Cloud-based Approach on Genetic Data Imputation Parameters' Optimization
- Author(s)
- Pavlo Horun, Christine Strauss
- Abstract
The imputation process for genetic data is cost and time-intensive, primarily due to the high complexity of the methods involved, and the substantial volume of data processed. A thorough performance evaluation of the imputation algorithms such as Beagle, AlphaPlantImpute, LinkImputeR, MACH and others shows that while some algorithms are highly accurate, they are often computationally expensive. Being widely used, they have multiple input parameters which impact the quality and accuracy of the imputation. Traditional machine learning techniques for parameter optimization like grid search and randomized search become inefficient in high-dimensional parameter spaces, leading to prohibitive computational costs, especially in large-scale applications. Our study proposes the cloud-based approach for input parameters optimization by using Bayesian optimization with consecutive Domain Reduction Transformer (DRT). Described algorithm and developed library allow users to find the optimal input parameters for the data imputation in a more flexible way.
- Organisation(s)
- Department of Marketing and International Business, Department of Business Decisions and Analytics
- External organisation(s)
- Lviv Polytechnic National University
- Pages
- 279-286
- No. of pages
- 8
- Publication date
- 01-2025
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 502050 Business informatics, 102038 Cloud computing, 101015 Operations research
- Keywords
- ASJC Scopus subject areas
- General Computer Science
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/a500f849-f2cd-42f9-8246-b53736aa82c8