Search: onr:"swepub:oai:DiVA.org:su-91295" > Optimal Sparsity Cr...
Fältnamn | Indikatorer | Metadata |
---|---|---|
000 | 03939naa a2200457 4500 | |
001 | oai:DiVA.org:su-91295 | |
003 | SwePub | |
008 | 130624s2013 | |||||||||||000 ||eng| | |
009 | oai:DiVA.org:kth-124050 | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-912952 URI |
024 | 7 | a https://doi.org/10.1089/cmb.2012.02682 DOI |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1240502 URI |
040 | a (SwePub)sud (SwePub)kth | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a art2 swepub-publicationtype |
100 | 1 | a Tjärnberg, Andreasu Stockholms universitet,Institutionen för biokemi och biofysik,Science for Life Laboratory (SciLifeLab),Stockholm University, Science for Life Laboratory4 aut0 (Swepub:su)antj9563 |
245 | 1 0 | a Optimal Sparsity Criteria for Network Inference |
264 | 1 | b Mary Ann Liebert Inc,c 2013 |
338 | a print2 rdacarrier | |
500 | a AuthorCount:4; | |
500 | a QC 20130627 | |
520 | a Gene regulatory network inference (that is, determination of the regulatory interactions between a set of genes) provides mechanistic insights of central importance to research in systems biology. Most contemporary network inference methods rely on a sparsity/regularization coefficient, which we call zeta (zeta), to determine the degree of sparsity of the network estimates, that is, the total number of links between the nodes. However, they offer little or no advice on how to select this sparsity coefficient, in particular, for biological data with few samples. We show that an empty network is more accurate than estimates obtained for a poor choice of zeta. In order to avoid such poor choices, we propose a method for optimization of zeta, which maximizes the accuracy of the inferred network for any sparsity-dependent inference method and data set. Our procedure is based on leave-one-out cross-optimization and selection of the zeta value that minimizes the prediction error. We also illustrate the adverse effects of noise, few samples, and uninformative experiments on network inference as well as our method for optimization of zeta. We demonstrate that our zeta optimization method for two widely used inference algorithms-Glmnet and NIR-gives accurate and informative estimates of the network structure, given that the data is informative enough. | |
650 | 7 | a NATURVETENSKAPx Biologix Biokemi och molekylärbiologi0 (SwePub)106022 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Biological Sciencesx Biochemistry and Molecular Biology0 (SwePub)106022 hsv//eng |
650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Bioinformatik0 (SwePub)102032 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Bioinformatics0 (SwePub)102032 hsv//eng |
653 | a algorithms | |
653 | a gene networks | |
653 | a linear algebra | |
653 | a Biochemistry towards Bioinformatics | |
653 | a biokemi med inriktning mot bioinformatik | |
700 | 1 | a Nordling, Torbjorn E. M.u KTH,Reglerteknik,Science for Life Laboratory, SciLifeLab4 aut0 (Swepub:kth)u15vtycm |
700 | 1 | a Studham, Matthewu KTH,Stockholms universitet,Institutionen för biokemi och biofysik,Science for Life Laboratory (SciLifeLab),Science for Life Laboratory, SciLifeLab4 aut0 (Swepub:kth)u1weehbg |
700 | 1 | a Sonnhammer, Erik L. L.u Stockholms universitet,Institutionen för biokemi och biofysik,Science for Life Laboratory (SciLifeLab),Stockholm University, Science for Life Laboratory4 aut0 (Swepub:kth)u1wnqkzi |
710 | 2 | a Stockholms universitetb Institutionen för biokemi och biofysik4 org |
773 | 0 | t Journal of Computational Biologyd : Mary Ann Liebert Incg 20:5, s. 398-408q 20:5<398-408x 1066-5277x 1557-8666 |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-91295 |
856 | 4 8 | u https://doi.org/10.1089/cmb.2012.0268 |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-124050 |
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