基于关联规则映射的生物信息网络多维数据挖掘算法

针对在生物信息网络中对复杂和大规模的数据集进行挖掘时所出现的算法挖掘精度低、运行速度慢、内存占用大等问题,提出一种基于关联规则映射的生物信息网络多维数据挖掘算法。该算法结合网络数据集之间的关联映射关系,从而确定网络数据集的关联规则,并引入挖掘因子和相对误差来提高算法的挖掘精度;根据多维子空间中数据集之间的关联程度进行子空间区分以及子空间内数据集区分,从而实现对不同数据集的有效挖掘。在实验中,对不同数据集数量下的算法内存占用情况、算法挖掘精度、算法运行时间进行仿真,从实验结果可以看出基于关联规则映射的挖掘算法可以有效地提高挖掘精度,在减少内存占用和提升计算速度上也具有一定的优势。

生命科学的进展与生物信息网络

近代生命科学迅猛发展,特别是作为生命科学的前沿——分子生物学的发展,已从根本上改变了它在自然科学中的地位和作用.有识之士都公认生命科学将在21世纪发挥推动科学进步的关键作用,21世纪也将成为公认的“生命科学世纪”.因此,加强发展以生命科学为核心的科技情报信息研究和服务,已成为推动科学技术发展,实施“科教兴国”的先导.最近,在剑桥成立的“欧洲生物信息研究所”(EU- ropean Bioinformatics Institute,EBT),是第一个跨国信息研究所,是欧洲分子生物学实验室数据中心的延伸.该研究所与环球网(World Wide Web,WWW)

基于关联规则映射的生物信息网络多维数据挖掘算法分析

要想对生物信息网络数据进行大范围的挖掘,所应用到的算法有很多不足之处,比如,精确度低,运行速度迟缓以及占内存等,基于这一背景,文章提出了一种能够对生物信息进行映射并关联的数据挖掘算法,这种算法不仅能够映射关联,确定网络数据集,还能够基于算法引入相对误差,使算法的精确性提高。通过构建数据集间的关联,能够对空间内的数据进行区分,达到更好的数据挖掘效果。

GSDS:基因结构显示系统

构建了一个用于绘制基因结构示意图的网站系统(http://gsds.cbi.pku.edu.cn/)。用户可提交核酸序列、NCBI核酸序列号或基因外显子位置信息,得到基因结构示意图;并可指定在基因结构图上标注某些特定区域。系统允许用户同时输入多个基因,并指定输出次序和标注区域。结果可用位图和矢量图两种图形格式显示。点击位图格式结果,可以查看相应序列。系统提供中英文两种用户界面。

A five-fingered underactuated prosthetic hand:hardware and its control scheme

A five-fingered underactuated prosthetic hand controlled by surface EMG (electromyographic) signals is presented in this paper. The prosthetic hand was designed with simplicity, lightweight and dexterity on the requirement of anthropomorphic hands. Underactuated self-adaptive theory was adopted to decrease the number of motors and weight. The control part of the prosthetic hand was based on a surface EMG motion pattern classifier which combines LM-based (Levenberg-Marquardt) neural network with the parametric AR (autoregressive) model. This motion pattern classifier can successfully identify the flexions of the thumb, the index finger and the middle finger by measuring the surface EMG signals through two electrodes mounted on the flexor digitorum profundus and flexor pollicis longus. Furthermore, via continuously controlling a single finger's motion, the five-fingered underactuated prosthetic hand can achieve more prehensile postures such as power grasp, centralized grip, fingertip grasp, cylindrical grasp, etc. The experimental results show that the classifier has a great potential application to the control of bionic man-machine systems because of its fast learning speed, high recognition capability and strong robustness.

国家基因库概述

国家基因库是服务于国家战略需求的国家级公益性创新科研及产业基础设施建设项目。2011年1月,国家发展改革委员会正式批复同意依托深圳华大基因研究院组建深圳国家基因库。同年10月国家发展改革委员会、财政部、工业和信息化部、国家卫生计划生育委员会(原卫生部)联合批复同意深圳国家基因库建设方案,相关项目列人国家战略性新兴产业发展专项资金计划。国家基因库建立集资源样本库、生物信息库、生物资源信息网络为一体的发展及运营模式,并由深圳华大基因研究院组建及运营,拟通过建立高水平的生物资源样本库、高效的生物信息数据处理、存储与管理系统以及覆盖广泛的联盟网络,有效保护、合理开发和利用我国生物资源及基因数据资源,该项目的建设实施有利于保护我国珍贵且特有的遗传资源,实现样本资源、基因及数据资源共享开发利用,提高我国遗传资源样本和基因信息数据的储存、分析、管理和应用能力,进而推动生命科学和生物产业发展。这对于我们国家抢占未来生物经济的战略制高点、掌控生物遗传资源、基因战略资源,具有极其重要的战略意义。

Network Pharmacology-based Analysis on the Molecular Biological Mechanisms of Xin Hui Tong Formula in Coronary Heart Disease Treatment

Objective To investigate the potential molecular mechanism of Xin Hui Tong Formula (XHTF) in the treatment of coronary heart disease (CHD) by using network pharmacology and bioinformatics. Methods The targets network of CHD was constructed through Therapeutic Targets Database (TTD) and Drugbank database;The XHTF pharmacodynamic molecule-targets network and the XHTF pharmacodynamic molecule-CHD targets network were explored by the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP). And the multi-targets mechanism and molecular regulation network of XHTF in the treatment of CHD were explored from multiple perspectives by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) database pathway enrichment analysis. Results A total of 88 CHD targets were screened out through the Therapeutic Targets and the Drugbank database. 393 compounds and corresponding 205 drug targets of XHTF were retrieved from TCMSP. A total of 13 known targets directly related to the development of CHD were retrieved from the disease-related databases: TP53, MAPK14, NFKB1, HSPA5, PLG, PTGS2, ADRB1, NOS2, CYP3A4, GRIA2, CYP2A6, GRIA1, PTGS1. XHTF also contained 118 drug targets that directly interact with CHD targets. GO enrichment analysis showed that the biological processes of 13 direct targets proteins were found to be mainly enriched in response to drug, cellular response to biotic stimulus, long-chain fatty acid metabolic process, fatty acid metabolic process and regulation of blood pressure. KEGG pathway enrichment analysis found that XHTF participated in the CHD pathological process mainly through retrograde endocannabinoid signaling, regulation of lipolysis in adipocytes, cAMP signaling pathway, chemical carcinogenesis and other pathways. Conclusions XHTF plays a role in the treatment of CHD through multiple targets and multiple pathways, and provides a scientific basis for the theory of "virtual standard" in the treatment of CHD.

Exploring the Action Mechanism of Yadanzi(Brucea javanica)in the Treatment of Glioblastoma Based on Bioinformatics and Network Pharmacology

ObjectiveThe aim of the study is to explore the molecular mechanism of Yadanzi(Brucea javanica)in the treatment of glioblastoma(GBM)by using the methods of bioinformatics and network pharmacology.Methods The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP)and literature retrieval method were applied to obtain the active ingredients of Yadanzi(Brucea javanica),and to predict the relevant targets of the active ingredients.The GBM-related targets were retrieved and screened through the Gene Expression Profling Interactive Analysis(GEPIA)database,and mapped to each other with the targets of the components of Yadanzi(Brucea javanica)to obtain the intersection targets.The GBM differentially expressed gene targets were imported into the String database to obtain the protein interaction relationship,the Cytoscape software was used to draw the protein interaction network,the Cytobba and MCODE plug-ins were used to screen the core genes and important protein interaction modules,and the GEPIA database was applied to make survival analysis of the core genes.The network map of“active ingredients-targets”was constructed through the Cytoscape 3.6.1 software.Gene Ontology(GO)biological function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analysis for GBM differentially expressed genes were performed through the DAVID database.ResultsThrough TCMSP and literature retrieval,23 potential active ingredients and 129 related targets were obtained from Yadanzi(Brucea javanica).In the GEPIA database,247 GBM differentially expressed genes were screened,including 113 upregulated genes and 134 downregulated genes.After mapping with the targets related to the active ingredients of Yadanzi(Brucea javanica),six intersection targets were obtained,that is,the potential action targets of Yadanzi(Brucea javanica)in treating GBM,including MMP2,HMOX1,BIRC5,EGFR,CCNB2,and TOP2A.Cytoscape software was applied to build an“active ingredient-action target”network.Two active ingredients and five action targets of β-sitosterol(BS)and luteolin were found,and the targets were mainly concentrated in BS.It was found by KEGG pathway enrichment analysis that GBM differentially expressed genes were mainly involved in signaling pathways related to Staphylococcus aureus infection,phagosome formation,tuberculosis and systemic lupus erythematosus and other infectious and autoimmune diseases.It was found by GO enrichment analysis that the GBM differentially expressed genes mainly involved such biological processes(BP)as the processing and presentation of exogenous antigenic peptides and polysaccharide antigens through MHC Il molecules,y-interferon-mediated signaling pathways,extracellular matrix composition,and chemical synapses transmission;it involved cellular components such as cell junctions,axon terminal buttons,extracellular space,vesicle membranes for endocytosis,and MHC Il protein complexes;molecular functions such as calcium-mediated ionic protein binding,MHC Il molecular receptor activity,immunoglobulin binding,and phospholipase inhibitor activity were also involved.Survival analysis was conducted by GEPIA on the top 37 core targets in degree value,and a total of five genes related to GBM prognosis were obtained.Among them,FN1 and MMP2 were highly expressed while GABRD(v-aminobutyric acid A receptor delta subunit),RBFOX1,and SLC6A7 were expressed at a low level in cancer patients.Conclusion The pathogenesis of GBM is closely related to the human immune system,and BS and luteolin may be the main material basis of Yadanzi(Brucea javanica)for the treatment of GBM and the improvement of prognosis.The molecular mechanism may be related to the physical barrier formed by destroying the tumor cell stromal 68 Treatment of Glioblastoma Based on Bioinformatics and Network Pharmacology Zhao,Si.molecules and its involvement in tumor immune response.

LncRNA-疾病关联预测方法研究

越来越多的生物实验证实,长链非编码RNA(lncRNA)的功能异常与多种人类复杂疾病的发生具有明显关联。传统实验方法验证lncRNA-疾病关联费时费力,利用现有生物实验数据,通过计算方法预测lncRNA-疾病关联,可为生物实验设计提供重要参考,具有重要现实意义。本文对当前主流lncRNA-疾病关联预测的计算方法进行综述,总结各类方法的优点和不足,并展望后续模型的开发。

A generative model of identifying informative proteins from dynamic PPI networks

Informative proteins are the proteins that play critical functional roles inside cells.They are the fundamental knowledge of translating bioinformatics into clinical practices.Many methods of identifying informative biomarkers have been developed which are heuristic and arbitrary,without considering the dynamics characteristics of biological processes.In this paper,we present a generative model of identifying the informative proteins by systematically analyzing the topological variety of dynamic protein-protein interaction networks(PPINs).In this model,the common representation of multiple PPINs is learned using a deep feature generation model,based on which the original PPINs are rebuilt and the reconstruction errors are analyzed to locate the informative proteins.Experiments were implemented on data of yeast cell cycles and different prostate cancer stages.We analyze the effectiveness of reconstruction by comparing different methods,and the ranking results of informative proteins were also compared with the results from the baseline methods.Our method is able to reveal the critical members in the dynamic progresses which can be further studied to testify the possibilities for biomarker research.

Edge biomarkers for classification and prediction of phenotypes

In general,a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network,which can be considered as a set of interactions or edges among molecules.Thus,instead of individual molecules,networks or edges are stable forms to reliably characterize complex diseases.This paper reviews both traditional node biomarkers and edge biomarkers,which have been newly proposed.These biomarkers are classified in terms of their contained information.In particular,we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample.First,we categorize the biomarkers based on the information used in the learning and prediction steps.We then briefly introduce conventional node biomarkers,or molecular biomarkers without network information,and their computational approaches.The main focus of this paper is edge and network biomarkers,which exploit network information to improve the accuracy of diagnosis and prognosis.Moreover,by extracting both network and dynamic information from the data,we can develop dynamical network and edge biomarkers.These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state.The identified critical molecules can be used as drug targets,and the critical state indicates the critical point of disease control.The paper also discusses representative biomarker-based methods.