More Web Proxy on the site http://driver.im/

research-article

Public Access

Bayesian Hyperparameter Optimization for Machine Learning Based eQTL Analysis

Authors:

Andrew Quitadadmo,

Xinghua ShiAuthors Info & Claims

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

Pages 98 - 106

https://doi.org/10.1145/3107411.3107434

Published: 20 August 2017 Publication History

Abstract

Machine learning methods are being applied to a wide range of problems in biology and bioinformatics. These methods often rely on configuring high level parameters, or hyperparameters, such as regularization hyperparameters in sparse learning models like graph-guided multitask Lasso methods. Different choices for these hyperparameters will lead to different results, which makes finding good hyperparameter combinations an important task when using these hyperparameter dependent methods. There are several different ways to tune hyperparameters including manual tuning, grid search, random search, and Bayesian optimization. In this paper, we apply three hyperparameter tuning strategies to eQTL analysis including grid and random search in addition to Bayesian optimization. Experiments show that the Bayesian optimization strategy outperforms the other strategies in modeling eQTL associations. Applying this strategy to assess eQTL associations using the 1000 Genomes structural variation genotypes and RNAseq data in gEUVADIS, we identify a set of new SVs associated with gene expression changes in a human population.

References

[1]

J. Bergstra and Y. Bengio. Random Search for Hyper-parameter Optimization. J. Mach. Learn. Res., 13:281--305, Feb. 2012.

Digital Library

[2]

E. J. Candes, M. B. Wakin, and S. P. Boyd. Enhancing sparsity by reweighted ??? 1 minimization. Journal of Fourier analysis and applications, 14(5--6):877--905, 2008.

[3]

E. Y. Chen, C. M. Tan, Y. Kou, Q. Duan, Z. Wang, G. V. Meirelles, N. R. Clark, and A. Maayan. Enrichr: interactive and collaborative html5 gene list enrichment analysis tool. BMC bioinformatics, 14(1):128, 2013.

[4]

X. Chen, X. Shi, X. Xu, Z. Wang, R. Mills, C. Lee, and J. Xu. A two-graph guided multi-task lasso approach for eqtl mapping. In International Conference on Artificial Intelligence and Statistics, pages 208--217, 2012.

[5]

P. Duchateau, C. Pullinger, M. Cho, C. Eng, and J. Kane. Apolipoprotein l gene family: tissue-specific expression, splicing, promoter regions; discovery of a new gene. Journal of Lipid Research, 42(4):620--630, 2001.

[6]

K. Kawasaki, S. Minoshima, E. Nakato, K. Shibuya, A. Shintani, J. L. Schmeits, J. Wang, and N. Shimizu. One-megabase sequence analysis of the human immunoglobulin lambda gene locus. Genome Research, 7(3):250--261, 1997.

[7]

W. J. Kent, C. W. Sugnet, T. S. Furey, K. M. Roskin, T. H. Pringle, A. M. Zahler, and D. Haussler. The human genome browser at ucsc. Genome research, 12(6):996-- 1006, 2002.

[8]

M. V. Kuleshov, M. R. Jones, A. D. Rouillard, N. F. Fernandez, Q. Duan, Z. Wang, S. Koplev, S. L. Jenkins, K. M. Jagodnik, A. Lachmann, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic acids research, page gkw377, 2016.

[9]

T. Lappalainen, M. Sammeth, M. R. Friedländer, P. AC???t Hoen, J. Monlong, M. A. Rivas, M. Gonzàlez-Porta, N. Kurbatova, T. Griebel, P. G. Ferreira, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature, 501(7468):506--511, 2013.

[10]

Q. Li, J.-H. Seo, B. Stranger, A. McKenna, I. Peter, T. LaFramboise, M. Brown, S. Tyekucheva, and M. L. Freedman. Integrative eqtl-based analyses reveal the biology of breast cancer risk loci. Cell, 152(3):633--641, 2013.

[11]

L. Liang, N. Morar, A. L. Dixon, G. M. Lathrop, G. R. Abecasis, M. F. Moffatt, and W. O. Cookson. A cross-platform analysis of 14,177 expression quantitative trait loci derived from lymphoblastoid cell lines. Genome research, 23(4):716--726, 2013.

[12]

Z. Liu, H. Lu, Z. Jiang, A. Pastuszyn, and C.-a. A. Hu. Apolipoprotein l6, a novel proapoptotic bcl-2 homology 3-only protein, induces mitochondria-mediated apoptosis in cancer cells. Molecular Cancer Research, 3(1):21--31, 2005.

[13]

R. E. Mills, K. Walter, C. Stewart, R. E. Handsaker, K. Chen, C. Alkan, A. Abyzov, S. C. Yoon, K. Ye, R. K. Cheetham, et al. Mapping copy number variation by population-scale genome sequencing. Nature, 470(7332):59--65, 2011.

[14]

J. Snoek, H. Larochelle, and R. P. Adams. Practical Bayesian Optimization of Machine Learning Algorithms. arXiv:1206.2944 {cs, stat}, June 2012. arXiv: 1206.2944.

[15]

B. E. Stranger, M. S. Forrest, A. G. Clark, M. J. Minichiello, S. Deutsch, R. Lyle, S. Hunt, B. Kahl, S. E. Antonarakis, S. Tavaré, et al. Genome-wide associations of gene expression variation in humans. PLoS genetics, 1(6):e78, 2005.

[16]

B. E. Stranger, M. S. Forrest, M. Dunning, C. E. Ingle, C. Beazley, N. Thorne, R. Redon, C. P. Bird, A. De Grassi, C. Lee, et al. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science, 315(5813):848-- 853, 2007.

[17]

P. H. Sudmant, T. Rausch, E. J. Gardner, R. E. Handsaker, A. Abyzov, J. Huddleston, Y. Zhang, K. Ye, G. Jun, M. H.-Y. Fritz, et al. An integrated map of structural variation in 2,504 human genomes. Nature, 526(7571):75--81, 2015.

[18]

S. Takahashi, Y. Cui, Y. Han, J. Fagerness, B. Galloway, Y. Shen, and M. T. Tsuang. Association of snps and haplotypes in apol1, 2 and 4 with schizophrenia. schizophrenia research. Journal of Schizophrenia Research, 104(0):153--164, 2008.

[19]

L. Tian, A. Quitadamo, F. Lin, and X. Shi. Methods for population-based eqtl analysis in human genetics. Tsinghua Science and Technology, 19(6):624--634, 2014.

[20]

G. Wan, S. Zhaorigetu, Z. Liu, R. Kaini, Z. Jiang, and C. Hu. Apolipoprotein l1, a novel bcl-2 homology domain 3-only lipid-binding protein, induces autophagic cell death. Journal of Biological Chemistry, 283(31):21540--21549, 2008.

[21]

L. Zhang and S. Kim. Learning gene networks under snp perturbations using eqtl datasets. PLoS computational biology, 10(2):e1003420, 2014.

Cited By

Nguyen TVo HYoo M(2024)Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost ApproachSensors10.3390/s2411333924:11(3339)Online publication date: 23-May-2024
https://doi.org/10.3390/s24113339
Alsenan SAl-Turaiki IAldayel MTounsi M(2024)Role of Optimization in RNA–Protein-Binding PredictionCurrent Issues in Molecular Biology10.3390/cimb4602008746:2(1360-1373)Online publication date: 4-Feb-2024
https://doi.org/10.3390/cimb46020087
M S KRajaguru HNair A(2024)Enhancement of Classifier Performance with Adam and RanAdam Hyper-Parameter Tuning for Lung Cancer Detection from Microarray Data—In Pursuit of PrecisionBioengineering10.3390/bioengineering1104031411:4(314)Online publication date: 26-Mar-2024
https://doi.org/10.3390/bioengineering11040314
Show More Cited By

Bayesian Hyperparameter Optimization for Machine Learning Based eQTL Analysis
1. Applied computing
  1. Life and medical sciences

Recommendations

Sequential parallel LASSO models for eQTL analysis
BCB '15: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

The availability of large-scale genomic and transcriptomic data on populations makes it necessary to perform computationally intensive expression quantitative trait locus (eQTL) analysis. Modeling in a sparse learning framework, LASSO based tools are ...
Radial Basis Function and Bayesian Methods for the Hyperparameter Optimization of Classification Random Forests
Computational Science and Its Applications – ICCSA 2023 Workshops
Abstract
The hyperparameter optimization of a random forest (RF) is a discrete black-box optimization problem that aims to find the settings of the hyperparameters that optimize an overall out-of-bag (OOB) performance measure of the RF. This problem is ...
Towards Efficient Multiobjective Hyperparameter Optimization: A Multiobjective Multi-fidelity Bayesian Optimization and Hyperband Algorithm
Parallel Problem Solving from Nature – PPSN XVII
Abstract
Developing an efficient solver for hyperparameter optimization (HPO) can help to support the environmental sustainability of modern AI. One popular solver for HPO problems is called BOHB, which attempts to combine the benefits of Bayesian ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

August 2017

800 pages

ISBN:9781450347228

DOI:10.1145/3107411

General Chairs:
Nurit Haspel
University of Massachusetts Boston, USA
,
Lenore J. Cowen
Tufts University, USA
,
Program Chairs:
Amarda Shehu
George Mason University, USA
,
Tamer Kahveci
University of Florida, USA
,
Giuseppe Pozzi
Politecnico di Milano, Italy

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGBio: ACM Special Interest Group on Bioinformatics

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Conference

BCB '17

Sponsor:

SIGBio

BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 20 - 23, 2017

Massachusetts, Boston, USA

Acceptance Rates

ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;

Overall Acceptance Rate 254 of 885 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
389
Total Downloads

Downloads (Last 12 months)74
Downloads (Last 6 weeks)11

Reflects downloads up to 14 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Nguyen TVo HYoo M(2024)Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost ApproachSensors10.3390/s2411333924:11(3339)Online publication date: 23-May-2024
https://doi.org/10.3390/s24113339
Alsenan SAl-Turaiki IAldayel MTounsi M(2024)Role of Optimization in RNA–Protein-Binding PredictionCurrent Issues in Molecular Biology10.3390/cimb4602008746:2(1360-1373)Online publication date: 4-Feb-2024
https://doi.org/10.3390/cimb46020087
M S KRajaguru HNair A(2024)Enhancement of Classifier Performance with Adam and RanAdam Hyper-Parameter Tuning for Lung Cancer Detection from Microarray Data—In Pursuit of PrecisionBioengineering10.3390/bioengineering1104031411:4(314)Online publication date: 26-Mar-2024
https://doi.org/10.3390/bioengineering11040314
Arden FSafitri C(2022)Hyperparameter Tuning Algorithm Comparison with Machine Learning Algorithms2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)10.1109/ICITISEE57756.2022.10057630(183-188)Online publication date: 13-Dec-2022
https://doi.org/10.1109/ICITISEE57756.2022.10057630
Fatyanosa TAritsugi M(2021)An Automatic Convolutional Neural Network Optimization Using a Diversity-Guided Genetic AlgorithmIEEE Access10.1109/ACCESS.2021.30917299(91410-91426)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3091729
Mao SJiang YMathew EKannan S(2020)BOAssembler: A Bayesian Optimization Framework to Improve RNA-Seq Assembly PerformanceAlgorithms for Computational Biology10.1007/978-3-030-42266-0_15(188-197)Online publication date: 3-Apr-2020
https://doi.org/10.1007/978-3-030-42266-0_15

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents