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基于蛋白质互作评价差异表达基因的重复性 被引量:2
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作者 任丽萍 章琳 +1 位作者 洪贵妮 郭政 《生物信息学》 2011年第3期181-184,共4页
利用高通量基因表达谱数据可以识别在肿瘤与正常组织中差异表达的基因,为研究癌机理提供重要的线索。目前,在研究同种癌型的不同实验中发现的差异表达基因的交叠比例很低。这种高通量基因表达谱数据低重复性的现象严重制约了基因芯片在... 利用高通量基因表达谱数据可以识别在肿瘤与正常组织中差异表达的基因,为研究癌机理提供重要的线索。目前,在研究同种癌型的不同实验中发现的差异表达基因的交叠比例很低。这种高通量基因表达谱数据低重复性的现象严重制约了基因芯片在癌症研究中的应用。然而,已有研究表明从研究同种癌型的不同实验数据中得到的不交叠的差异表达基因倾向于扰动相同的功能。因此,在评价差异表达基因重复性时,应考虑其在生物学功能上的一致性。本文结合基因共表达和蛋白质互作关系,设计了功能重复性指标来评价差异表达基因列表的可重复性。通过分析两套卵巢癌数据,发现对同种癌型得到的差异表达基因具有很高的功能一致性(p<0.0001)。结果表明,在功能水平上评估差异表达基因的一致性具有合理性。 展开更多
关键词 差异表达基因 共表达 蛋白 功能一致性
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生物信息学预测类风湿关节炎核心基因与互作miRNA 被引量:4
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作者 丁晓 郝颖 +3 位作者 王维山 孟德峰 王梦雨 王超 《安徽医科大学学报》 CAS 北大核心 2020年第2期228-234,共7页
目的通过生物信息学分析筛选类风湿性关节炎(RA)滑膜炎中相关的核心差异基因与相互作用的微小核糖核酸(miRNA)。方法通过GEO数据库下载基因芯片GSE55235,通过R语言3.5.0筛选出差异表达基因,并通过David在线数据库进行功能富集分析。应用... 目的通过生物信息学分析筛选类风湿性关节炎(RA)滑膜炎中相关的核心差异基因与相互作用的微小核糖核酸(miRNA)。方法通过GEO数据库下载基因芯片GSE55235,通过R语言3.5.0筛选出差异表达基因,并通过David在线数据库进行功能富集分析。应用String 10.5、Cytoscape v3.6.1和MCODE插件建立蛋白相互作用网络,筛选出RA发生过程中的核心基因。通过CyTargetLinker预测与核心基因互作的miRNA。结果筛选出605个差异表达基因,其中上调314个,下调291个。其功能主要富集于免疫反应和细胞大分子生物合成和结合等过程。蛋白相互作用网络共有552个节点和5163条边,筛选出了前4的作用模块和10个核心基因:蛋白酪氨酸磷酸酶受体C型(PTPRC)、血管内皮生长因子α(VEGFα)、纤连蛋白1基因(FN1)、整合素亚基M(ITGAM)、表皮生长因子(EGFR)、CD86、基质金属蛋白酶9(MMP9)、整合素亚基β2(ITGB2)、TYRO蛋白酪氨酸激酶结合蛋白(TYROBP)和MYC。并预测了36个miRNA可与其中3个核心基因靶向性相互作用。结论筛选出的核心基因与相互作用的miRNA可能成为RA潜在治疗的靶点。 展开更多
关键词 类风湿关节炎 生物信息学 蛋白互作网 核心基因 微小核糖核酸
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Analysis of mechanism on Indigo Naturalis in treating chronic myelocytic leukemia based on two-dimentional model of protein-protein interaction network-moleculardocking technique
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作者 Cun Liu Xiao-Ming Zhang +3 位作者 Li-Juan Liu Chao Zhou Hong Liu Jing Zhuang 《TMR Theory and Hypothesis》 2018年第1期13-17,共5页
To explore the molecular mechanism of Ind-igo Naturalis in intervening chronic myelocytic leukemia (CML) under the guidance of protein-protein interaction network, the molecular docking technique and in vitro c... To explore the molecular mechanism of Ind-igo Naturalis in intervening chronic myelocytic leukemia (CML) under the guidance of protein-protein interaction network, the molecular docking technique and in vitro cell experiment were chosen. CML-related genes were obtained from the online mendelian inheritance in man database (OMIM), then String 10. 0 was used for text mining and constructing the CML protein-protein interaction network. The interaction data were input in Cytoscape 3. 4. 0 software. Plug-in CentiScaPe 2. 1 was used for implement topology analysis. Small active substances of Indigo Naturalis were obtained from a third-party database, which were optimized by Chemoffice 8. 0 and Sybyl 8. 1, then small molecular ligand library was obtained. The molecular docking was carried out by Surflex-Dock module, the key target was received after scoring. Protein-protein interaction network of CML was constructed, which was consisted of 425 nodes ( proteins) and 2 799 sides ( interactions). The key gene J.AK2 was got. CML is a polygenic disease and JAK2 is likely to be a key node. 展开更多
关键词 Indigo Naturalis Chronic myelocytic leukemia PROTEIN Interaction network Molecular docking
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基于降维的蛋白质不相关功能预测 被引量:4
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作者 余国先 傅广垣 +1 位作者 王峻 郭茂祖 《中国科学:信息科学》 CSCD 北大核心 2017年第10期1349-1368,共20页
蛋白质是生命活动的重要物质基础,对其功能的准确标注可以极大地促进生命科学的研究与发展.已有的蛋白质功能预测方法通常仅关注利用蛋白质具有某些功能的信息(正样例),并没有关注利用蛋白质不相关的功能信息(负样例).已有研究表明,结... 蛋白质是生命活动的重要物质基础,对其功能的准确标注可以极大地促进生命科学的研究与发展.已有的蛋白质功能预测方法通常仅关注利用蛋白质具有某些功能的信息(正样例),并没有关注利用蛋白质不相关的功能信息(负样例).已有研究表明,结合蛋白质负样例可以降低蛋白质功能预测的复杂度并提高预测精度.本文提出一种基于降维的蛋白质不相关功能预测方法 (predicting irrelevant functions of proteins based on dimensionality reduction,IFDR).IFDR通过在蛋白质互作网邻接矩阵和蛋白质–功能标记关联矩阵上分别进行随机游走,挖掘蛋白质之间的内在关系和预估蛋白质的缺失功能标记,再分别利用奇异值分解将上述2个矩阵投影降维为低维实数矩阵,最后利用半监督回归预测负样例.在酵母菌、人类和拟南芥的蛋白质数据集上的实验表明,IFDR比已有相关算法能够更准确地预测负样例,对互作网络和功能标记空间的降维均可以提高负样例预测精度. 展开更多
关键词 蛋白质功能预测 正负样例 蛋白 功能标记 降维
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基于有向混合图的蛋白质新功能预测 被引量:5
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作者 傅广垣 余国先 +1 位作者 王峻 张自力 《中国科学:信息科学》 CSCD 北大核心 2016年第4期461-475,共15页
蛋白质执行着生物体内各种重要生物活动,对蛋白质功能的准确标注能极大地促进生命科学研究与应用.传统的湿实验法通量低,已无法测定高通量技术产生的海量蛋白质功能,基于计算模型的大规模蛋白质功能预测是后基因时代生物信息学的核心任... 蛋白质执行着生物体内各种重要生物活动,对蛋白质功能的准确标注能极大地促进生命科学研究与应用.传统的湿实验法通量低,已无法测定高通量技术产生的海量蛋白质功能,基于计算模型的大规模蛋白质功能预测是后基因时代生物信息学的核心任务之一.当前基于机器学习的方法通常仅关注对完全未标记功能的蛋白质的功能预测,而忽略了已标注功能的蛋白质可能存在的自身功能标记的不完整性,预测精度有限.本文结合基因本体层次结构关系和蛋白质互作网信息,设计了一种有向混合图(directed hybrid graph,d HG)对上述信息进行描述,并在此基础上提出一种基于有向混合图重启动随机游走的蛋白质功能预测方法——d HG.本文提出的d HG方法不仅能补充已知部分功能标记的蛋白质新功能,还能预测功能完全未知的蛋白质新功能.在酵母菌和人类蛋白质上的实验结果表明,d HG在多种评价度量上的预测性能均优于现有方法,且效率更高. 展开更多
关键词 蛋白质功能预测 机器学习 有向混合图 随机游走 基因本体 蛋白
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基于0-1矩阵分解的蛋白质功能预测 被引量:2
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作者 赵颖闻 王峻 +2 位作者 郭茂祖 张自力 余国先 《中国科学:信息科学》 CSCD 北大核心 2019年第9期1159-1174,共16页
准确地标注蛋白质功能是功能基因组学的核心任务之一.蛋白质功能标注信息存在大量缺失且功能标签空间巨大.近期一些标签压缩方法被提出并应用于蛋白质功能预测,但是这些方法获取的压缩标签可解释性差,且面临着多标记学习中的阈值划分难... 准确地标注蛋白质功能是功能基因组学的核心任务之一.蛋白质功能标注信息存在大量缺失且功能标签空间巨大.近期一些标签压缩方法被提出并应用于蛋白质功能预测,但是这些方法获取的压缩标签可解释性差,且面临着多标记学习中的阈值划分难题.为解决这些问题,本文提出一种基于0-1矩阵分解的蛋白质功能预测方法 (zero-one matrix factorization, ZOMF). ZOMF首先将蛋白质–功能标签关联矩阵分解成两个低秩0-1矩阵,挖掘蛋白质和功能标签间的内在关联.其次它利用蛋白质互作网和基因本体结构信息分别针对上述两个低秩矩阵定义了平滑正则项,约束指导低秩矩阵的优化.最后它利用优化获取的低秩矩阵重构关联矩阵,进而实现蛋白质功能预测.通过在酵母菌、拟南芥、老鼠和人类数据集上的实验表明, ZOMF比已有的相关算法能够更准确地预测蛋白质功能,它无需对重构的关联矩阵进行阈值划分,压缩的0-1标签可解释性更直观. 展开更多
关键词 蛋白质功能预测 矩阵分解 蛋白 基因本体 阈值划分
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ON NETWORK-BASED KERNEL METHODS FOR PROTEIN-PROTEIN INTERACTIONS WITH APPLICATIONS IN PROTEIN FUNCTIONS PREDICTION 被引量:1
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作者 Limin LI Waiki CHING +1 位作者 Yatming CHAN Hiroshi MAMITSUKA 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2010年第5期917-930,共14页
Predicting protein functions is an important issue in the post-genomic era. This paper studies several network-based kernels including local linear embedding (LLE) kernel method, diffusion kernel and laplacian kerne... Predicting protein functions is an important issue in the post-genomic era. This paper studies several network-based kernels including local linear embedding (LLE) kernel method, diffusion kernel and laplacian kernel to uncover the relationship between proteins functions and protein-protein interactions (PPI). The author first construct kernels based on PPI networks, then apply support vector machine (SVM) techniques to classify proteins into different functional groups. The 5-fold cross validation is then applied to the selected 359 GO terms to compare the performance of different kernels and guilt-by-association methods including neighbor counting methods and Chi-square methods. Finally, the authors conduct predictions of functions of some unknown genes and verify the preciseness of our prediction in part by the information of other data source. 展开更多
关键词 Diffusion kernel kernel method Laplacian kernel local linear embedding (LLE) kernel protein function prediction support vector machine.
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Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks 被引量:6
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作者 LI Min LI Qi +3 位作者 GANEGODA Gamage Upeksha WANG JianXin WU FangXiang PAN Yi 《Science China(Life Sciences)》 SCIE CAS 2014年第11期1064-1071,共8页
Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies.However,it is still time-consuming and laborious to determine the real disease-causing gen... Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies.However,it is still time-consuming and laborious to determine the real disease-causing genes by biological experiments.With the advances of the high-throughput techniques,a large number of protein-protein interactions have been produced.Therefore,to address this issue,several methods based on protein interaction network have been proposed.In this paper,we propose a shortest path-based algorithm,named SPranker,to prioritize disease-causing genes in protein interaction networks.Considering the fact that diseases with similar phenotypes are generally caused by functionally related genes,we further propose an improved algorithm SPGOranker by integrating the semantic similarity of gene ontology(GO)annotations.SPGOranker not only considers the topological similarity between protein pairs in a protein interaction network but also takes their functional similarity into account.The proposed algorithms SPranker and SPGOranker were applied to 1598 known orphan disease-causing genes from 172 orphan diseases and compared with three state-of-the-art approaches,ICN,VS and RWR.The experimental results show that SPranker and SPGOranker outperform ICN,VS,and RWR for the prioritization of orphan disease-causing genes.Importantly,for the case study of severe combined immunodeficiency,SPranker and SPGOranker predict several novel causal genes. 展开更多
关键词 disease-causing genes PRIORITIZATION gene ontology protein interaction network shortest path
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Overlap maximum matching ratio (OMMR): a new measure to evaluate overlaps of essential modules 被引量:1
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作者 Xiao-xia ZHANG Qiang-hua XIAO +3 位作者 Bin LI Sai HU Hui-jun XIONG Bi-hai ZHAO 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2015年第4期293-300,共8页
Protein complexes are the basic units of macro-molecular organizations and help us to understand the cell's mechanism.The development of the yeast two-hybrid,tandem affinity purification,and mass spectrometry high... Protein complexes are the basic units of macro-molecular organizations and help us to understand the cell's mechanism.The development of the yeast two-hybrid,tandem affinity purification,and mass spectrometry high-throughput proteomic techniques supplies a large amount of protein-protein interaction data,which make it possible to predict overlapping complexes through computational methods.Research shows that overlapping complexes can contribute to identifying essential proteins,which are necessary for the organism to survive and reproduce,and for life's activities.Scholars pay more attention to the evaluation of protein complexes.However,few of them focus on predicted overlaps.In this paper,an evaluation criterion called overlap maximum matching ratio(OMMR) is proposed to analyze the similarity between the identified overlaps and the benchmark overlap modules.Comparison of essential proteins and gene ontology(GO) analysis are also used to assess the quality of overlaps.We perform a comprehensive comparison of serveral overlapping complexes prediction approaches,using three yeast protein-protein interaction(PPI) networks.We focus on the analysis of overlaps identified by these algorithms.Experimental results indicate the important of overlaps and reveal the relationship between overlaps and identification of essential proteins. 展开更多
关键词 Protein-protein interaction network Essential protein modules OVERLAP Overlap maximum matching ratio
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A generative model of identifying informative proteins from dynamic PPI networks 被引量:2
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作者 ZHANG Yuan CHENG Yue +1 位作者 JIA KeBin ZHANG AiDong 《Science China(Life Sciences)》 SCIE CAS 2014年第11期1080-1089,共10页
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 b... 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. 展开更多
关键词 dynamic protein-protein interaction network abnormal detection multi-view data deep belief network
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Two-dimensional gel electrophoresis-based analysis provides global insights into the cotton ovule and fiber proteomes 被引量:3
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作者 Xiang Jin Limin Wang +2 位作者 Liping He Weiqiang Feng Xuchu Wang 《Science China(Life Sciences)》 SCIE CAS CSCD 2016年第2期154-163,共10页
Proteomic analysis of upland cotton was performed to profile the global detectable proteomes of ovules and fibers using two-dimensional electrophoresis(2DE).A total of 1,203 independent protein spots were collected fr... Proteomic analysis of upland cotton was performed to profile the global detectable proteomes of ovules and fibers using two-dimensional electrophoresis(2DE).A total of 1,203 independent protein spots were collected from representative 2DE gels,which were digested with trypsin and identified by matrix-assisted laser desorption and ionization-time-offlight/time-of-flight(MALDI-TOF/TOF)mass spectrometry.The mass spectrometry or tandem mass spectrometry(MS or MS/MS)data were then searched against a local database constructed from Gossypium hirsutum genome sequences,resulting in successful identification of 975 protein spots(411 for ovules and 564 for fibers).Functional annotation analysis of the 975identified proteins revealed that ovule-specific proteins were mainly enriched in functions related to fatty acid elongation,sulfur amino acid metabolism and post-replication repair,while fiber-specific proteins were enriched in functions related to root hair elongation,galactose metabolism and D-xylose metabolic processes.Further annotation analysis of the most abundant protein spots showed that 28.96%of the total proteins in the ovule were mainly located in the Golgi apparatus,endoplasmic reticulum,mitochondrion and ribosome,whereas in fibers,27.02%of the total proteins were located in the cytoskeleton,nuclear envelope and cell wall.Quantitative real-time polymerase chain reaction(q RT-PCR)analyses of the ovule-specific protein spots P61,P93 and P198 and fiber-specific protein spots 230,477 and 511 were performed to validate the proteomics data.Protein-protein interaction network analyses revealed very different network cluster patterns between ovules and fibers.This work provides the largest protein identification dataset of 2DE-detectable proteins in cotton ovules and fibers and indicates potentially important roles of tissue-specific proteins,thus providing insights into the cotton ovule and fiber proteomes on a global scale. 展开更多
关键词 COTTON 2DE MALDI-TOF/TOF GO enrichment protein-protein interaction networks
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Identification of neuron-related genes for cell therapy of neurological disorders by network analysis
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作者 Li-ning SU Xiao-qing SONG +1 位作者 Hui-ping WEI Hai-feng YIN 《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》 SCIE CAS CSCD 2017年第2期172-182,共11页
Bone mesenchymal stem cells(BMSCs) differentiated into neurons have been widely proposed for use in cell therapy of many neurological disorders. It is therefore important to understand the molecular mechanisms under... Bone mesenchymal stem cells(BMSCs) differentiated into neurons have been widely proposed for use in cell therapy of many neurological disorders. It is therefore important to understand the molecular mechanisms underlying this differentiation. We screened differentially expressed genes between immature neural tissues and untreated BMSCs to identify the genes responsible for neuronal differentiation from BMSCs. GSE68243 gene microarray data of rat BMSCs and GSE18860 gene microarray data of rat neurons were received from the Gene Expression Omnibus database. Transcriptome Analysis Console software showed that 1248 genes were up-regulated and 1273 were down-regulated in neurons compared with BMSCs. Gene Ontology functional enrichment, protein-protein interaction networks, functional modules, and hub genes were analyzed using DAVID, STRING 10, BiN GO tool, and Network Analyzer software, revealing that nine hub genes, Nrcam, Sema3 a, Mapk8, Dlg4, Slit1, Creb1, Ntrk2, Cntn2, and Pax6, may play a pivotal role in neuronal differentiation from BMSCs. Seven genes, Dcx, Nrcam, Sema3 a, Cntn2, Slit1, Ephb1, and Pax6, were shown to be hub nodes within the neuronal development network, while six genes, Fgf2, Tgfβ1, Vegfa, Serpine1, Il6, and Stat1, appeared to play an important role in suppressing neuronal differentiation. However, additional studies are required to confirm these results. 展开更多
关键词 Neuronal differentiation Bone mesenchymal stem cells(BMSCs) Protein-protein interaction network Differentially expressed genes
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Chapman-Kolmogorov equations for global PPIs with Discriminant-EM
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作者 Md. Sarwar Kamal Mohammad Ibrahim Khan 《International Journal of Biomathematics》 2014年第5期95-101,共7页
Ongoing improvements in Computational Biology research have generated massive amounts of Protein-Protein Interactions (PPIs) dataset. In this regard, the availability of PPI data for several organisms provoke the di... Ongoing improvements in Computational Biology research have generated massive amounts of Protein-Protein Interactions (PPIs) dataset. In this regard, the availability of PPI data for several organisms provoke the discovery of computational methods for measurements, analysis, modeling, comparisons, clustering and alignments of biological data networks. Nevertheless, fixed network comparison is computationally stubborn and as a result several methods have been used instead. We illustrate a prohabilistic approach among proteins nodes that are part of various networks by using Chapman-Kolmogorov (CK) formula. We have compared CK formula with semi-Markov random method, SMETANA. We significantly noticed that CK outperforms the SMETANA in all respects such as efficiency, speed, space and complexity. We have modified the SMETANA source codes available in MATLAB in the light of CK formula. Discriminant-Expectation Maximization (D-EM) accesses the parameters of a protein network datasets and determines a linear transformation to simplify the assumption of probabilistic format of data distributions and find good features dynamically. Our implementation finds that D-EM has a satisfactory performance in protein network alignment applications. 展开更多
关键词 SMETANA semi-Markov method Chapman-Kolmogorov formula D-EM.
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