基于復雜網(wǎng)絡的疾病基因預測的研究
發(fā)布時間：2018-10-13 12:06
【摘要】：近年來,識別疾病的相關基因成為生命科學領域富有挑戰(zhàn)性的工作之一。傳統(tǒng)的預測疾病基因的方法有連鎖分析(Linkage Analysis)和關聯(lián)研究(Association Study)。但是連鎖分析方法只能定位染色體上的一段區(qū)域,這段區(qū)域包含幾十個到幾百個基因。同時關聯(lián)研究也需要明確候選基因。各國研究人員陸續(xù)的提出對這段區(qū)域的候選基因進行進一步篩選的方法。 人類基因組計劃(Human Genome Project, HGP)的完成和高通量生物技術的產(chǎn)生,我們獲取了大規(guī)模的人類蛋白質(zhì)交互作用數(shù)據(jù)(Protein-Protein Interaction, PPI)。有研究表明,在PPI網(wǎng)絡上,具有較高拓撲重疊的蛋白質(zhì)共屬于一個生物功能模塊或生物通路的可能性就越大�；诖�,本文中,我們提出了基于PPI網(wǎng)絡的對疾病候選基因進行預測的方法MTOMATOM。此方法結合多點拓撲重疊法(Multi-node Topology Overlap Measure, MTOM)和兩點拓撲重疊法(Averaged Topology Overlap Measure, ATOM)。 MTOMATOM方法是通過衡量網(wǎng)絡節(jié)點間拓撲重疊性大小來反映網(wǎng)絡節(jié)點間的相似性,是一個更能反映生物意義的網(wǎng)絡距離度量法。我們把該方法在包含783個基因的110類疾病-基因家族中進行50-fold留一法交叉驗證,發(fā)現(xiàn)enrichment達到27-fold, roc曲線下面積為92.3%,取得了與同類方法相比較好的效果。 我們把MTOMATOM方法應用于阿爾茨海默氏病(Alzheimer's Disease,AD)相關基因的發(fā)現(xiàn)研究。首先,在kohler等人構建的PPI網(wǎng)絡上進行預測分析,取得了跟kohler等人提出來的全局度量法隨機游走相同的效果。其次,基于劉等人提出來的腦特異網(wǎng)絡進行AD基因的預測,前46個分值最高的基因中,有40個與AD相關聯(lián)的基因,比劉等人的預測結果稍好。MTOMATOM方法復雜度低,運算速度快,并且對網(wǎng)絡的不完整性和連接的假陽性有較強的魯棒性。
[Abstract]:In recent years, the identification of disease-related genes has become one of the challenging tasks in life sciences. The traditional methods for predicting disease genes are linkage analysis (Linkage Analysis) and association study (Association Study). But linkage analysis can only locate a region of a chromosome that contains dozens to hundreds of genes. At the same time, association studies also need to identify candidate genes. Researchers from all over the world have proposed methods for further screening candidate genes in this region. With the completion of the Human Genome Project (Human Genome Project, HGP) and the production of high-throughput biotechnology, we have obtained large-scale human protein interaction data (Protein-Protein Interaction, PPI). Studies have shown that on PPI networks, proteins with high topological overlaps are more likely to belong to one biological functional module or biological pathway. Therefore, in this paper, we propose a method of disease candidate gene prediction based on PPI network, MTOMATOM. This method combines multi-point topology overlap method (Multi-node Topology Overlap Measure, MTOM) and two-point topological overlap method (Averaged Topology Overlap Measure, ATOM). MTOMATOM method) to reflect the similarity of network nodes by measuring the degree of topological overlap between nodes. It is a network distance measure that can reflect biological meaning more. The method was cross-validated by 50-fold method in 110 disease-gene families containing 783 genes. It was found that the enrichment reached 27-fold and the area under the roc curve was 92.3.The results were better than that of the similar methods. We applied the MTOMATOM method to the discovery of genes associated with Alzheimer's disease (Alzheimer's Disease,AD). First, the prediction analysis is carried out on the PPI network constructed by kohler et al., and the results are the same as the global metric proposed by kohler et al. Secondly, based on the brain-specific network proposed by Liu et al., 40 of the first 46 genes with the highest score are associated with AD, which is slightly better than that of Liu et al. the MTOMATOM method is less complex and faster. And it has strong robustness to the network imperfection and false positive connection.
【學位授予單位】：東北大學
【學位級別】：碩士
【學位授予年份】：2009
【分類號】：R346
本文編號：2268556
[Abstract]:In recent years, the identification of disease-related genes has become one of the challenging tasks in life sciences. The traditional methods for predicting disease genes are linkage analysis (Linkage Analysis) and association study (Association Study). But linkage analysis can only locate a region of a chromosome that contains dozens to hundreds of genes. At the same time, association studies also need to identify candidate genes. Researchers from all over the world have proposed methods for further screening candidate genes in this region. With the completion of the Human Genome Project (Human Genome Project, HGP) and the production of high-throughput biotechnology, we have obtained large-scale human protein interaction data (Protein-Protein Interaction, PPI). Studies have shown that on PPI networks, proteins with high topological overlaps are more likely to belong to one biological functional module or biological pathway. Therefore, in this paper, we propose a method of disease candidate gene prediction based on PPI network, MTOMATOM. This method combines multi-point topology overlap method (Multi-node Topology Overlap Measure, MTOM) and two-point topological overlap method (Averaged Topology Overlap Measure, ATOM). MTOMATOM method) to reflect the similarity of network nodes by measuring the degree of topological overlap between nodes. It is a network distance measure that can reflect biological meaning more. The method was cross-validated by 50-fold method in 110 disease-gene families containing 783 genes. It was found that the enrichment reached 27-fold and the area under the roc curve was 92.3.The results were better than that of the similar methods. We applied the MTOMATOM method to the discovery of genes associated with Alzheimer's disease (Alzheimer's Disease,AD). First, the prediction analysis is carried out on the PPI network constructed by kohler et al., and the results are the same as the global metric proposed by kohler et al. Secondly, based on the brain-specific network proposed by Liu et al., 40 of the first 46 genes with the highest score are associated with AD, which is slightly better than that of Liu et al. the MTOMATOM method is less complex and faster. And it has strong robustness to the network imperfection and false positive connection.
【學位授予單位】：東北大學
【學位級別】：碩士
【學位授予年份】：2009
【分類號】：R346
【共引文獻】
相關期刊論文 前2條
1 ;Biomarkers of Alzheimer’s disease in body fluids[J];Science China(Life Sciences);2010年04期
2 鄭妍鵬;何金生;洪濤;;阿爾茨海默病體液生物學標記物研究進展[J];中國科學(C輯:生命科學);2009年09期
，本文編號：2268556
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