Relational database index choices for genome annotation data

Karpenko, Oleksiy; Dai, Yang

doi:10.1109/bibmw.2010.5703810

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…The focus of the second group of integration studies, which we call biological integration studies, is the analysis of biological pathways and regulatory mechanisms among data obtained from different platforms, such as the relationship between gene expression and protein abundances, or the relationship between gene expression and copy number changes in patient tumor samples (Karpenko and Dai, 2010; van Wieringen et al. , 2012; Waters et al.…”

Section: Introductionmentioning

confidence: 99%

iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data

et al. 2012

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Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current data integration approaches are limited in that they do not consider the fundamental biological relationships that exist among the data obtained from different platforms.Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses hierarchical modeling to combine the data obtained from multiple platforms into one model.Results: We assess the performance of our methods using several synthetic and real examples. Simulations show our integrative methods to have higher power to detect disease-related genes than non-integrative methods. Using the Cancer Genome Atlas glioblastoma dataset, we apply the iBAG model to integrate gene expression and methylation data to study their associations with patient survival. Our proposed method discovers multiple methylation-regulated genes that are related to patient survival, most of which have important biological functions in other diseases but have not been previously studied in glioblastoma.Availability: http://odin.mdacc.tmc.edu/∼vbaladan/. Contact: veera@mdanderson.org Supplementary information: Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data

et al. 2012

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“…We rely on an additive regression model that accommodates potential nonlinear functional relationships to detect a typically small subset of the K cluster representatives as the regression predictors. By applying extensions of the spike-and-slab approaches (Brown et al, 1998;George and McCulloch, 1993;Karpenko and Dai, 2010), the additive regression model is y i indep ∼ N (η i , τ 2 ), where…”

Section: Models For Survival Outcomes and Gene Selectionmentioning

confidence: 99%

A Clustering Approach to Integrative Analysis of Multiomic Cancer Data

Yan¹,

Guha²

2022

Preprint

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Rapid technological advances have allowed for molecular profiling across multiple omics domains from a single sample for clinical decision making in many diseases, especially cancer. As tumor development and progression are dynamic biological processes involving composite genomic aberrations, key challenges are to effectively assimilate information from these domains to identify genomic signatures and biological entities that are druggable, develop accurate risk prediction profiles for future patients, and identify novel patient subgroups for tailored therapy and monitoring. We propose integrative probabilistic frameworks for high-dimensional multiple-domain cancer data that coherently incorporate dependence within and between domains to accurately detect tumor subtypes, thus providing a catalogue of genomic aberrations associated with cancer taxonomy. We propose an innovative, flexible and scalable Bayesian nonparametric framework for simultaneous clustering of both tumor samples and genomic probes. We describe an efficient variable selection procedure to identify relevant genomic aberrations that can potentially reveal underlying drivers of a disease. Although the work is motivated by several investigations related to lung cancer, the proposed methods are broadly applicable in a variety of contexts involving high-dimensional data. The success of the methodology is demonstrated using artificial data and lung cancer omics profiles publicly available from The Cancer Genome Atlas.

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“…Exponential growth in the volume of genome annotation data has resulted in several recent genomics techniques employed in systems biology. To this effect, Karpenko & Dai [18] consider the conventional B-tree, R-tree, and no index options to effectively index annotation data in MySQL, Oracle, and PostgreSQL databases. Through experimentations, the R-tree solution is observed to outperform the B-tree based solution, and thus is a potential indexing structure for supporting and organizing genome annotation databases.…”

Section: A Work That Adopt the R-based Indexing Structuresmentioning

confidence: 99%

“…Consequently, applications with high-dimensional data are in dire need of an efficient mechanism to organize their data [6], [7], [10], [15], [17], [24], [25], [31]- [33], [37], [39], [40], [42]. Nevertheless, the quest for processing high-dimensional data has resulted in a number of innovative indexing techniques which are found useful in many applications, like Geographical Information Systems (GIS), robotics, environmental protection, metric spaces, medical imaging, and geosciences, as they are geometrically suited for both point and spatial data [2], [3], [5]- [7], [9], [11], [12], [14], [16], [18], [24], [28], [30], [41], [43], [46].…”

Section: Introductionmentioning

confidence: 99%

Analyses of Indexing Techniques on Uncertain Data With High Dimensionality

et al. 2020

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Deploying a solution for handling critical decision-based problem efficiently requires the processing of high-dimensional data. Over the years, due to modern technological advancement, unprecedented volume of uncertain data is been captured and this has necessitated the need to organize such data for better data access performance. To this effect, the use of indexing technique for supporting, organizing, and storing of uncertain data with high dimensionality has become pertinent. However, the choice of an indexing technique to improve search performance is highly influenced by the properties of the underlying data set, data construction methods employed by the indexing structure, and the query types it supports. This paper is motivated to conduct an extensive performance analysis among existing indexing techniques, namely: R-tree, R*-tree and X -tree, in order to realize the most efficient indexing structure for organizing, storing and ultimately improving search performance over uncertain data with high dimensionality. The results of the analyses with regard to CPU processing time and number of nodes visited clearly show the superiority of X -tree over R-tree and R*-tree, as its superiority holds for different data set sizes, data distributions, number of dimensions and even with varying selectivity ratio.INDEX TERMS Data partitioning, indexing techniques, MBR, uncertain data, high-dimensional data.

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Relational database index choices for genome annotation data

Cited by 6 publications

References 18 publications

iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data

iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data

A Clustering Approach to Integrative Analysis of Multiomic Cancer Data

Analyses of Indexing Techniques on Uncertain Data With High Dimensionality

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