Handbook of Research on Computational and Systems Biology最新文献_第2页

Linking Interactome to Disease: A Network-Based Analysis of Metastatic Relapse in Breast Cancer 将相互作用组与疾病联系起来:基于网络的乳腺癌转移性复发分析

Handbook of Research on Computational and Systems Biology Pub Date : 1900-01-01 DOI: 10.4018/978-1-60960-491-2.ch019

M. Garcia, Olivier Stahl, P. Finetti, D. Birnbaum, F. Bertucci, Ghislain Bidaut

{"title":"Linking Interactome to Disease: A Network-Based Analysis of Metastatic Relapse in Breast Cancer","authors":"M. Garcia, Olivier Stahl, P. Finetti, D. Birnbaum, F. Bertucci, Ghislain Bidaut","doi":"10.4018/978-1-60960-491-2.ch019","DOIUrl":"https://doi.org/10.4018/978-1-60960-491-2.ch019","url":null,"abstract":"The introduction of high-throughput gene expression profiling technologies (DNA microarrays) in molecular biology and their expected applications to the clinic have allowed the design of predictive signatures linked to a particular clinical condition or patient outcome in a given clinical setting. However, it has been shown that such signatures are prone to several problems: (i) they are heavily unstable and linked to the set of patients chosen for training; (ii) data topology is problematic with regard to the data dimensionality (too many variables for too few samples); (iii) diseases such as cancer are provoked by subtle misregulations which cannot be readily detected by current analysis methods. To find a predictive signature generalizable for multiple datasets, a strategy of superimposition of a large scale of proteinprotein interaction data (human interactome) was devised over several gene expression datasets (a total of 2,464 breast cancer tumors were integrated), to find discriminative regions in the interactome (subnetworks) predicting metastatic relapse in breast cancer. This method, Interactome-Transcriptome Integration (ITI), was applied to several breast cancer DNA microarray datasets and allowed the extraction of a signature constituted by 119 subnetworks. All subnetworks have been stored in a relational database and linked to Gene Ontology and NCBI EntrezGene annotation databases for analysis. Exploration of annotations has shown that this set of subnetworks reflects several biological processes linked to cancer and is a good candidate for establishing a network-based signature for prediction of metastatic relapse in breast cancer.","PeriodicalId":254251,"journal":{"name":"Handbook of Research on Computational and Systems Biology","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130750947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Unsupervised Methods to Identify Cellular Signaling Networks from Perturbation Data 从扰动数据中识别蜂窝信号网络的无监督方法

Handbook of Research on Computational and Systems Biology Pub Date : 1900-01-01 DOI: 10.4018/978-1-60960-491-2.ch015

Madhusudan Natarajan

{"title":"Unsupervised Methods to Identify Cellular Signaling Networks from Perturbation Data","authors":"Madhusudan Natarajan","doi":"10.4018/978-1-60960-491-2.ch015","DOIUrl":"https://doi.org/10.4018/978-1-60960-491-2.ch015","url":null,"abstract":"The inference of cellular architectures from detailed time-series measurements of intracellular variables is an active area of research. High throughput measurements of responses to cellular perturbations are usually analyzed using a variety of machine learning methods that typically only work within one type of measurement. Here, summaries of some recent research attempts are presented–these studies have expanded the scope of the problem by systematically integrating measurements across multiple layers of regulation including second messengers, protein phosphorylation markers, transcript levels, and functional phenotypes into signaling vectors or signatures of signal transduction. Data analyses through simple unsupervised methods provide rich insight into the biology of the underlying network, and in some cases reconstruction of key architectures of the underlying network from perturbation data. The methodological advantages provided by these efforts are examined using data from a publicly available database of responses to systematic perturbations of cellular signaling networks generated by the Alliance for Cellular Signaling (AfCS). DOI: 10.4018/978-1-4666-3604-0.ch030","PeriodicalId":254251,"journal":{"name":"Handbook of Research on Computational and Systems Biology","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127739650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Modules in Biological Networks: Identification and Application 生物网络中的模块:识别与应用

Handbook of Research on Computational and Systems Biology Pub Date : 1900-01-01 DOI: 10.4018/978-1-60960-491-2.ch011

Bing Zhang, Zhiao Shi

引用次数: 5

Pathway Resources at the Rat Genome Database: A Dynamic Platform for Integrating Gene, Pathway and Disease Information 大鼠基因组数据库中的通路资源:一个整合基因、通路和疾病信息的动态平台

Handbook of Research on Computational and Systems Biology Pub Date : 1900-01-01 DOI: 10.4018/978-1-60960-491-2.ch014

V. Petri

引用次数: 4

Knowledge-Driven, Data-Assisted Integrative Pathway Analytics 知识驱动、数据辅助的综合路径分析

Handbook of Research on Computational and Systems Biology Pub Date : 1900-01-01 DOI: 10.4018/978-1-60960-491-2.ch010

P. Reddy, Stuart Murray, W. Liu

引用次数: 2

Complexity and Modularity of MAPK Signaling Networks MAPK信令网络的复杂性和模块化

Handbook of Research on Computational and Systems Biology Pub Date : 1900-01-01 DOI: 10.4018/978-1-60960-491-2.ch016

George V. Popescu, Sorina C. Popescu

引用次数: 5

Structural Alignment of RNAs with Pseudoknots 带有假结的rna的结构定位

Handbook of Research on Computational and Systems Biology Pub Date : 1900-01-01 DOI: 10.4018/978-1-60960-491-2.ch024

Thomas K. F. Wong, S. Yiu

{"title":"Structural Alignment of RNAs with Pseudoknots","authors":"Thomas K. F. Wong, S. Yiu","doi":"10.4018/978-1-60960-491-2.ch024","DOIUrl":"https://doi.org/10.4018/978-1-60960-491-2.ch024","url":null,"abstract":"Non-coding RNAs (ncRNAs) are found to be critical for many biological processes. However, identifying these molecules is very difficult and challenging due to the lack of strong detectable signals such as opening read frames. Most computational approaches rely on the observation that the secondary structures of ncRNA molecules are conserved within the same family. Aligning a known ncRNA to a target candidate to determine the sequence and structural similarity helps in identifying de novo ncRNA molecules that are in the same family of the known ncRNA. However, the problem becomes more difficult if the secondary structure contains pseudoknots. Only until recently, many of the existing approaches could not handle structures with pseudoknots. This chapter reviews the state-of-the-art algorithms for different types of structures that contain pseudoknots including standard pseudoknot, simple non-standard pseudoknot, recursive standard pseudoknot, and recursive simple non-standard pseudoknot. Although none of the algorithms is designed for general pseudoknots, these algorithms already cover all known ncRNAs in both Rfam and PseudoBase databases. The evaluation of the algorithms also shows that the approach is useful in identifying ncRNA molecules in other species, which are in the same family of a known ncRNA. DOI: 10.4018/978-1-60960-491-2.ch024","PeriodicalId":254251,"journal":{"name":"Handbook of Research on Computational and Systems Biology","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124018757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Current Omics Technologies in Biomarker Discovery 生物标志物发现中的组学技术

Handbook of Research on Computational and Systems Biology Pub Date : 1900-01-01 DOI: 10.4018/978-1-60960-491-2.ch004

Wei Ding, Ping Qiu, Yan-Hui Liu, W. Feng

引用次数: 3

Using Functional Linkage Gene Networks to Study Human Diseases 利用功能连锁基因网络研究人类疾病

Handbook of Research on Computational and Systems Biology Pub Date : 1900-01-01 DOI: 10.4018/978-1-60960-491-2.ch012

B. Linghu, Guohui Liu, Yu Xia

引用次数: 1

Virtual Screening: An Overview on Methods and Applications 虚拟筛选:方法与应用综述

Handbook of Research on Computational and Systems Biology Pub Date : 1900-01-01 DOI: 10.4018/978-1-60960-491-2.ch002

K. Barakat, Jonathan Y. Mane, J. Tuszynski

引用次数: 8