{"title":"Clustering DNA methylation expressions using nonparametric beta mixture model","authors":"Lin Zhang, Jia Meng, Hui Liu, Yufei Huang","doi":"10.1109/GENSiPS.2011.6169472","DOIUrl":"https://doi.org/10.1109/GENSiPS.2011.6169472","url":null,"abstract":"The problem of defining the clustering structure in DNA methylation expressions is considered. A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation data array. The model allows automatic learning of the cluster structure parameters such as the cluster mixing proportion, the models of each cluster, and especially the number of clusters. To enable the learning, we proposed a Gibbs sampling algorithm for computing the posterior distributions, hence the estimates of the parameters. We investigate the performance of the proposed clustering algorithm via simulation.","PeriodicalId":181666,"journal":{"name":"2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"36 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":"127404496","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}
Xiangfang Li, Lijun Qian, M. Bittner, E. Dougherty
{"title":"Assessing the efficacy of molecularly targeted agents by using Kalman filter","authors":"Xiangfang Li, Lijun Qian, M. Bittner, E. Dougherty","doi":"10.1109/GENSiPS.2011.6169439","DOIUrl":"https://doi.org/10.1109/GENSiPS.2011.6169439","url":null,"abstract":"A novel preclinical model combining experimental methods and theoretical analysis is proposed to investigate the mechanism of action and identify pharmacodynamic characteristic of a drug. Instead of fixed time point analysis of the drug exposure to drug effect, the time course of drug effect for different doses are quantitatively studied on cell line-based platforms using Kalman filter, where tumor cells' responses to drugs through the use of fluorescent reporters are sampled frequently over a time-course. It is expected that such preclinical study will provide valuable suggestions about dosing regimens for in vivo experimental stage to increase productivity.","PeriodicalId":181666,"journal":{"name":"2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"1 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":"131037348","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}
{"title":"Steady state probability approximation applied to stochastic model of biological network","authors":"Md. Shahriar Karim, David M. Umulis, G. Buzzard","doi":"10.1109/GENSiPS.2011.6169442","DOIUrl":"https://doi.org/10.1109/GENSiPS.2011.6169442","url":null,"abstract":"The Steady State (SS) probability distribution for the Chemical Master Equation (CME) is an important quantity used to characterize many biological systems. In this paper, we propose a comparatively easy, yet efficient and accurate, way of finding the SS distribution assuming the existence of a unique deterministic SS (unimodal) of the system. In order to find the approximate SS, we first use the truncated-state space representation to reduce the system to a finite dimension, and subsequently reformulate an eigenvalue problem into a linear system. To demonstrate the utility of the approach, we apply the method and determine the SS probability distribution to quantify the parameter dependency of surface-associated BMP binding proteins (SBPs) in the regulation of BMP mediated signaling and pattern formation.","PeriodicalId":181666,"journal":{"name":"2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"7 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":"134424257","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}
{"title":"Effects of slice thickness filter in filtered backprojection reconstruction with the parallel breast tomosynthesis imaging configuration","authors":"L. Cong, Ying Chen, Weihua Zhou","doi":"10.1109/GENSiPS.2011.6169478","DOIUrl":"https://doi.org/10.1109/GENSiPS.2011.6169478","url":null,"abstract":"Digital breast tomosynthesis is a novel breast cancer detection technique by allowing the reconstruction of arbitrary planes in the breast from a set of limited-angle projection images acquired at different view angles with a particular tube geometry setup. In this paper, filtered backprojection (FBP) was optimized as the reconstruction method with a parallel imaging breast tomosynthesis system. The slice thickness filter (profile filter) with task-adapted parameters was applied to computer simulated data, in order to investigate the main effects of this kind of filter regarding spatial resolution and artifacts in the reconstructed results.","PeriodicalId":181666,"journal":{"name":"2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"213 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":"134029032","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}
{"title":"Chromatin signature analysis and prediction of genome-wide novel promoters using finite mixture model","authors":"C. Taslim, Shili Lin, Kun Huang, T. Huang","doi":"10.1109/GENSiPS.2011.6169429","DOIUrl":"https://doi.org/10.1109/GENSiPS.2011.6169429","url":null,"abstract":"Regulation of gene expression has been shown to involve not only binding of transcription factor in target gene promoters but also characterization of histone around which DNA is wrapped around. Some histone modification, for example di-methylated histone H3 at lysine 4 (H3K4me2), has been shown to be associated with gene activation. However, no clear pattern has been shown to predict human promoters. This paper proposed a novel quantitative approach to characterize chromatin signature and patterns of promoters, which are then used to predict novel (alternative) promoters. In this paper, chromatin immunoprecipitation methods followed by massive parallel sequencing (ChIP-seq) data against RNA Polymerase II (Pol II) and H3K4me2 are used to identify common patterns of promoter regions. These patterns were then used to search for similar patterns over the entire genome to find novel promoters. Common patterns of promoter regions are modeled using a mixture model involving double-exponential and uniform distributions. Regions with high correlations with the common patterns are identified as putative novel promoters. We used this proposed algorithm and RNA-seq data to identify novel promoters in the MCF7 cell line. We found 4,392 high-confidence regions that display the identified promoter patterns (referred to as putative novel promoters). Of these, 875 regions (20%) overlap with RNA transcripts. Around 70% of these putative novel promoters have overlapped with RNA transcripts, EST and/or non-coding RNA suggesting that these putative novel promoters might be promoters which are currently undiscovered.","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":"125498527","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}
{"title":"A novel approach for tumor sensitivity prediction and combination therapy design for targeted drugs","authors":"Noah E. Berlow, R. Pal","doi":"10.1109/GENSiPS.2011.6169435","DOIUrl":"https://doi.org/10.1109/GENSiPS.2011.6169435","url":null,"abstract":"Drugs that target specific kinases are becoming common in cancer research. Cancer-related kinases are the paradigm of molecularly-targeted therapies, and a cornerstone of Personalized Cancer Therapy. Here, we present an approach to generate abstract circuit representations of cancer pathways from drug tumor sensitivity data of a cell line and utilize them to predict the sensitivities of a new drug given the kinase inhibitors of the drug and to design and measure the effectiveness of drug combination therapies.","PeriodicalId":181666,"journal":{"name":"2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"13 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114020264","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}
Man-Hung Eric Tang, V. Varadan, S. Kamalakaran, Michael Q. Zhang, N. Dimitrova, J. Hicks
{"title":"A method for finding novel associations between genome-wide copy number and dna methylation patterns","authors":"Man-Hung Eric Tang, V. Varadan, S. Kamalakaran, Michael Q. Zhang, N. Dimitrova, J. Hicks","doi":"10.1109/GENSiPS.2011.6169448","DOIUrl":"https://doi.org/10.1109/GENSiPS.2011.6169448","url":null,"abstract":"We present a computational method that combines genome-wide DNA methylation and copy number variation data in an integrated fashion with the aim of finding mechanistic associations between genome instability and local DNA methylation changes. The method is applied to Luminal A breast cancer early-stage tumour samples and focuses on methylation events occurring at frequently rearranged genome locations. Our method accommodates array and sequencing platforms for methylation and DNA copy number estimates. We find significant local methylation changes in tumours tend to occur in the viscinity of breakpoint rich regions, with 80% of the differentially methylated regions occurring within 2Mb from a breakpoint rich locus.","PeriodicalId":181666,"journal":{"name":"2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127269045","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}
A. Janevski, V. Varadan, S. Kamalakaran, N. Banerjee, N. Dimitrova
{"title":"Comparative copy number variation from whole genome sequencing","authors":"A. Janevski, V. Varadan, S. Kamalakaran, N. Banerjee, N. Dimitrova","doi":"10.1109/GENSiPS.2011.6169460","DOIUrl":"https://doi.org/10.1109/GENSiPS.2011.6169460","url":null,"abstract":"Whole genome sequencing enables a high resolution view of the human genome and enables unique insights into copy number variations on an unprecedented scale. Numerous tools and studies have already been introduced that provide confirmatory evidence and new genomic structure variation data in individuals as well as across populations. We utilize two such tools, CNV-seq and FREEC to compare their outputs when applied to five whole genome sequences representing four populations. We focus on the ability of these tools to detect segments from two sets of segments known to vary across populations, and discuss the direction and the challenges in developing tools that detect copy number variation in collections of human genomes.","PeriodicalId":181666,"journal":{"name":"2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115406501","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}
Mark Doderer, Zachry Anguiano, U. Suresh, Ravi Dashnamoorthy, A. Bishop, Yidong Chen
{"title":"Multisource biological pathway consolidation","authors":"Mark Doderer, Zachry Anguiano, U. Suresh, Ravi Dashnamoorthy, A. Bishop, Yidong Chen","doi":"10.1109/GENSiPS.2011.6169447","DOIUrl":"https://doi.org/10.1109/GENSiPS.2011.6169447","url":null,"abstract":"A typical method to discover phenotypic descriptions of an ordered set of differential gene expressions is to identify pathway enrichments. There are many pathways that are highly related or maybe redundant across different databases making their consolidation an essential step when interpreting these results. Two methods of pathway consolidation are explored, one utilizes the gene set of the most enriched pathway to find similar pathways also enriched in a given experiment. The other method uses only the gene members in each pathway, this finds de novo pathway clusters independent of any given experiment. Unique consolidation results from both methods are presented, demonstrating their applications in biological studies.","PeriodicalId":181666,"journal":{"name":"2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)","volume":"85 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":"124133140","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}