基于LDA主题模型的功能性miRNA-mRNA调控模块识别

借助mRNAs分析MicroRNAs(miRNAs)的研究已经用于阐述miRNAs调控机理,但是它们大部分的准确功能仍然处于未知状态。基于此,本文提出了一种基于LDA(Latent Dirichlet allocation)主题模型来识别特定生物条件下miRNAs和靶标mRNAs之间的调控模块。该模型首先利用Welch′s t-检验挖掘具有差异表达的miRNAs和mRNAs,然后采用折叠Gibbs抽样法进行参数估计。在上皮细胞-间充质细胞转型(Epithelial to Mesenchymal transition,EMT)数据集中的结果表明,所识别出的功能性miRNA-mRNA调控模块(FMRMs)能够构造不同生物条件下miRNAs与mRNAs之间的调控关系,从而为了解EMT生物过程和miRNA靶标治疗提供新的视角。与基于K-means聚类算法比较,LDA主题模型比K-means聚类在挖掘FMRMs上更加有效。     

拓扑推断乳腺癌的活性miRNA介导的子通路

笔者获取了人类通路数据、TCGA(The Cancer Genome Atlas)中乳腺癌表达数据、mirTarBase和miRBase等数据库中用于实验验证的miRNA-mRNA靶向关系数据,通过整合生物网络的拓扑信息、miRNAs和基因表达的差异性以及miRNAs和基因的靶向互做关系,计算出每个miRNA介导的自通路的活性值,再从中筛选出活性较高的自通路作为癌症分子诊断的标志物,进而提出一种算法miDRW。实验证明,乳腺癌的数据内5倍交叉验证的AUC值可达0.994 8,准确率达98.86%,明显高于单基因和单miRNA标志物的分类指标。可见,笔者提出的整合算法可以精确识别乳腺癌的诊断标志,并实现精确分类。     

基于动态网络切分的关键蛋白质预测方法

关键蛋白质作为蛋白质中的关键物质,不仅对研究细胞生长调控有着重要意义,也为更深层次的疾病研究奠定理论基础.目前,针对关键蛋白质的识别方法大多为应用基因表达信息和蛋白质相互作用网络,提出识别关键蛋白质的静态和动态网络方法,但这些方法未考虑基因表达调控的周期性规律,无法准确地刻画受基因周期调控的蛋白质网络.为此,在基因表达动态性的基础上引入了基因周期性表达的概念,提出了一种动态网络切分方法.该方法通过构建基因“活性”表达矩阵,利用切分后的“活性”表达矩阵作用于蛋白质相互作用网络,从而形成蛋白质周期子网络,最终综合各周期子网络来衡量蛋白质结点在网络中的重要性.实验结果表明,该方法在酵母、大肠杆菌和人类膀胱数据中可以有效地提高关键蛋白质预测率.     

转录调控网络模块和模体识别算法研究进展

转录调控网络是生物体遗传信息传递的整体表示,是人们理解基因表达过程的重要内容。识别转录调控网络的模块和模体是分析网络拓扑结构和组织方式的重要方法,是揭示转录调控机制、生物发育与进化过程的重要环节之一。通过分析比较近年来用于转录调控网络模块识别的三类典型算法,阐述了它们各自的优势和不足。介绍了一种被广泛使用的转录调控网络模体识别算法。以此为基础,提出了转录调控网络模块和模体识别算法未来的研究方向。     

SegHMC:一种基于Segmental HMM模型的顺式调控模块识别算法

顺式调控模块（Cis-regulatory module,CRM）在真核生物基因的转录调控中起着重要作用,识别顺式调控模块是当前计算生物学的一个重要课题.虽然当前有许多计算方法用于识别顺式调控模块,但识别准确率仍有待进一步提高.将顺式调控模块的多种特征信息结合在一起,有助于提高识别顺式调控模块的准确率.基于此,本文提出了一种识别顺式调控模块的算法SegHMC（Segmental HMM model for discovery of cis-regulatory module）.该算法建立了一种关于顺式调控模块识别问题的Segmental HMM模型,进一步扩展了顺式调控模块调控结构（或调控语法）的表示,不仅将顺式调控模块表示为模体（Motif）的组合,还进一步将模体共同出现的频率、模体顺序偏好以及顺式调控模块中相邻模体间的距离分布等特征引入到顺式调控模块的调控语法中.在模拟数据集和真实生物数据集上的实验结果表明,本文方法识别顺式调控模块的准确率显著优于当前的主要方法.     

基于结构模型的DBP-DNA识别及预测研究综述

DNA结合蛋白DBP(DNA Binding Protein)对细胞内遗传物质的生命活动中起着至关重要的作用,包括染色体的复制、剪切及基因表达调控.DBP通过与DNA发生结合反应,行使各种不同的生物功能.因此研究DBP-DNA之间的识别规律,对基因调控分析、蛋白功能预测和转录因子结合位点预测有着十分重要的意义.随着DBP-DNA复合物结构数据库的出现,基于结构信息的计算机分析方法为DBP与DNA之间的相互作用研究提供了新的发展契机.介绍了若干经典的DBP-DNA结构模型,简要评述了DBP-DNA结构分析方法目前的研究现状.     

使用稳态系统和粒群优化算法进行基因调控网络推断

基因调控网络模型试图从海量的时序基因表达数据中研究基因的功能,推断基因之间的调控关系,从而揭示复杂的病理现象和生命现象.通过利用时序基因表达数据来推断一个基于稳态系统(S-system)模型的基因网络,提出使用粒群优化算法(PSO)来优化模型参数,从而捕捉基因表达数据中的动力学特性.实验结果表明,该方法能够使模型参数快速得到收敛,配置参数后模型仿真能力好,可以较好地识别基因调控关系.     

microRNA计算识别中的模式识别技术

MicroRNAa(miRNAs)是一种大小约21-23个碱基的单链RNA小分子,对多种生物学过程起调控作用,它们主要参与基因转录后水平的调控,能有效地抑制相关蛋白质的合成,与生物体的生长发育和某些疾病的发生密切相关.对mi-croRNAa(miRNAs)的研究正在不断增加,计算识别为分子生物学实验寻找新microRNA提供一组高质量的候选序列.文中从模式识别的角度审视现有的计算识别技术,分析和比较各种方法的特点后发现基于支持向量机的识别方法已经能在识别精度上得到很好的效果,这也是microRNA识别技术将来发展的主要方向.     

真核生物启动子的预测技术

启动子是基因表达过程中非常重要的调控序列,是影响基因能否转录的重要功能单位之一,真核生物的启动子预测已经成为生物信息学研究的热点.将结合人工神经网络、支持向量机、二次判别分析和位置权值矩阵技术,对国内外真核生物启动子的预测研究进行介绍,并在最后采用合适的技术应用到模拟真核生物基因表达过程的电子细胞模型Analog-Cell中.     

基因表达谱数据分析技术

人类基因组计划的研究已进入后基因组时代,后基因组时代研究的焦点已经从测序转向功能研究,主要采用无监督和有监督技术来分析基因表达谱和识别基因功能,通过基因转录调控网络分析细胞内基因之间的相互作用关系的整体表示,说明生命功能在基因表达层面的展现,对目前基因表达谱数据分析技术及它们的发展,进行了综述性的研究,分析了它们的优缺点,提出了解决问题的思路和方法,为基因表达谱的进一步研究提供了新的途径。     

计算方法预测microRNA研究进展

microRNA(miRNA)是一类内源性、长度为19-24个碱基的非编码小分子RNA。它在调控动植物的基因表达、生长发育等方面起着重要的作用。现阶段寻找miRNA的方法主要分为实验方法和计算预测两大类,其中,实验的方法很难测定表达量偏低或者特异表达的miRNA,也不适合进行基因组范围的发现。而计算预测的方法则恰好可以弥补这些不足。总结近几年计算预测miRNA的方法,归纳为三类,分别是基于同源性比较的方法、基于机器学习的方法和基于高通量测序的方法。最后对miRNA计算预测方法未来的发展方向作出了探讨。     

Incorporating multiple genomic features with the utilization of interacting domain patterns to improve the prediction of protein-protein interactions

Protein-protein interaction (PPI) networks play an outstanding role in the organization of life. Parallel to the growth of experimental techniques on determining PPIs, the emergence of computational methods has greatly accelerated the time needed for the identification of PPIs on a wide genomic scale. Although experimental approaches have limitations that can be complemented by the computational methods, the results from computational methods still suffer from high false positive rates which contribute to the lack of solid PPI information. Our study introduces the PPI-Filter; a computational framework aimed at improving PPI prediction results. It is a post-prediction process which involves filtration, using information based on three different genomic features; (i) gene ontology annotation (GOA), (ii) homologous interactions and (iii) protein families (PFAM) domain interactions. In the study, we incorporated a protein function prediction method, based on interacting domain patterns, the protein function predictor or PFP (), for the purpose of aiding the GOA. The goal is to improve the robustness of predicted PPI pairs by removing the false positive pairs and sustaining as much true positive pairs as possible, thus achieving a high confidence level of PPI datasets. The PPI-Filter has been proven to be applicable based on the satisfactory results obtained using signal-to-noise ratio (SNR) and strength measurements that were applied on different computational PPI prediction methods.     

基于因果关系挖掘的概率基因调控网络的构建

科学的基因聚类方法是构建基因调控网络的前提,但仅以聚类作为构建网络的主要手段只能找到共同调控的基因,不能精确反映基因之间的相互作用过程。贝叶斯网络模型通过基于图的方式求得多变量之间条件独立的概率因果关系,但因其计算复杂性受到应用层面的限制。该文综合考虑几方面因素,在对基因进行聚类基础上,通过对调控关系的预测获得对目标基因的调控基因组,再利用LCD(local causal relation discovery)方法通过限制搜索条件发现基因间的独立关系,进而获得基因调控网络。实验结果表明了该方法的可行性和有效性。     

Identification of polynomial input/output recursive models with simulation error minimisation methods

Polynomial input/output (I/O) recursive models are widely used in nonlinear model identification for their flexibility and representation capabilities. Several identification algorithms are available in the literature, which deal with both model selection and parameter estimation. Previous works have shown the limitations of the classical prediction error minimisation approach in this context, especially (but not only) when the disturbance contribution is unknown, and suggested the use of a simulation error minimisation (SEM) approach for a better selection of the I/O model. This article goes a step further by integrating the model selection procedure with a simulation-oriented parameter estimation algorithm. Notwithstanding the algorithmic and computational complexity of the proposed method, it is shown that it can sometimes achieve great performance improvements with respect to previously proposed approaches. Two different parameter estimation algorithms are suggested, namely a direct SEM optimisation algorithm, and an approximate method based on multi-step prediction iteration, which displays several convenient properties from the computational point of view. Several simulation examples are shown to demonstrate the effectiveness of the suggested SEM approaches.     

Promoter analysis of co-regulated genes in the yeast genome.

The use of high density DNA arrays to monitor gene expression at a genome-wide scale constitutes a fundamental advance in biology. In particular, the expression pattern of all genes in Saccharomyces cerevisiae can be interrogated using microarray analysis where cDNAs are hybridized to an array of more than 6000 genes in the yeast genome. In an effort to build a comprehensive Yeast Promoter Database and to develop new computational methods for mapping upstream regulatory elements, we started recently in an on going collaboration with experimental biologists on analysis of large-scale expression data. It is well known that complex gene expression patterns result from dynamic interacting networks of genes in the genetic regulatory circuitry. Hierarchical and modular organization of regulatory DNA sequence elements are important information for our understanding of combinatorial control of gene expression. As a bioinformatics attempt in this new direction, we have done some computational exploration of various initial experimental data. We will use cell-cycle regulated gene expression as a specific example to demonstrate how one may extract promoter information computationally from such genome-wide screening. Full report of the experiments and of the complete analysis will be published elsewhere when all the experiments are to be finished later in this year (Spellman, P.T., et al. 1998. Mol. Biol. Cell 9, 3273-3297).     

Kernel methods in system identification,machine learning and function estimation: A survey

Most of the currently used techniques for linear system identification are based on classical estimation paradigms coming from mathematical statistics. In particular, maximum likelihood and prediction error methods represent the mainstream approaches to identification of linear dynamic systems, with a long history of theoretical and algorithmic contributions. Parallel to this, in the machine learning community alternative techniques have been developed. Until recently, there has been little contact between these two worlds. The first aim of this survey is to make accessible to the control community the key mathematical tools and concepts as well as the computational aspects underpinning these learning techniques. In particular, we focus on kernel-based regularization and its connections with reproducing kernel Hilbert spaces and Bayesian estimation of Gaussian processes. The second aim is to demonstrate that learning techniques tailored to the specific features of dynamic systems may outperform conventional parametric approaches for identification of stable linear systems.     

Interactive visual analysis of time-series microarray data

Estimating dynamic regulatory pathways using DNA microarray time-series can provide invaluable information about the dynamic interactions among genes and result in new methods of rational drug design. Even though several purely computational methods have been introduced for DNA pathway analysis, most of these techniques do not provide a fully interactive method to explore and analyze these dynamic interactions in detail, which is necessary to obtain a full understanding. In this paper, we present a unified modeling and visual approach focusing on visual analysis of gene regulatory pathways over time. As a preliminary step in analyzing the gene interactions, the method applies two different techniques, a clustering algorithm and an auto regressive (AR) model. This approach provides a successful prediction of the dynamic pathways involved in the biological process under study. At this level, these pure computational techniques lack the transparency required for analysis and understanding of the gene interactions. To overcome the limitations, we have designed a visual analysis method that applies several visualization techniques, including pixel-based gene representation, animation, and multi-dimensional scaling (MDS), in a new way. This visual analysis framework allows the user to quickly and thoroughly search for and find the dynamic interactions among genes, highlight interesting gene information, show the detailed annotations of the selected genes, compare regulatory behaviors for different genes, and support gene sequence analysis for the interesting genes. In order to enhance these analysis capabilities, several methods are enabled, providing a simple graph display, a pixel-based gene visualization technique, and a relation-displaying technique among gene expressions and gene regulatory pathways.