, occupational therapists must evaluate many features to diagnose self-care problems. According to the experiences of occupational therapists, these features have different effects on classification. Hence, GA is employed to select relevant and important features in self-care problems classification. Since the classification rules are important for occupational therapists, the self-care problems classification rules are extracted additionally by using the CART algorithm. The experimental results show that by using the feature selection algorithm, the accuracy and time complexity of classification are improved in comparison to other models. The proposed model can classify self-care problems of children with 94.28% accuracy by using only 16.5% of all features.     

An integrated approach for structural damage identification using wavelet neuro-fuzzy model

Structural damage can be identified by processing structural vibration response signals and excitation data, and thus the suitability of signal processing methods is essential to structural damage identification. To explore an intelligent signal processing method for structural damage identification, the paper integrated wavelet real-time filtering algorithm, Adaptive Neruo-Fuzzy Inference System (ANFIS) and interval modeling technique to process structural response signals and excitation data. With Wavelet Transform (WT) algorithm filtering random noise, ANFIS was found to model the structural behavior properly and interval modeling technique to quantify damage index accurately. The rapid identifications of several unknown damages and small damages indicate the efficiency of this integrated method. The comparison of these results and some other signal processing methods shows that, the proposed method can be used to identify both the time and the location when the structural damage occurs unexpectedly.     

Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG

The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. An efficient common model structure selection (CMSS) algorithm is proposed to select a common model structure, with application to EEG data modelling. The time-varying parameters for the identified common-structured model are then estimated using a sliding-window recursive least squares (SWRLS) approach. The new method can effectively detect and adaptively track and rapidly capture the transient variation of nonstationary signals, and can also produce robust models with better generalisation properties. Two examples are presented to demonstrate the effectiveness and applicability of the new approach including an application to EEG data.