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Optimal Sparsity Se...
Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference
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- Seçilmiş, Deniz, 1991- (författare)
- Stockholms universitet,Institutionen för biokemi och biofysik,Science for Life Laboratory (SciLifeLab),Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Solna, Sweden.
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- Nelander, Sven (författare)
- Uppsala universitet,Science for Life Laboratory, SciLifeLab,Neuroonkologi och neurodegeneration,Uppsala Univ, Dept Immunol Genet & Pathol, Sci Life Lab, Uppsala, Sweden.
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- Sonnhammer, Erik L. L. (författare)
- Stockholms universitet,Institutionen för biokemi och biofysik,Science for Life Laboratory (SciLifeLab),Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Solna, Sweden.
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(creator_code:org_t)
- 2022-07-13
- 2022
- Engelska.
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Ingår i: Frontiers in Genetics. - : Frontiers Media SA. - 1664-8021. ; 13
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Abstract
Ämnesord
Stäng
- Accurate inference of gene regulatory networks (GRNs) is important to unravel unknown regulatory mechanisms and processes, which can lead to the identification of treatment targets for genetic diseases. A variety of GRN inference methods have been proposed that, under suitable data conditions, perform well in benchmarks that consider the entire spectrum of false-positives and -negatives. However, it is very challenging to predict which single network sparsity gives the most accurate GRN. Lacking criteria for sparsity selection, a simplistic solution is to pick the GRN that has a certain number of links per gene, which is guessed to be reasonable. However, this does not guarantee finding the GRN that has the correct sparsity or is the most accurate one. In this study, we provide a general approach for identifying the most accurate and sparsity-wise relevant GRN within the entire space of possible GRNs. The algorithm, called SPA, applies a “GRN information criterion” (GRNIC) that is inspired by two commonly used model selection criteria, Akaike and Bayesian Information Criterion (AIC and BIC) but adapted to GRN inference. The results show that the approach can, in most cases, find the GRN whose sparsity is close to the true sparsity and close to as accurate as possible with the given GRN inference method and data. The datasets and source code can be found at https://bitbucket.org/sonnhammergrni/spa/.
Ämnesord
- NATURVETENSKAP -- Biologi -- Biokemi och molekylärbiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Biochemistry and Molecular Biology (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
Nyckelord
- gene expression data
- gene regulatory network inference
- information criteria
- noise in gene expression
- sparsity selection
- accuracy
- algorithm
- Article
- bootstrapping
- controlled study
- diagnostic test accuracy study
- DNA extraction
- gene expression
- gene regulatory network
- human
- machine learning
- photoreceptor
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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