2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)最新文献_第5页

An ERα/modulator regulatory network in the breast cancer cells 乳腺癌细胞中的ERα/调节剂调控网络

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) Pub Date : 2011-12-01 DOI: 10.1109/GENSiPS.2011.6169427

Heng-Yi Wu, Yu Wang, Pengyue Zheng, Guanglong Jiang, Yunlong Liu, T. Huang, K. Nephew, Lang Li

{"title":"An ERα/modulator regulatory network in the breast cancer cells","authors":"Heng-Yi Wu, Yu Wang, Pengyue Zheng, Guanglong Jiang, Yunlong Liu, T. Huang, K. Nephew, Lang Li","doi":"10.1109/GENSiPS.2011.6169427","DOIUrl":"https://doi.org/10.1109/GENSiPS.2011.6169427","url":null,"abstract":"Estrogens control multiple functions of hormone-responsive breast cancer (BC) cells [1]. They regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer [2]. However, ERα requires distinct co-regulator complex or modulators for efficient transcriptional regulation. To have insight into the regulatory network of ERα and discover the novel modulators of ERα which acted by distinct mechanisms, we proposed an analytical method based on a linear regression model to identify translational modulators and the relationship between genes for network. To comprehend the network associated with ERα, a dynamic gene expression profile and ChIP-Seq data shown to characterize the breast cancer cell response to estrogens are utilized. The role of modulators within molecular mechanism can be learned from the exploration of these two data sets. Based on the active or repressive function of the ERα, active or repressive function of a modulator, and agonist or antagonist effect of a modulator on the ERα, the ERα/modulator/target relationships were categorized into 27 classes.","PeriodicalId":181666,"journal":{"name":"2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127830250","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

Modeling and systematic analysis of LC-MS proteomics pipeline LC-MS蛋白质组学流水线建模与系统分析

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) Pub Date : 2011-12-01 DOI: 10.1109/GENSiPS.2011.6169457

Youting Sun, U. Braga-Neto, E. Dougherty

引用次数: 2

Beyond seed match: Improving miRNA target prediction using PAR-CLIP data 超越种子匹配:利用PAR-CLIP数据改进miRNA靶标预测

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) Pub Date : 2011-12-01 DOI: 10.1109/GENSiPS.2011.6169461

M. Lu, C. L. P. Chen, Yufei Huang

{"title":"Beyond seed match: Improving miRNA target prediction using PAR-CLIP data","authors":"M. Lu, C. L. P. Chen, Yufei Huang","doi":"10.1109/GENSiPS.2011.6169461","DOIUrl":"https://doi.org/10.1109/GENSiPS.2011.6169461","url":null,"abstract":"Since miRNA plays an important role in post-transcript regulation, many computational approaches have been proposed for miRNA target prediction. Yet, the existing algorithms lack the capability to predict the true target when the perfect seed match presents in mRNA sequences and methods based on seed-match still suffer from a high false positive rate. Therefore, this paper proposes a new prediction method that exploits the data produced by the PAR-CLIP, which is a recent high throughput, high precision technology for genome-wide miRNA targets. This algorithm searches true miRNA targets among the candidates with seed-matches by using machine learning approaches. The target prediction results on top 20 expressed miRNAs in HEK293 cells of AGO1-4 proteins PAR-CLIP data show that given presence of seed pairing, the proposed method greatly outperforms the traditional miRNA target prediction algorithms and improve the precision significantly. Because biologists usually need to mutate the seed region to validation the miRNA targets, and only capable of conducting biological experiments on limited miRNA and mRNA sequences due to the time and cost, the proposed approach will make significant impact on the biology and healthcare fields.","PeriodicalId":181666,"journal":{"name":"2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123892593","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

Computational prediction of microRNA regulatory pathways microRNA调控途径的计算预测

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) Pub Date : 2011-12-01 DOI: 10.1109/GENSiPS.2011.6169469

Dong Yue, Yidong Chen, Shou-Jiang Gao, Yufei Huang

引用次数: 1

Identifying genes associated with chemotherapy response in ovarian carcinomas based on DNA copy number and expression profiles 基于DNA拷贝数和表达谱鉴定卵巢癌化疗反应相关基因

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) Pub Date : 2011-12-01 DOI: 10.1109/GENSiPS.2011.6169438

Fang-Han Hsu, E. Serpedin, T. Hsiao, A. Bishop, E. Dougherty, Yidong Chen

{"title":"Identifying genes associated with chemotherapy response in ovarian carcinomas based on DNA copy number and expression profiles","authors":"Fang-Han Hsu, E. Serpedin, T. Hsiao, A. Bishop, E. Dougherty, Yidong Chen","doi":"10.1109/GENSiPS.2011.6169438","DOIUrl":"https://doi.org/10.1109/GENSiPS.2011.6169438","url":null,"abstract":"DNA copy number alterations (CNAs) may change transcription profiles and are reported to be associated with chemotherapy response. Using a recently concluded ovarian cancer study derived from the Cancer Genome Atlas (TCGA) Research Network, we selected 98 ovarian cancer samples derived from patients who were only treated with Paclitaxel/Carboplatin after the surgery. A statistical testing procedure was proposed to examine the genes with CNAs and correlated changes in expression level, and their associated response to chemotherapy in progression-free survival. Among 12,042 genes under consideration, 112 genes with CNAs and correlated gene expression levels were found to be associated with progression-free survival (PFS) significantly. The region containing many selected genes, 1p35.1–1p34.2, is closely examined as a candidate segment where CNAs are significantly associated with chemotherapeutic response to Paclitaxel/Carboplatin. Biological processes and molecular functions associated with chemotherapy response were further proposed based on a gene ontology enrichment analysis.","PeriodicalId":181666,"journal":{"name":"2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132095768","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: 2

Finding effective subnetwork markers for cancer by passing messages 通过传递信息找到有效的癌症子网络标记

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) Pub Date : 2011-12-01 DOI: 10.1109/GENSiPS.2011.6169452

Byung-Jun Yoon

引用次数: 0

Modeling cyclic and acyclic therapeutic methods with persistent intervention effect in probabilistic Boolean networks 概率布尔网络中具有持续干预效果的循环和非循环治疗方法建模

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) Pub Date : 2011-12-01 DOI: 10.1109/GENSiPS.2011.6169436

Mohammadmahdi R. Yousefi, A. Datta, E. Dougherty

引用次数: 0

Uncertainty-based essentiality in gene regulatory networks 基因调控网络中基于不确定性的本质

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) Pub Date : 2011-12-01 DOI: 10.1109/GENSiPS.2011.6169430

Xiaoning Qian, Byung-Jun Yoon, E. Dougherty

{"title":"Uncertainty-based essentiality in gene regulatory networks","authors":"Xiaoning Qian, Byung-Jun Yoon, E. Dougherty","doi":"10.1109/GENSiPS.2011.6169430","DOIUrl":"https://doi.org/10.1109/GENSiPS.2011.6169430","url":null,"abstract":"In this paper, we propose a definition for the essentiality of regulatory relationships among molecules in a Boolean network model, which takes the regulatory relationships between the molecules into account, in addition to their connectivity. The proposed definition of essentiality is tightly related to the ultimate goal of designing intervention strategies to achieve beneficial dynamic changes in the network. Focusing on Boolean networks, we define the essentiality of each regulatory relationship as the difference between the expected performance of the Bayesian robust structural intervention over the uncertainty class of networks, which arises from the uncertainty in the given regulatory relationship, and the performance of the optimal structural intervention for the known network in which there is no uncertainty. For a specific regulatory relationship, a large difference in performance implies that the given relationship is critical for designing effective therapeutic strategies. On the other hand, small difference implies that the regulatory relationship under consideration may not be crucial in designing intervention strategies. This new definition of essentiality, grounded on the quantification of uncertainty in network dynamics, may provide a deep understanding of the robustness, adaptability, and controllability of gene regulatory networks.","PeriodicalId":181666,"journal":{"name":"2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134186130","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: 0

Identification of biomarkers in breast cancer metastasis by integrating protein-protein interaction network and gene expression data 结合蛋白-蛋白相互作用网络和基因表达数据鉴定乳腺癌转移的生物标志物

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) Pub Date : 2011-12-01 DOI: 10.1109/GENSiPS.2011.6169443

M. Jahid, Jianhua Ruan

{"title":"Identification of biomarkers in breast cancer metastasis by integrating protein-protein interaction network and gene expression data","authors":"M. Jahid, Jianhua Ruan","doi":"10.1109/GENSiPS.2011.6169443","DOIUrl":"https://doi.org/10.1109/GENSiPS.2011.6169443","url":null,"abstract":"Identification of biomarkers for breast cancer metastasis is a well studied problem. Recently, several large-scale studies used gene expression data to identify markers related to metastatic process. However, it was shown that these gene expression based markers often have low reproducibility across different data sets, for a number of reasons. These include small sample sizes compared to the number of genes, gene expression variations between individuals that do not contribute to the metastasis process, and the limitation for microarray technology being unable to detect changes beyond transcriptional level. Here a graph-theoretical approach based on the topology of protein-protein interaction (PPI) networks is proposed for biomarker discovery. The idea is to identify a set of genes that give connectivity to differentially expressed (DE) genes in a PPI network, based on the key observation that biomarkers may provide functional linkage to DE genes in PPI networks. Our approach is applied to two breast cancer microarray datasets for biomarker discovery. Those biomarkers have a significant number of known cancer susceptibility genes among them and are significantly enriched in biological processes and pathways that are involved in carcinogenic process. Furthermore, markers selected by our method have a higher stability across the two datasets than in the previous studies. Therefore, the approach described in this study is a new way to identify novel biomarkers for cancer metastasis and can potentially improve the understanding of carcinogenesis dynamics.","PeriodicalId":181666,"journal":{"name":"2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123587510","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: 12

Multiple reaction monitoring: Modeling and systematic analysis 多反应监测:建模与系统分析

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) Pub Date : 2011-12-01 DOI: 10.1109/GENSiPS.2011.6169455

E. Atashpaz-Gargari, U. Braga-Neto, E. Dougherty

引用次数: 0