publication . Article . 2009

Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Kyle A. McQuisten; Andrew S. Peek;
Open Access English
  • Published: 01 Oct 2009 Journal: PLoS ONE (issn: 1932-6203, Copyright policy)
  • Publisher: Public Library of Science (PLoS)
Abstract
Background Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation app...
Subjects
free text keywords: Medicine, R, Science, Q, Research Article, Computational Biology, Genetics and Genomics, Biochemistry/Drug Discovery, Biochemistry/Transcription and Translation, Molecular Biology/mRNA Stability, General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Medicine
Funded by
NIH| Support Vector Machine modeling software for improving RNAi efficacy prediction
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R43GM079132-01
  • Funding stream: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
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41 references, page 1 of 3

Fire, A, Xu, S, Montgomery, MK, Kostas, SA, Driver, SE. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans.. Nature. 1998; 39: 806-811

Walters, DK, Jelinek, DF. The effectiveness of double-stranded short inhibitory RNAs (siRNAs) may depend on the method of transfection.. Antisense Nucleic Acid Drug Dev. 2002; 12: 411-418 [PubMed]

Schwarz, DS, Hutvagner, G, Du, T, Xu, Z, Aronin, N. Asymmetry in the Assembly of the RNAi Enzyme Complex.. Cell. 2003; 115: 199-208 [OpenAIRE] [PubMed]

Khvorova, A, Reynolds, A, Jayasena, SD. Functional siRNAs and miRNAs exhibit strand bias.. Cell. 2003; 115: 209-216 [OpenAIRE] [PubMed]

Bohula EA, SA, Sohail, M, Playford, MP, Riedemann, J, Southern, EM, Macaulay, VM. The efficacy of small interfering RNAs targeted to the type 1 insulin-like growth factor receptor (IGF1R) is influenced by secondary structure in the IGF1R transcript.. J Biol Chemistry. 2003; 278: 15991-15997 [OpenAIRE]

Vickers, TA, Koo, S, Bennett, CF, Crooke, ST, Dean, NM. Efficient reduction of target RNAs by small interfering RNA and RNase H-dependent antisense agents. A comparative analysis.. J Biol Chem. 2003; 278: 7108-7118 [PubMed]

Kretschmer-Kazemi Far, R, Sczakiel, G. The activity of siRNA in mammalian cells is related to structural target accessibility: a comparison with antisense oligonucleotides.. Nucleic Acids Res. 2003; 31: 4417-4424 [OpenAIRE] [PubMed]

Reynolds, A, Leake, D, Boese, Q, Scaringe, S, Marshall, WS. Rational siRNA design for RNA interference.. Nat Biotechnol. 2004; 22: 326-330 [PubMed]

Ui-Tei, K, Naito, Y, Takahashi, F, Haraguchi, T, Ohki-Hamazaki, H. Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference.. Nucleic Acids Res. 2004; 32: 936-948 [OpenAIRE] [PubMed]

Amarzguioui, M, Prydz, H. An algorithm for selection of functional siRNA sequences.. Biochemical and Biophysical Research Communications. 2004; 316: 1050-1058 [OpenAIRE] [PubMed]

Hsieh, AC, Bo, R, Manola, J, Vazquez, F, Bare, O. A library of siRNA duplexes targeting the phosphoinositide 3-kinase pathway: determinants of gene silencing for use in cell-based screens.. Nucleic Acids Res. 2004; 32: 893-901 [OpenAIRE] [PubMed]

Takasaki, S, Kotani, S, Konagaya, A. An Effective Method for Selecting siRNA Target Sequences in Mammalian Cells.. Cell Cycle. 2004; 3: 790-795 [PubMed]

Poliseno, L, Evangelista, M, Mercatanti, A, Mariani, L, Citti, L. The energy profiling of short interfering RNAs is highly predictive of their activity.. Oligonucleotides. 2004; 14: 227-232 [OpenAIRE] [PubMed]

Sætrom, P, Snove, O. A comparison of siRNA efficacy predictors.. Biochem Biophys Res Commun. 2004; 321: 247-253 [OpenAIRE] [PubMed]

Sætrom, P. Predicting the efficacy of short oligonucleotides in antisense and RNAi experiments with boosted genetic programming.. Bioinformatics. 2004; 20: 3055-3063 [OpenAIRE] [PubMed]

41 references, page 1 of 3
Abstract
Background Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation app...
Subjects
free text keywords: Medicine, R, Science, Q, Research Article, Computational Biology, Genetics and Genomics, Biochemistry/Drug Discovery, Biochemistry/Transcription and Translation, Molecular Biology/mRNA Stability, General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Medicine
Funded by
NIH| Support Vector Machine modeling software for improving RNAi efficacy prediction
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R43GM079132-01
  • Funding stream: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
Download fromView all 3 versions
PLoS ONE
Article . 2009
PLoS ONE
Article . 2009
Provider: Crossref
PLoS ONE
Article
Provider: UnpayWall
41 references, page 1 of 3

Fire, A, Xu, S, Montgomery, MK, Kostas, SA, Driver, SE. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans.. Nature. 1998; 39: 806-811

Walters, DK, Jelinek, DF. The effectiveness of double-stranded short inhibitory RNAs (siRNAs) may depend on the method of transfection.. Antisense Nucleic Acid Drug Dev. 2002; 12: 411-418 [PubMed]

Schwarz, DS, Hutvagner, G, Du, T, Xu, Z, Aronin, N. Asymmetry in the Assembly of the RNAi Enzyme Complex.. Cell. 2003; 115: 199-208 [OpenAIRE] [PubMed]

Khvorova, A, Reynolds, A, Jayasena, SD. Functional siRNAs and miRNAs exhibit strand bias.. Cell. 2003; 115: 209-216 [OpenAIRE] [PubMed]

Bohula EA, SA, Sohail, M, Playford, MP, Riedemann, J, Southern, EM, Macaulay, VM. The efficacy of small interfering RNAs targeted to the type 1 insulin-like growth factor receptor (IGF1R) is influenced by secondary structure in the IGF1R transcript.. J Biol Chemistry. 2003; 278: 15991-15997 [OpenAIRE]

Vickers, TA, Koo, S, Bennett, CF, Crooke, ST, Dean, NM. Efficient reduction of target RNAs by small interfering RNA and RNase H-dependent antisense agents. A comparative analysis.. J Biol Chem. 2003; 278: 7108-7118 [PubMed]

Kretschmer-Kazemi Far, R, Sczakiel, G. The activity of siRNA in mammalian cells is related to structural target accessibility: a comparison with antisense oligonucleotides.. Nucleic Acids Res. 2003; 31: 4417-4424 [OpenAIRE] [PubMed]

Reynolds, A, Leake, D, Boese, Q, Scaringe, S, Marshall, WS. Rational siRNA design for RNA interference.. Nat Biotechnol. 2004; 22: 326-330 [PubMed]

Ui-Tei, K, Naito, Y, Takahashi, F, Haraguchi, T, Ohki-Hamazaki, H. Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference.. Nucleic Acids Res. 2004; 32: 936-948 [OpenAIRE] [PubMed]

Amarzguioui, M, Prydz, H. An algorithm for selection of functional siRNA sequences.. Biochemical and Biophysical Research Communications. 2004; 316: 1050-1058 [OpenAIRE] [PubMed]

Hsieh, AC, Bo, R, Manola, J, Vazquez, F, Bare, O. A library of siRNA duplexes targeting the phosphoinositide 3-kinase pathway: determinants of gene silencing for use in cell-based screens.. Nucleic Acids Res. 2004; 32: 893-901 [OpenAIRE] [PubMed]

Takasaki, S, Kotani, S, Konagaya, A. An Effective Method for Selecting siRNA Target Sequences in Mammalian Cells.. Cell Cycle. 2004; 3: 790-795 [PubMed]

Poliseno, L, Evangelista, M, Mercatanti, A, Mariani, L, Citti, L. The energy profiling of short interfering RNAs is highly predictive of their activity.. Oligonucleotides. 2004; 14: 227-232 [OpenAIRE] [PubMed]

Sætrom, P, Snove, O. A comparison of siRNA efficacy predictors.. Biochem Biophys Res Commun. 2004; 321: 247-253 [OpenAIRE] [PubMed]

Sætrom, P. Predicting the efficacy of short oligonucleotides in antisense and RNAi experiments with boosted genetic programming.. Bioinformatics. 2004; 20: 3055-3063 [OpenAIRE] [PubMed]

41 references, page 1 of 3
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