基于功能一致性预测冠心病致病基因

目的:为了解疾病致病机理和改进临床治疗,基于功能一致性挖掘潜在的疾病致病基因.方法:本文基于功能一致性基因的共定位特性,结合蛋白质互作网络拓扑结构,获取疾病候选基因集,并通过GO及KEGG功能富集分析方法进一步筛选,预测出新的致病基因.结果:挖掘得到的59个冠心病致病基因通过文献证实绝大部分基因与疾病的发生发展存在着联系.结论:本方法具有可行性,研究者能够在此基础上很好地进行疾病致病机理的研究.     

基于整合网络和联合策略预测心肌梗死的新致病基因

目的:基于整合网络和联合策略预测心肌梗死的新致病基因.方法:从系统生物学的角度,提出基于蛋白质亚细胞定位信息,构建区域化的蛋白质互作的整合网络;通过疾病风险基因与已知致病基因的功能一致性程度和互作相关性的强度联合筛选的新策略,预测心肌梗死的新致病基因.结果:预测出10个心肌梗死的新致病基因(CCL19、CCL25、COMP、CCL11、CCL7、F2、KLKB1、HTR6、ADRB1、BDKRB2),其中8个基因(CCL 19、CCL25、CCL11、CCL7、F2、KLKB1、ADRB1、BDKRB2)经文献证实与心肌梗死的发生发展有着密切的联系;另外2个基因(COMP、HTR6)尚需实验验证.结论:基于整合网络和联合策略预测出10个心肌梗死的新致病基因,此方法为探索复疾病的致病基因提供了新的思路,有助于阐明复杂疾病的致病机理.     

基于表达及网络拓扑结构挖掘动脉粥样硬化风险疾病基因

基于功能基因组信息、网络拓扑结构信息整合分析方法,利用基因表达谱数据和蛋白质互作数据挖掘动脉粥样硬化(AS)风险疾病基因,为从基因组层面研究动脉粥样硬化提供了新的视角．经过差异表达分析,支持向量机(SVM)的机器学习方法双重筛选,可以鉴别出可信度水平较高的风险疾病基因,对于研究动脉粥样硬化疾病基因在网络中的拓扑性质,建立基因与疾病发生发展过程的联系,提供了新的思路．得到了巨噬细胞样本中59个风险疾病基因,泡沫细胞中61个风险疾病基因．这些风险基因与已知疾病基因共享大部分动脉粥样硬化病变相关生物学过程及信号通路．并应用到对其他复杂疾病致病机理的研究中．     

基于二元网络异步重启随机游走算法预测肺癌风险致病基因

肺癌致病基因的发现及预测有助于认识肺癌的发生机理、诊断与防治,是人类基因组研究的重要目标。应用现有二元网络重启随机游走算法预测致病基因时,一般先在疾病表型网络、蛋白质作用网络及疾病-蛋白质二分图网络内随机游走一步,然后进行网络间跳转,这种策略不仅搜索效率较低,还可能遗漏蛋白质(或疾病)网络中的局部拓扑信息。鉴于此,作者提出一种二元网络异步重启游走(asynchronously random walk with restart,ARWRH)算法,构建疾病表型-蛋白质异构网络,深层次挖掘潜在肺癌风险致病基因。ARWRH算法首先在疾病表型网络、蛋白质作用网络及疾病表型-蛋白质二分图网络内随机游走不同步数,然后进行网络间跳转,迭代形成稳态概率向量,从而获得候选致病基因。仿真实验表明,ARWRH算法可有效预测肺癌潜在风险致病基因,多数预测结果获得了文献证据支持。     

基于SNP 互作识别潜在冠心病致病基因

目的:基于全基因组关联分析(Genomewideassociationstudy,GWAS)数据与生物信息学方法,识别冠心病潜在致病基因。方法:利用生物信息学方法和GWAS数据,对单核苷酸多态性(SingleNucleotidePolymorphisms,SNP)进行疾病风险打分,依据特定距离阈值内的SNP-SNP互作关系,筛选出疾病相关SNP显著风险模块,识别潜在致病基因。结果:设定阈值20kb,经筛选获得279个SNP显著风险模块,映射到79个基因,文献验证率为71.01%。结论:基于SNP互作识别的潜在致病基因,能更加准确的分析冠心病的发生发展过程。     

致心律失常性右心室心肌病致病蛋白的挖掘

目的:鉴定疾病蛋白对深入理解致心律失常性右心室心肌病(ARVC)致病机制至关重要。可以采用计算生物学的方法,在ARVC疾病相关网络中挖掘新的潜在的致病蛋白。方法:本文整合HPRD和BioGRID的蛋白质互作数据,获得了较为全面且真实可靠的蛋白质互作数据;通过结合文本挖掘和统计学检验筛选出ARVC种子蛋白,应用最近邻居扩增的方法,构建ARVC蛋白质互作网络(PPIN),并采用PRINCESS法则对网络中每对互作蛋白加权;最后,基于ARVC关联得分策略对网络中的每个蛋白质打分并排秩。结果:分析发现排秩前50的候选蛋白大都与ARVC关系密切,如PRKCA,CDH1,SMAD4,SMAD2,CDH5,CTNNA1,DSC1等在调节心肌收缩、细胞程序性死亡、心脏的发育过程及维持桥粒的完整性方面起重要作用。结论:我们提出的方法为鉴定与ARVC致病机制相关的新致病蛋白提供了有效的途径。     

人类蛋白质结构互作网络——结构域对网络拓扑与蛋白质功能的影响

高通量的蛋白质互作数据与结构域互作数据的出现,使得在蛋白质组学领域内研究人类蛋白质结构互作网络,进一步揭示蛋白质结构与功能间的潜在关系成为可能.蛋白质上广泛分布的结构域被认为是蛋白质结构、功能以及进化的基本功能单元.然而,结合蛋白质的结构信息(例如蛋白质结构域数目、长度和覆盖率等)来研究这些表象后的内部机制仍然面临着挑战.将蛋白质分为单结构域蛋白质与多结构域蛋白质,并进一步结合蛋白质互作信息与结构域互作信息构建了人类蛋白质结构互作网络;通过与人类蛋白质互作网络进行比较,研究了人类蛋白质结构互作网络的特殊结构特征;对于单结构域蛋白质与多结构域蛋白质,分别进行了功能富集分析、功能离散度分析以及功能一致性分析等.结果发现,将结构域互作信息综合考虑进来后,人类蛋白质结构互作网络可以提供更多的单纯的蛋白质互作网络无法提供的细节信息,揭示蛋白质互作网络的复杂性.     

利用eQTL构建基因-基因网络挖掘类风湿性关节炎风险基因

目的：类风湿性关节炎是一种全身的慢性炎症型疾病,可能影响许多组织和器官,主要发作于灵活的关节。全世界人群中大约有1％会患有类风湿性关节炎。目前已经证实了一些基因与类风湿性关节炎相关,但是这些基因只能解释一小部分遗传风险,因此我们需要新的策略和方法来解决这个问题。方法：表达数量性状位点（eQTL）是指能够调控基因或蛋白质表达的基因组位点,本文采用了eQTL数据构建基因一基因网络并挖掘候选类风湿性关节炎风险基因。结果：首先,利用eQTL数据,基于基因之间的共调控系数,建立基因-基因网络,我们建立了5个不同阈值（0、O．2、0．4、0．6和0．8）的基因-基因网络;然后,在OMIM和GAD数据库中搜索已经证实的与类风湿性关节炎相关的186个基因;最后我们将已证实与类风湿性关节炎相关的186个基因分别投入到这5个网络中,利用基因与基因之间的相关性来挖掘到一些可能与类风湿性关节炎相关的候选风险基因。结论：本文基于eQTL构建了基因．基因网络,结合已知类风湿性关节炎风险基因,挖掘未知风险基因,得到了较好的结果,证明了本方法的有效性,且对于类风湿性关节炎的发病机制研究具有重要价值。除了类风湿性关节炎外,本方法还可推广到其它复杂疾病中,因此本方法对人类复杂疾病的研究具有很强的学术理论价值和应用价值。     

基于致病基因网络模块性预测风险致病基因

相关疾病基因的发现和预测有助于认识疾病发生机理及该疾病的诊断与防治,是人类基因组研究的重要目标。临床表现重叠的疾病经常由同一功能模块中的一个或多个基因变异引起,且导致疾病表型相似的基因间经常发生直接或间接相互作用,也就是致病基因具有网络模块性。鉴于此,基于k近邻思想扩展异构网络游走RWRH算法中的初始游走概率向量,作者提出一种改进的异构网络随机游走KRWRH算法,在基因-表型异构网络中深层次挖掘潜在风险致病基因。KRWRH算法通过扩展种子集合构建起始概率向量,种子集合包含已知致病基因及其k近邻基因;然后在异构网络中随机游走,通过迭代形成稳态概率向量,从而获得候选致病基因。通过对孟德尔遗传在线数据库中的18种遗传疾病进行仿真验证,说明KRWRH算法可有效预测潜在风险致病基因。     

基于CNV区域网络识别神经胶质瘤的重要功能区域

目的：基于基因拷贝数变异（CNV）区域网络识别神经胶质瘤的重要功能区域。方法：运用独特的计算样本的共相关性值的方法,使CNV数据与基因数据产生联系;基于蛋白质互作关系,在CNV区域与基因之间搭建桥梁,构建CNV区域网络;分析网络拓扑性质,识别出神经胶质瘤的重要功能CNV区域。结果：本文共识别出了11个与神经胶质瘤相关的候选重要功能CNV区域,通过功能注释和通路分析,确认了识别到的区域与神经胶质瘤有重要联系。结论：通过基因与表型之间的联系,利用已知表型基因在同源、功能、互作、结构域上的特征将CNV区域与基因联系起来,通过基因的功能可以了解到CNV区域的功能,对于疾病的预测和诊断有重要的意义。     

Predicting candidate genes based on combined network topological features: a case study in coronary artery disease

Predicting candidate genes using gene expression profiles and unbiased protein-protein interactions (PPI) contributes a lot in deciphering the pathogenesis of complex diseases. Recent studies showed that there are significant disparities in network topological features between non-disease and disease genes in protein-protein interaction settings. Integrated methods could consider their characteristics comprehensively in a biological network. In this study, we introduce a novel computational method, based on combined network topological features, to construct a combined classifier and then use it to predict candidate genes for coronary artery diseases (CAD). As a result, 276 novel candidate genes were predicted and were found to share similar functions to known disease genes. The majority of the candidate genes were cross-validated by other three methods. Our method will be useful in the search for candidate genes of other diseases.     

Vavien: an algorithm for prioritizing candidate disease genes based on topological similarity of proteins in interaction networks

Genome-wide linkage and association studies have demonstrated promise in identifying genetic factors that influence health and disease. An important challenge is to narrow down the set of candidate genes that are implicated by these analyses. Protein-protein interaction (PPI) networks are useful in extracting the functional relationships between known disease and candidate genes, based on the principle that products of genes implicated in similar diseases are likely to exhibit significant connectivity/proximity. Information flow?based methods are shown to be very effective in prioritizing candidate disease genes. In this article, we utilize the topology of PPI networks to infer functional information in the context of disease association. Our approach is based on the assumption that PPI networks are organized into recurrent schemes that underlie the mechanisms of cooperation among different proteins. We hypothesize that proteins associated with similar diseases would exhibit similar topological characteristics in PPI networks. Utilizing the location of a protein in the network with respect to other proteins (i.e., the "topological profile" of the proteins), we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. We test the resulting algorithm, Vavien, via systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance in Man (OMIM) database. Vavien outperforms other network-based prioritization algorithms as shown in the results and is available at www.diseasegenes.org.     

Global risk transformative prioritization for prostate cancer candidate genes in molecular networks

Understanding the pathogenesis of complex diseases is aided by precise identification of the genes responsible. Many computational methods have been developed to prioritize candidate disease genes, but coverage of functional annotations may be a limiting factor for most of these methods. Here, we introduce a global candidate gene prioritization approach that considers information about network properties in the human protein interaction network and risk transformative contents from known disease genes. Global risk transformative scores were then used to prioritize candidate genes. This method was introduced to prioritize candidate genes for prostate cancer. The effectiveness of our global risk transformative algorithm for prioritizing candidate genes was evaluated according to validation studies. Compared with ToppGene and random walk-based methods, our method outperformed the two other candidate gene prioritization methods. The generality of our method was assessed by testing it on prostate cancer and other types of cancer. The performance was evaluated using standard leave-one-out cross-validation.     

Gene gravity-like algorithm for disease gene prediction based on phenotype-specific network

Background

Polygenic diseases are usually caused by the dysfunction of multiple genes. Unravelling such disease genes is crucial to fully understand the genetic landscape of diseases on molecular level. With the advent of ‘omic’ data era, network-based methods have prominently boosted disease gene discovery. However, how to make better use of different types of data for the prediction of disease genes remains a challenge.

Results

In this study, we improved the performance of disease gene prediction by integrating the similarity of disease phenotype, biological function and network topology. First, for each phenotype, a phenotype-specific network was specially constructed by mapping phenotype similarity information of given phenotype onto the protein-protein interaction (PPI) network. Then, we developed a gene gravity-like algorithm, to score candidate genes based on not only topological similarity but also functional similarity. We tested the proposed network and algorithm by conducting leave-one-out and leave-10%-out cross validation and compared them with state-of-art algorithms. The results showed a preference to phenotype-specific network as well as gene gravity-like algorithm. At last, we tested the predicting capacity of proposed algorithms by test gene set derived from the DisGeNET database. Also, potential disease genes of three polygenic diseases, obesity, prostate cancer and lung cancer, were predicted by proposed methods. We found that the predicted disease genes are highly consistent with literature and database evidence.

Conclusions

The good performance of phenotype-specific networks indicates that phenotype similarity information has positive effect on the prediction of disease genes. The proposed gene gravity-like algorithm outperforms the algorithm of Random Walk with Restart (RWR), implicating its predicting capacity by combing topological similarity with functional similarity. Our work will give an insight to the discovery of disease genes by fusing multiple similarities of genes and diseases.

     

Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.     

Identifying miRNA-disease association based on integrating miRNA topological similarity and functional similarity

Background: MicroRNAs (miRNAs) are a significant type of non-coding RNAs, which usually were encoded by endogenous genes with about ~22 nt nucleotides. Accumulating biological experiments have shown that miRNAs have close associations with various human diseases. Although traditional experimental methods achieve great successes in miRNA-disease interaction identification, these methods also have some limitations. Therefore, it is necessary to develop computational method to predict miRNA-disease interactions. Methods: Here, we propose a computational framework (MDVSI) to predict interactions between miRNAs and diseases by integrating miRNA topological similarity and functional similarity. Firstly, the CosRA index is utilized to measure miRNA similarity based on network topological feature. Then, in order to enhance the reliability of miRNA similarity, the functional similarity and CosRA similarity are integrated based on linear weight method. Further, the potential miRNA-disease associations are predicted by using recommendation method. In addition, in order to overcome limitation of recommendation method, for new disease, a new strategy is proposed to predict potential interactions between miRNAs and new disease based on disease functional similarity. Results: To evaluate the performance of different methods, we conduct ten-fold cross validation and de novo test in experiment and compare MDVSI with two the-state-of-art methods. The experimental result shows that MDVSI achieves an AUC of 0.91, which is at least 0.012 higher than other compared methods. Conclusions: In summary, we propose a computational framework (MDSVI) for miRNA-disease interaction prediction. The experiment results demonstrate that it outperforms other the-state-of-the-art methods. Case study shows that it can effectively identify potential miRNA-disease interactions.     

An integrated approach to inferring gene-disease associations in humans

One of the most important tasks of modern bioinformatics is the development of computational tools that can be used to understand and treat human disease. To date, a variety of methods have been explored and algorithms for candidate gene prioritization are gaining in their usefulness. Here, we propose an algorithm for detecting gene-disease associations based on the human protein-protein interaction network, known gene-disease associations, protein sequence, and protein functional information at the molecular level. Our method, PhenoPred, is supervised: first, we mapped each gene/protein onto the spaces of disease and functional terms based on distance to all annotated proteins in the protein interaction network. We also encoded sequence, function, physicochemical, and predicted structural properties, such as secondary structure and flexibility. We then trained support vector machines to detect gene-disease associations for a number of terms in Disease Ontology and provided evidence that, despite the noise/incompleteness of experimental data and unfinished ontology of diseases, identification of candidate genes can be successful even when a large number of candidate disease terms are predicted on simultaneously. Availability: www.phenopred.org.     

A computational method based on the integration of heterogeneous networks for predicting disease-gene associations

The identification of disease-causing genes is a fundamental challenge in human health and of great importance in improving medical care, and provides a better understanding of gene functions. Recent computational approaches based on the interactions among human proteins and disease similarities have shown their power in tackling the issue. In this paper, a novel systematic and global method that integrates two heterogeneous networks for prioritizing candidate disease-causing genes is provided, based on the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein interactions. In this method, the association score function between a query disease and a candidate gene is defined as the weighted sum of all the association scores between similar diseases and neighbouring genes. Moreover, the topological correlation of these two heterogeneous networks can be incorporated into the definition of the score function, and finally an iterative algorithm is designed for this issue. This method was tested with 10-fold cross-validation on all 1,126 diseases that have at least a known causal gene, and it ranked the correct gene as one of the top ten in 622 of all the 1,428 cases, significantly outperforming a state-of-the-art method called PRINCE. The results brought about by this method were applied to study three multi-factorial disorders: breast cancer, Alzheimer disease and diabetes mellitus type 2, and some suggestions of novel causal genes and candidate disease-causing subnetworks were provided for further investigation.     

Network‐based global inference of human disease genes

Deciphering the genetic basis of human diseases is an important goal of biomedical research. On the basis of the assumption that phenotypically similar diseases are caused by functionally related genes, we propose a computational framework that integrates human protein–protein interactions, disease phenotype similarities, and known gene–phenotype associations to capture the complex relationships between phenotypes and genotypes. We develop a tool named CIPHER to predict and prioritize disease genes, and we show that the global concordance between the human protein network and the phenotype network reliably predicts disease genes. Our method is applicable to genetically uncharacterized phenotypes, effective in the genome‐wide scan of disease genes, and also extendable to explore gene cooperativity in complex diseases. The predicted genetic landscape of over 1000 human phenotypes, which reveals the global modular organization of phenotype–genotype relationships. The genome‐wide prioritization of candidate genes for over 5000 human phenotypes, including those with under‐characterized disease loci or even those lacking known association, is publicly released to facilitate future discovery of disease genes.