Bump Hunting in the Dark: Local Discrepancy Maximization on Graphs

2017 ◽  
Vol 29 (3) ◽  
pp. 529-542 ◽  
Author(s):  
Aristides Gionis ◽  
Michael Mathioudakis ◽  
Antti Ukkonen
Keyword(s):  
Bump Hunting

Bump hunting in regression analysis

1992 ◽  
Vol 14 (2) ◽  
pp. 141-152 ◽  
Author(s):  
Nancy E. Heckman
Keyword(s):  
Regression Analysis ◽  
Bump Hunting

scholarly journals Flexible expressed region analysis for RNA-seq with derfinder

2015 ◽  
Author(s):  
Leonardo Collado-Torres ◽  
Abhinav Nellore ◽  
Alyssa C. Frazee ◽  
Christopher Wilks ◽  
Michael I. Love ◽  
...  
Keyword(s):  
Differential Expression ◽  
Time Course ◽  
Differential Expression Analysis ◽  
Tissue Expression ◽  
Rna Seq ◽  
Computationally Efficient ◽  
Modeling Framework ◽  
Single Base ◽  
Region Analysis ◽  
Bump Hunting

AbstractBackgroundDifferential expression analysis of RNA sequencing (RNA-seq) data typically relies on reconstructing transcripts or counting reads that overlap known gene structures. We previously introduced an intermediate statistical approach called differentially expressed region (DER) finder that seeks to identify contiguous regions of the genome showing differential expression signal at single base resolution without relying on existing annotation or potentially inaccurate transcript assembly.ResultsWe present the derfinder software that improves our annotation-agnostic approach to RNA-seq analysis by: (1) implementing a computationally efficient bump-hunting approach to identify DERs which permits genome-scale analyses in a large number of samples, (2) introducing a flexible statistical modeling framework, including multi-group and time-course analyses and (3) introducing a new set of data visualizations for expressed region analysis. We apply this approach to public RNA-seq data from the Genotype-Tissue Expression (GTEx) project and BrainSpan project to show that derfinder permits the analysis of hundreds of samples at base resolution in R, identifies expression outside of known gene boundaries and can be used to visualize expressed regions at base-resolution. In simulations our base resolution approaches enable discovery in the presence of incomplete annotation and is nearly as powerful as feature-level methods when the annotation is complete.Conclusionsderfinder analysis using expressed region-level and single base-level approaches provides a compromise between full transcript reconstruction and feature-level analysis.The package is available from Bioconductor at www.bioconductor.org/packages/derfinder.

scholarly journals Bump hunting with non-Gaussian kernels

2004 ◽  
Vol 32 (5) ◽  
pp. 2124-2141 ◽  
Author(s):  
Chunming Zhang ◽  
Michael C. Minnotte ◽  
Peter Hall
Keyword(s):  
Gaussian Kernels ◽  
Bump Hunting ◽  
Non Gaussian

Evolutionary Algorithm Embedded With Bump-Hunting for Constrained Design Optimization

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Kamrul Hasan Rahi ◽  
Hemant Kumar Singh ◽  
Tapabrata Ray
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Computational Cost ◽  
Search Space ◽  
Bump Hunting ◽  
Simulation Based ◽  
Engineering Problems ◽  
Turbine Design

Abstract Real-world design optimization problems commonly entail constraints that must be satisfied for the design to be viable. Mathematically, the constraints divide the search space into feasible (where all constraints are satisfied) and infeasible (where at least one constraint is violated) regions. The presence of multiple constraints, constricted and/or disconnected feasible regions, non-linearity and multi-modality of the underlying functions could significantly slow down the convergence of evolutionary algorithms (EA). Since each design evaluation incurs some time/computational cost, it is of significant interest to improve the rate of convergence to obtain competitive solutions with relatively fewer design evaluations. In this study, we propose to accomplish this using two mechanisms: (a) more intensified search by identifying promising regions through “bump-hunting,” and (b) use of infeasibility-driven ranking to exploit the fact that optimal solutions are likely to be located on constraint boundaries. Numerical experiments are conducted on a range of mathematical benchmarks and empirically formulated engineering problems, as well as a simulation-based wind turbine design optimization problem. The proposed approach shows up to 53.48% improvement in median objective values and up to 69.23% reduction in cost of identifying a feasible solution compared with a baseline EA.

Bump hunting by topological data analysis

Stat ◽  
2017 ◽  
Vol 6 (1) ◽  
pp. 462-471 ◽  
Author(s):  
Max Sommerfeld ◽  
Giseon Heo ◽  
Peter Kim ◽  
Stephen T. Rush ◽  
J. S. Marron
Keyword(s):  
Data Analysis ◽  
Topological Data Analysis ◽  
Bump Hunting ◽  
Topological Data
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