An Improved Whale Algorithm and Its Application in Truss Optimization

The current Whale Optimization Algorithm(WOA)has several drawbacks,such as slow convergence,low solution accuracy and easy to fall into the local optimal soluti...     

Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms.     

Using Hierarchical Clustering and Dendrograms to Quantify the Clustering of Membrane Proteins

Cell biologists have developed methods to label membrane proteins with gold nanoparticles and then extract spatial point patterns of the gold particles from transmission electron microscopy images using image processing software. Previously, the resulting patterns were analyzed using the Hopkins statistic, which distinguishes nonclustered from modestly and highly clustered distributions, but is not designed to quantify the number or sizes of the clusters. Clusters were defined by the partitional clustering approach which required the choice of a distance. Two points from a pattern were put in the same cluster if they were closer than this distance. In this study, we present a new methodology based on hierarchical clustering to quantify clustering. An intrinsic distance is computed, which is the distance that produces the maximum number of clusters in the biological data, eliminating the need to choose a distance. To quantify the extent of clustering, we compare the clustering distance between the experimental data being analyzed with that from simulated random data. Results are then expressed as a dimensionless number, the clustering ratio that facilitates the comparison of clustering between experiments. Replacing the chosen cluster distance by the intrinsic clustering distance emphasizes densely packed clusters that are likely more important to downstream signaling events.     

脉冲场凝胶电泳技术及其在真菌学研究中的应用

脉冲电泳是用于分离大分子量DNA的一种电泳技术,已广泛用于真菌的核型分析,种群特异性鉴定,基因定位及遗传分析的研究。介绍了脉冲电泳的原理,发展和基本操作程序,并阐述了脉冲电泳技术在真菌分子生物学研究领域的应用。     

改进的K—mean聚类算法在基因系统发育谱分析中的应用

简要介绍了系统发育谱法的原理,着重阐述了K—mean聚类算法在对基因系统发育谱分析中的改进,并与传统的K—mean聚类算法进行比较。实验结果表明,改进的K—mean聚类算法在运用系统发育谱法进行基因功能注释上是快而有效的,可以快速收敛到近似最优解。     

Taq DNA聚合酶结构改造与功能改进及其在医药领域的应用前景

TaqDNA聚合酶（ThermusaquaticusDNApolymerase）是重要的生物技术工具酶,广泛应用于疾病诊断与治疗、传染病检测、药物作用机理等医药领域。TaqDNA聚合酶在结构上由三部分构成：DNA聚合酶区、3’核酸外切酶区和5’核酸外切酶区。由于3’核酸外切酶区的氨基酸序列的变化而使其失去了3’-5＇的外切校读功能,因此TaqDNA聚合酶有错误复制倾向。利用特定位点的突变、与其他酶互换功能区和删除N末端的5’核酸外切酶区等方式可以改进TaqDNA聚合酶的性能,使它的忠实性、热稳定性和聚合性能得到提高。本文介绍了TaqDNA聚合酶的结构与功能改造的研究现状,并展望了TaqDNA聚合酶在医药领域的应用前景。     

一种启动子克隆的改良Adaptor-PCR方法及在橡胶树上的应用实例

真核生物通过顺式作用元件和反式作用因子相互作用实现基因的转录调控,而启动子区域含有多种顺式元件,作为基因表达调控网络的枢纽控制着基因转录的起始与效率,一直是基因表达调控研究的重点[1]。本文提供了一种改良的基于Adaptor-PCR启动子克隆方法,改进了接头序列,设计了适合两步法PCR的接头引物。选取了25种限制性内切酶对橡胶树基因组DNA进行酶切,在所有酶切产物都平端化后,与改进的接头连接,成功构建了以Adaptor-PCR为基础的橡胶树基因启动子克隆文库,并利用此文库成功克隆了橡胶树6个蔗糖转运蛋白基因、1个转化酶基因和1个海藻糖合酶基因的启动子。本文研究结果为橡胶树基因启动子克隆提供了一个高效平台,也为其他生物基因启动子克隆提供了有益的参考。     

Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images

We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.     

SHIPS: Spectral Hierarchical Clustering for the Inference of Population Structure in Genetic Studies

Inferring the structure of populations has many applications for genetic research. In addition to providing information for evolutionary studies, it can be used to account for the bias induced by population stratification in association studies. To this end, many algorithms have been proposed to cluster individuals into genetically homogeneous sub-populations. The parametric algorithms, such as Structure, are very popular but their underlying complexity and their high computational cost led to the development of faster parametric alternatives such as Admixture. Alternatives to these methods are the non-parametric approaches. Among this category, AWclust has proven efficient but fails to properly identify population structure for complex datasets. We present in this article a new clustering algorithm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS), based on a divisive hierarchical clustering strategy, allowing a progressive investigation of population structure. This method takes genetic data as input to cluster individuals into homogeneous sub-populations and with the use of the gap statistic estimates the optimal number of such sub-populations. SHIPS was applied to a set of simulated discrete and admixed datasets and to real SNP datasets, that are data from the HapMap and Pan-Asian SNP consortium. The programs Structure, Admixture, AWclust and PCAclust were also investigated in a comparison study. SHIPS and the parametric approach Structure were the most accurate when applied to simulated datasets both in terms of individual assignments and estimation of the correct number of clusters. The analysis of the results on the real datasets highlighted that the clusterings of SHIPS were the more consistent with the population labels or those produced by the Admixture program. The performances of SHIPS when applied to SNP data, along with its relatively low computational cost and its ease of use make this method a promising solution to infer fine-scale genetic patterns.     

Applying of Hierarchical Clustering to Analysis of Protein Patterns in the Human Cancer-Associated Liver

Background

There are two ways that statistical methods can learn from biomedical data. One way is to learn classifiers to identify diseases and to predict outcomes using the training dataset with established diagnosis for each sample. When the training dataset is not available the task can be to mine for presence of meaningful groups (clusters) of samples and to explore underlying data structure (unsupervised learning).

Results

We investigated the proteomic profiles of the cytosolic fraction of human liver samples using two-dimensional electrophoresis (2DE). Samples were resected upon surgical treatment of hepatic metastases in colorectal cancer. Unsupervised hierarchical clustering of 2DE gel images (n = 18) revealed a pair of clusters, containing 11 and 7 samples. Previously we used the same specimens to measure biochemical profiles based on cytochrome P450-dependent enzymatic activities and also found that samples were clearly divided into two well-separated groups by cluster analysis. It turned out that groups by enzyme activity almost perfectly match to the groups identified from proteomic data. Of the 271 reproducible spots on our 2DE gels, we selected 15 to distinguish the human liver cytosolic clusters. Using MALDI-TOF peptide mass fingerprinting, we identified 12 proteins for the selected spots, including known cancer-associated species.

Conclusions/Significance

Our results highlight the importance of hierarchical cluster analysis of proteomic data, and showed concordance between results of biochemical and proteomic approaches. Grouping of the human liver samples and/or patients into differing clusters may provide insights into possible molecular mechanism of drug metabolism and creates a rationale for personalized treatment.     

Hierarchical Clustering of Breast Cancer Methylomes Revealed Differentially Methylated and Expressed Breast Cancer Genes

Oncogenic transformation of normal cells often involves epigenetic alterations, including histone modification and DNA methylation. We conducted whole-genome bisulfite sequencing to determine the DNA methylomes of normal breast, fibroadenoma, invasive ductal carcinomas and MCF7. The emergence, disappearance, expansion and contraction of kilobase-sized hypomethylated regions (HMRs) and the hypomethylation of the megabase-sized partially methylated domains (PMDs) are the major forms of methylation changes observed in breast tumor samples. Hierarchical clustering of HMR revealed tumor-specific hypermethylated clusters and differential methylated enhancers specific to normal or breast cancer cell lines. Joint analysis of gene expression and DNA methylation data of normal breast and breast cancer cells identified differentially methylated and expressed genes associated with breast and/or ovarian cancers in cancer-specific HMR clusters. Furthermore, aberrant patterns of X-chromosome inactivation (XCI) was found in breast cancer cell lines as well as breast tumor samples in the TCGA BRCA (breast invasive carcinoma) dataset. They were characterized with differentially hypermethylated XIST promoter, reduced expression of XIST, and over-expression of hypomethylated X-linked genes. High expressions of these genes were significantly associated with lower survival rates in breast cancer patients. Comprehensive analysis of the normal and breast tumor methylomes suggests selective targeting of DNA methylation changes during breast cancer progression. The weak causal relationship between DNA methylation and gene expression observed in this study is evident of more complex role of DNA methylation in the regulation of gene expression in human epigenetics that deserves further investigation.     

高压静电场在农业中的应用及其作用机理的物理微观解析

本文概括了近几十年来电场处理种子、分选种子、空间电场防病促生技术、电场果蔬保鲜的应用及其产生的电场生物学效应,同时阐述了电场生物效应作用机理及其研究现状.本文重点介绍了近年来一些物理学者对电场作用机理的物理微观解析一介质极化微观理论、一维自由谐振子能级理论、势垒贯穿理论等.指出了电场在农业中的应用非常广泛,但到目前为止对其作用机理还没有统一的理论解释.电场生物学效应作用机理的深入研究需要生物学者、化学学者和物理学者的密切合作.     

Gravitation field algorithm and its application in gene cluster

Background  

Searching optima is one of the most challenging tasks in clustering genes from available experimental data or given functions. SA, GA, PSO and other similar efficient global optimization methods are used by biotechnologists. All these algorithms are based on the imitation of natural phenomena.     

秸秆降解菌的筛选及模拟田间应用效果分析

以透明圈筛选法和滤纸崩解法相结合,建立初筛方法;模拟田间覆土菌种筛选方法,以秸秆腐解率为指标,建立了复筛方法,并进行了模拟田间应用效果分析。Y13、774、HX及J1 4株菌的腐解率较高,比对照分别提高27.04%、29.65%、35.38%和27.86%;其产CO2能力皆高于空白对照,分别提高41.67%、51.85%、30.56%和57.41%;其可溶性糖含量均提高,比对照平均提高15.40%。     

Parallel Clustering Algorithm for Large Data Sets with Applications in Bioinformatics

Large sets of bioinformatical data provide a challenge in time consumption while solving the cluster identification problem, and that is why a parallel algorithm is so needed for identifying dense clusters in a noisy background. Our algorithm works on a graph representation of the data set to be analyzed. It identifies clusters through the identification of densely intraconnected subgraphs. We have employed a minimum spanning tree (MST) representation of the graph and solve the cluster identification problem using this representation. The computational bottleneck of our algorithm is the construction of an MST of a graph, for which a parallel algorithm is employed. Our high-level strategy for the parallel MST construction algorithm is to first partition the graph, then construct MSTs for the partitioned subgraphs and auxiliary bipartite graphs based on the subgraphs, and finally merge these MSTs to derive an MST of the original graph. The computational results indicate that when running on 150 CPUs, our algorithm can solve a cluster identification problem on a data set with 1,000,000 data points almost 100 times faster than on single CPU, indicating that this program is capable of handling very large data clustering problems in an efficient manner. We have implemented the clustering algorithm as the software CLUMP.