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Stepwise Tikhonov Regularisation: Application to the Prediction of HIV-1 Drug Resistance
- Source :
- IEEE/ACM Transactions on Computational Biology and Bioinformatics. 17:292-301
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- This paper focuses on constructing genotypic predictors for antiretroviral drug susceptibility of HIV. To this end, a method to recover the largest elements of an unknown vector in a least squares problem is developed. The proposed method introduces two novel ideas. The first idea is a novel forward stepwise selection procedure based on the magnitude of the estimates of the candidate variables. To implement this newly introduced procedure, we revise Tikhonov regularisation from a sparse representations' perspective. This analysis leads us to the second novel idea in the paper, which is the development of a new method to recover the largest elements of the unknown vector in the least squares problem. The method implements a sequence of Tikhonov regularisation problems which aim to recover the largest of the remaining elements of the unknown vector. Additionally, we derive sufficient conditions that ensure the recovery of the largest elements of the unknown vector. We perform numerical studies using simulated data and data from the Stanford HIV resistance database. The performance of the proposed method is compared against a state-of-the-art method.
- Subjects :
- Anti-HIV Agents
Computer science
0206 medical engineering
Human immunodeficiency virus (HIV)
HIV Infections
Antiretroviral drug
02 engineering and technology
medicine.disease_cause
Least squares
Tikhonov regularization
Drug Resistance, Viral
Genetics
medicine
Humans
Coherence (signal processing)
Least-Squares Analysis
Hiv resistance
Applied Mathematics
Computational Biology
Stepwise regression
Simulated data
Mutation
HIV-1
Algorithm
Algorithms
020602 bioinformatics
Biotechnology
Subjects
Details
- ISSN :
- 23740043 and 15455963
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Accession number :
- edsair.doi.dedup.....3363e760f1b16e04d68a9307e027a367