A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae

Two large-scale yeast two-hybrid screens were undertaken to identify protein-protein interactions between full-length open reading frames predicted from the Saccharomyces cerevisiae genome sequence. In one approach, we constructed a protein array of about 6,000 yeast transformants, with each transformant expressing one of the open reading frames as a fusion to an activation domain. This array was screened by a simple and automated procedure for 192 yeast proteins, with positive responses identified by their positions in the array. In a second approach, we pooled cells expressing one of about 6,000 activation domain fusions to generate a library. We used a high-throughput screening procedure to screen nearly all of the 6,000 predicted yeast proteins, expressed as Gal4 DNA-binding domain fusion proteins, against the library, and characterized positives by sequence analysis. These approaches resulted in the detection of 957 putative interactions involving 1,004 S. cerevisiae proteins. These data reveal interactions that place functionally unclassified proteins in a biological context, interactions between proteins involved in the same biological function, and interactions that link biological functions together into larger cellular processes. The results of these screens are shown here.     

General conditions for predictivity in learning theory

Developing theoretical foundations for learning is a key step towards understanding intelligence. 'Learning from examples' is a paradigm in which systems (natural or artificial) learn a functional relationship from a training set of examples. Within this paradigm, a learning algorithm is a map from the space of training sets to the hypothesis space of possible functional solutions. A central question for the theory is to determine conditions under which a learning algorithm will generalize from its finite training set to novel examples. A milestone in learning theory was a characterization of conditions on the hypothesis space that ensure generalization for the natural class of empirical risk minimization (ERM) learning algorithms that are based on minimizing the error on the training set. Here we provide conditions for generalization in terms of a precise stability property of the learning process: when the training set is perturbed by deleting one example, the learned hypothesis does not change much. This stability property stipulates conditions on the learning map rather than on the hypothesis space, subsumes the classical theory for ERM algorithms, and is applicable to more general algorithms. The surprising connection between stability and predictivity has implications for the foundations of learning theory and for the design of novel algorithms, and provides insights into problems as diverse as language learning and inverse problems in physics and engineering.     

基于局部最大距离的孤立点检测算法的研究

孤立点检测是数据挖掘研究中的一项重要内容,其目标是发现数据集中行为异常的数据对象．本文在局部稀疏系数算法的基础上提出了基于局部最大距离的局部孤立点检测算法,该算法提出检测孤立点只需计算它的最近邻居对象的最大距离．实验结果表明,该算法发现局部孤立点是高效的。     

Predicting disease genes for familial dilated cardiomyopathy based on the codon usage bias

Familial dilated cardiomyopathy （FDC） is a common monogenic disease mostly with autosomal dominant inheritance. Fifteen different loci for autosomal dominant FDC have been mapped; however, only eight FDC genes have been found, and it is still a big challenge to identify additional seven FDC genes in their chromosomal regions. We found that the codon usage frequencies in most of known FDC gene sequences are consistently biased, and significantly different from the average codon usage frequencies of human genes. This unique feature of codon usage was used to develop a novel approach to predicting FDC genes. Leave-oneout cross-validation results demonstrate that this approach can effectively detect FDC genes from numbers of genes in their chromosomal regions. Another advantage of this approach is that it is solely based on DNA sequences and therefore has the ability to identify potential FDC genes whose functions are completely unknown. Further, this approach has been used to analyze the seven FDC loci in which the FDC genes are still unknown. Both the detailed prediction results and the prediction program are available at http：// infosci.hust.edu.cn, which might provide help for relevant experimental researches to find new FDC genes.     

A Heterogeneous Ensemble of Extreme Learning Machines with Correntropy and Negative Correlation

The Extreme Learning Machine(ELM) is an effective learning algorithm for a Single-Layer Feedforward Network(SLFN). It performs well in managing some problems due to its fast learning speed. However, in practical applications, its performance might be affected by the noise in the training data. To tackle the noise issue, we propose a novel heterogeneous ensemble of ELMs in this article. Specifically, the correntropy is used to achieve insensitive performance to outliers, while implementing Negative Correlation Learning(NCL) to enhance diversity among the ensemble. The proposed Heterogeneous Ensemble of ELMs(HE2 LM) for classification has different ELM algorithms including the Regularized ELM(RELM), the Kernel ELM(KELM), and the L2-norm-optimized ELM(ELML2). The ensemble is constructed by training a randomly selected ELM classifier on a subset of the training data selected through random resampling. Then, the class label of unseen data is predicted using a maximum weighted sum approach. After splitting the training data into subsets, the proposed HE2 LM is tested through classification and regression tasks on real-world benchmark datasets and synthetic datasets. Hence, the simulation results show that compared with other algorithms, our proposed method can achieve higher prediction accuracy, better generalization, and less sensitivity to outliers.     

竞技运动中功能性力量训练的训练理念及作用机制探析

通过文献资料法,阐述了功能性力量训练的概念、作用、训练理念及作用机制,探讨了功能性力量训练现有的训练手段。研究认为:(1)功能性力量训练以提高全身肌肉整体工作能力和效率为其训练目的;(2)功能性力量训练具有提高力量的传递、组合及控制的功能,对运动技术具有重要的支持及预防运动损伤的功能;(3)训练理念:强调躯干部位及各关节周围肌肉的训练,强调稳定与平衡及神经对肌肉的控制能力,强调自身的"柔性力量"作用,最终达到提高多块肌肉参加完成整体力量的目标;(4)作用机制:神经肌肉控制作用机制、"三亚系"模型作用机制。     

A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform

Objective： To develop a new bioinformatic tool based on a data-mining approach for extraction of the most informative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods： Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surfaceenhanced laser desorption/ionization time-of-flight mass spectrometry （SELDI-TOF MS）. The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality reduction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results： From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion： The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.     

功能性力量训练的实质及其训练方法探析——基于悬吊训练、振动力量训练、核心力量训练、本体感觉功能训练

通过文献资料法,阐述了功能性力量训练的概念、作用、训练理念及作用机制,探讨了功能性力量训练现有的训练手段。结论:(1)功能性力量训练以提高全身肌肉整体工作能力和效率为其训练目的;(2)功能性力量训练具有提高力量的传递、组合及控制的功能,对运动技术具有重要的支持及预防运动损伤的功能;(3)训练理念:强调躯干部位及各关节周围肌肉的训练,强调稳定与平衡及神经对肌肉的控制能力,强调自身的"柔性力量"作用,最终达到提高多块肌肉参加完成整体力量的目标;(4)作用机制:神经肌肉控制作用机制、"三亚系"模型作用机制;(5)已报道的与功能性力量训练有关的训练方法和手段主要包括:悬吊训练、振动力量训练、核心力量训练、本体感觉功能训练。     

基于邻域空间模式的运动相关电位特征提取方法

为解决脑-机接口(BCI)研究中所采集的脑电图(EEG)信号数据分布复杂和训练样本不足的问题,文中提出了一种新的特征提取方法--邻域空间模式(NSP)算法,用于提取BCI想象肢体运动分类算法中使用的重要分类特征--运动相关电位(MRPs).NSP算法不需要对样本的数据分布进行假设,主要利用样本的邻域关系和类别信息寻找最佳投影方向,使得映射后邻域内异类样本距离之和与同类样本距离之和的比值最大化.采用BCI竞赛2003和2001的其中两组数据进行实验,结果表明NSP算法能更有效地提取MRPs特征.     

A novel approach of hybrid genetic optimization with elite ant colony: Application in the modulation recognition

A novel approach (HGO-EAC) for hybrid genetic op-timization (GO) with elite ant colony (EAC) is proposed for the automatic modulation recognition of communication signals,through which we improve the basic ant colony algorithms by referencing elite strategy and present a new fusion strategy for genetic optimization and elite ant colony. This approach is used to train the neural networks as the classifier for modulation. Simula-tion results indicate good performance on an additive white Gaus-sian noise (AWGN) channel,with recognition rate reaching to 70% especially for CW even at signal-to-noise ratios as low as 5 dB. This approach can achieve a high recognition rate for the typical modulations such as CW,4ASK,4FSK,BPSK,and QAM16. Test result shows that it has better performance than BP algorithm and basic ant colony algorithms by achieving faster training and stronger robustness.     

用改进的遗传算法进行多流股换热器网络综合

建立了有分流情况下换热器网络综合的教学模型,该模型改进了文献中等温混合的不合理假设,有分流情况下的换热器网络综合问题本质上是一个混合整数非线性规划(MINLP)问题。这类问题的非凸非线性特性使得目标函数存在多个局部最优解,传统的基于梯度的搜索方法在处理这类问题时由于计算规模庞大且极易陷于局部最优解而不再适用,因此对遗传算法进行多流股换热器网络综合的方法进行了研究,最后通过例题证明了方法的有效性。     

一种新的蛋白质亚细胞定位预测训练集构造方法

设计了一种新的蛋白质亚细胞定位预测训练集构造方法.该方法针对传统预测方法缺乏足够的实验标记数据的问题,基于主动学习策略从非实验标记蛋白质数据中主动选择有效数据,并与原有的实验标记数据共同训练预测模型,以提高基准分类器的预测精度.结合支持向量机分类器,该方法在病毒蛋白质独立测试集上进行了预测实验,测试结果表明,该方法能够有效地提高基准分类器的预测能力,性能优于现有的病毒蛋白质预测系统.     

Discovering the cellular-localized functional modules and modular interactions in response to liver cancer

In this paper, we firstly identify the functional modules enriched with differentially expressed genes （DEGs） and characterized by biological processes in specific cellular locations, based on gene ontology （GO） and microarray data. Then, we further define and filter disease relevant signature modules according to the ranking of the disease discriminating abilities of the pre-selected functional modules. At last, we analyze the potential way by which they cooperate towards human disease. Application of the proposed method to the analysis of a liver cancer dataset shows that, using the same false discovery rate （ FDR ） threshold, we can find more biologically meaningful and detailed processes by using the cellular localization information. Some biological evidences support the relevancy of our biological modules to the disease mechanism.     

贫困地区农村中学体育教师的继续教育研究

通过对贫困地区农村中学体育教师的问卷调查,切实了解基层贫困农村中学体育教师的学历结构、年龄结构、师资培训情况,为实现我国科教兴国战略,构建一支高素质、高教学水平的师资队伍,提高教学质量,巩固教学效果,提高学生素质,提供详尽的资料．     

RSOM算法及其应用研究

神经网络以其优越的非线性拟合能力和强大的自组织模式分类能力已被用于许多模式识别问题,并取得了很好结果．但是对于大样本集分类和复杂模式识别问题,大多数常规神经网络在决定网络结构与规模、设计自学习算法和应付庞大的计算量等方面存在诸多困难．为了克服这些困难,在Kohonen自组织映射模型(SOM)的基础上,提出了两种基于类别可分性判据RSOM分类树：非结构自适应的RSOM-Ⅰ分类树与基于奇异值分解方法的结构自适应RSOM-Ⅱ分类树,这两种分类树的每个节点由拓扑有序的自组织映射网络组成．RSOM分类树的优点在于处理大样本集和复杂模式的识别问题时能够自适应地确定网络的结构和规模,最后的数据试验就是很好的佐证．     

基于选择性的贝叶斯分类器集成方法

提出一种基于选择性的贝叶斯分类器集成方法．该方法为避免数据预处理时的属性约简对分类效果的直接影响，在训练集上通过随机属性选取生成若干属性子集，并以这些子集训练不同的贝叶斯分类器，进而采用遗传算法优选贝叶斯分类器集成，其适应度函数综合了分类器的精度和差异度两项指标．实验中，将该方法与已有方法在UCI的标准数据集上进行了性能比较，并将该方法用于C^3I系统中的威胁度估计。     

A proteomic view of the Plasmodium falciparum life cycle

The completion of the Plasmodium falciparum clone 3D7 genome provides a basis on which to conduct comparative proteomics studies of this human pathogen. Here, we applied a high-throughput proteomics approach to identify new potential drug and vaccine targets and to better understand the biology of this complex protozoan parasite. We characterized four stages of the parasite life cycle (sporozoites, merozoites, trophozoites and gametocytes) by multidimensional protein identification technology. Functional profiling of over 2,400 proteins agreed with the physiology of each stage. Unexpectedly, the antigenically variant proteins of var and rif genes, defined as molecules on the surface of infected erythrocytes, were also largely expressed in sporozoites. The detection of chromosomal clusters encoding co-expressed proteins suggested a potential mechanism for controlling gene expression.     

Genome-wide survey of protein kinases required for cell cycle progression

Cycles of protein phosphorylation are fundamental in regulating the progression of the eukaryotic cell through its division cycle. Here we test the complement of Drosophila protein kinases (kinome) for cell cycle functions after gene silencing by RNA-mediated interference. We observed cell cycle dysfunction upon downregulation of 80 out of 228 protein kinases, including most kinases that are known to regulate the division cycle. We find new enzymes with cell cycle functions; some of these have family members already known to phosphorylate microtubules, actin or their associated proteins. Additionally, depletion of several signalling kinases leads to specific mitotic aberrations, suggesting novel roles for familiar enzymes. The survey reveals the inter-digitation of systems that monitor cellular physiology, cell size, cellular stress and signalling processes with the basic cell cycle regulatory machinery.