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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.

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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
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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]

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New York, NY, United States

Publication History

Published: 20 August 2017

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National Science Foundation

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BCB '17

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SIGBio

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

August 20 - 23, 2017

Massachusetts, Boston, USA

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

Overall Acceptance Rate 254 of 885 submissions, 29%

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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

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