Ebook Cancer systems biology: Part 2
➤ Gửi thông báo lỗi ⚠️ Báo cáo tài liệu vi phạmNội dung chi tiết: Ebook Cancer systems biology: Part 2
Ebook Cancer systems biology: Part 2
CHAPTER 11Cancer Gene PredictionUsing a Network ApproachXuebing Wu and Shao LiCONTENTS11.1Introduction19111.2Molecular Networks and Human Diseases1921 Ebook Cancer systems biology: Part 211.3Network Approach for Cancer Gene Prediction19511.3.1 Prioritize by Nel work Proximil y19611.3.1.1Proximity to Known Disease Genes of the Same Disease 19611.3.1.2Proximity of Candidate Gene Pairs:Enabling de Novo Discovery20011.3.2Phenotype Similarity-Assisted Methods20011.3.2.1Calculating and Va Ebook Cancer systems biology: Part 2lidating Phenotypic Similarity20011.3.2.2Modeling with Molecular Network and Phenotype Similarity 20211.3.3Prioritize by Network Centrality20511.3.3.1Ebook Cancer systems biology: Part 2
Centrality in a Context-Specific Gene Network20511.3.3.2Centrality in a Genomic-Phenomic Network20511.3.4Other Methods20611.4Discussion207AcknowledgmeCHAPTER 11Cancer Gene PredictionUsing a Network ApproachXuebing Wu and Shao LiCONTENTS11.1Introduction19111.2Molecular Networks and Human Diseases1921 Ebook Cancer systems biology: Part 2s of research in molecular genetics have identified a number of important genes responsible for the genesis of various types of cancer (Futreal et al. 2004) and drugs targeting these mutated cancer genes have brought dramatic therapeutic advances and substantially improved and prolonged the lives of Ebook Cancer systems biology: Part 2 cancer patients (Huang and Harari 1999). However, cancer is extremely complex and heterogeneous. It has been suggested that 5% to 10% of the human geEbook Cancer systems biology: Part 2
nes probably contribute to oncogenesis (Strausberg, Simpson, and Wooster 2003), while current experimentally validated cancer genes only cover 1% of hCHAPTER 11Cancer Gene PredictionUsing a Network ApproachXuebing Wu and Shao LiCONTENTS11.1Introduction19111.2Molecular Networks and Human Diseases1921 Ebook Cancer systems biology: Part 2 to be identified. For example, in breast cancer, known susceptibility genes, including BRCA1 (Miki et al. 1994) and BRCA2 (Wooster et al. 1995), can only explain less than 5% of the total breast cancer incidence and less than 25% of the familial risk (Oldenburg et al. 2007). The same challenge is a Ebook Cancer systems biology: Part 2lso faced by other types of cancer and other complex diseases, such as diabetes (Frayling 2007) and many brain diseases (Burmeister, McInnis, and ZollEbook Cancer systems biology: Part 2
ner 2008; Folstcin and Rosen-Sheidley 2001). There is a long way to go from changes in genetic sequence to visible clinical phenotypes. The complex moCHAPTER 11Cancer Gene PredictionUsing a Network ApproachXuebing Wu and Shao LiCONTENTS11.1Introduction19111.2Molecular Networks and Human Diseases1921 Ebook Cancer systems biology: Part 2p between genes and diseases. This high complexity and low penetrance might explain why so many disease genes remain unidentified.Traditional gene mapping approaches, such as linkage analysis and association studies, have limited resolution to localize the causal genes in the genome, and the resulta Ebook Cancer systems biology: Part 2nt region often contains hundreds of candidate genes (Altshuler, Daly, and Lander 2008). The functional testing and validation of causative genes areEbook Cancer systems biology: Part 2
time consuming and laborious. I he priority of candidate genes is usually determined by expert judgment based on the gene’s known functions (Pharoah CCHAPTER 11Cancer Gene PredictionUsing a Network ApproachXuebing Wu and Shao LiCONTENTS11.1Introduction19111.2Molecular Networks and Human Diseases1921 Ebook Cancer systems biology: Part 2enomics, proteomics, and cpigcnomics data, computational methods arc exploited to predict and prioritize disease genes (Oli and Brunner 2007; Zhu and Zhao 2007), significantly reducing the number of candidate genes for further testing. Computational prediction and prioritization is complementary to Ebook Cancer systems biology: Part 2genetic mapping, in terms of integrating existing knowledge on disease biology and relatively- unbiased whole genome measurements.More recently, largeEbook Cancer systems biology: Part 2
-scale molecular interaction network data have become available, and it turns out to be particularly powerful for disease gene prediction when used alCHAPTER 11Cancer Gene PredictionUsing a Network ApproachXuebing Wu and Shao LiCONTENTS11.1Introduction19111.2Molecular Networks and Human Diseases1921 Ebook Cancer systems biology: Part 2 al. 2008). Molecular interaction networks depict the basic skeleton of cellular processes, and network analysis has the ability to model the complex interactions among multiple genes and their higher-level organizations (Barabasi and Oltvai 2004; I Ian 2008; Zhu, Gerstein, and Snyder 2007). In this Ebook Cancer systems biology: Part 2 chapter, we will focus on network-based approaches for cancer gene prediction. Many of the methods discussed here arc designed for general disease inEbook Cancer systems biology: Part 2
stead of cancer. Nonetheless, they can be applied to predict cancer genes as a special case, and most of these network-based methods have been demonstCHAPTER 11Cancer Gene PredictionUsing a Network ApproachXuebing Wu and Shao LiCONTENTS11.1Introduction19111.2Molecular Networks and Human Diseases1921 Ebook Cancer systems biology: Part 2o the details of network-based gene prioritization methods, we will briefly introduce some basic concepts about molecular networks, the data sources and tools for building networks, and the working principles for network approaches in predicting disease genes.Network is a simple but efficient abstra Ebook Cancer systems biology: Part 2ction of biological systems (Barabasi and Oltvai 2004). Nodes/vert ices in a molecular network represent biomolecules, such as genes, proteins, and meEbook Cancer systems biology: Part 2
tabolites. Edges/links between nodes indicate physical or functional interactions, including transcriptional binding, protein-protein interaction, genCHAPTER 11Cancer Gene PredictionUsing a Network ApproachXuebing Wu and Shao LiCONTENTS11.1Introduction19111.2Molecular Networks and Human Diseases1921 Ebook Cancer systems biology: Part 2 network (if it happens in the cell) shows that two molecules are functionally related to each other, and the distance on a network is correlated with functional similarity (Sharan, Ulitsky, and Shamir 2007). Network/graph theory provides multiple definitions and tools to measure the distance/proxim Ebook Cancer systems biology: Part 2ity between two nodes on a network, which makes network analysis particularly suitable to the quantitative modeling of gene-gene and gene-disease relaEbook Cancer systems biology: Part 2
tionships (see Box 11.1 for basic graph concepts).BOX 11.1 BASIC GRAPH CONCEPTSA graph is a pair G(V,L), where V is a set of nodes (or vertices) and LCHAPTER 11Cancer Gene PredictionUsing a Network ApproachXuebing Wu and Shao LiCONTENTS11.1Introduction19111.2Molecular Networks and Human Diseases1921 Ebook Cancer systems biology: Part 2es or proteins, and the edges represent interactions such as protein-protein interaction, transcriptional binding between protein and UNA.A graph can lx; reprcĩsentcxl by an adjacent matrix A, where Aịị - 1 if there is an edge between nodes i and j; otherwise A;.: - 0. Ebook Cancer systems biology: Part 2CHAPTER 11Cancer Gene PredictionUsing a Network ApproachXuebing Wu and Shao LiCONTENTS11.1Introduction19111.2Molecular Networks and Human Diseases1921Gọi ngay
Chat zalo
Facebook