Its been about a year, so things have changed for Table 1, so this is an update.

the formatting will probably not be very neat, but the columns should be tab delimited if you want to copy paste...



number Techniques class/reg siRNAdataset Features year reference
1 Rule classification 180–19mers 8 2004 Reynolds A, Leake D, Boese Q, Scaringe S, Marshall WS, et al. (2004) Rational siRNA design for RNA interference. Nat Biotechnol 22: 326–330.
2 Rule classification 62-19mers 4 2004 Ui-Tei K, Naito Y, Takahashi F, Haraguchi T, Ohki-Hamazaki H, et al. (2004) Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference. Nucleic Acids Res 32: 936–948.
3 Rule classification 46–19mers-train, 34–19mers-test 9 2004 Amarzguioui M, Prydz H (2004) An algorithm for selection of functional siRNA sequences. Biochemical and Biophysical Research Communications 316:1050–1058.
4 Rule classification 148-19mers 18 2004 Hsieh AC, Bo R, Manola J, Vazquez F, Bare O, et al. (2004) A library of siRNA duplexes targeting the phosphoinositide 3-kinase pathway: determinants of gene silencing for use in cell-based screens. Nucleic Acids Res 32: 893–901.
5 Rule classification 249-19mers 12 2004 Takasaki S, Kotani S, Konagaya A (2004) An Effective Method for Selecting siRNA Target Sequences in Mammalian Cells. Cell Cycle 3: 790–795.
6 Rule classification 23-19mers 2 2004 Poliseno L, Evangelista M, Mercatanti A, Mariani L, Citti L, et al. (2004) The energy profiling of short interfering RNAs is highly predictive of their activity. Oligonucleotides 14: 227–232.
7 GPBoost, SVM class/reg 204–19mers ? 2004 Sætrom P, Snove O Jr (2004) A comparison of siRNA efficacy predictors. Biochem Biophys Res Commun 321: 247–253.
8 GPBoost, SVM regression 581-19mers ? 2004 Sætrom P (2004) Predicting the efficacy of short oligonucleotides in antisense and RNAi experiments with boosted genetic programming. Bioinformatics 20: 3055–3063.
9 DT class/reg 398-19mers 11 2004 Chalk AM, Wahlestedt C, Sonnhammer EL (2004) Improved and automated prediction of effective siRNA. Biochem Biophys Res Commun 319: 264–274.
10 Rule classification composite 8 2005 Henschel A, Buchholz F, Habermann B (2004) DEQOR: a web-based tool for the design and quality control of siRNAs. Nucleic Acids Res 32: W113–120.
11 ANN regression 2431-21mers 84 2005 Huesken D, Lange J, Mickanin C, Weiler J, Asselbergs F, et al. (2005) Corrigendum: Design of a genome-wide siRNA library using an artificial neural network. Nat Biotechnol 23: 1315. Huesken D, Lange J, Mickanin C, Weiler J, Asselbergs F, et al. (2005) Design of a genome-wide siRNA library using an artificial neural network. Nat Biotechnol 23: 995–1001.
12 ANN classification 180-19mers 6 2005 Ge G, Wong GW, Luo B (2005) Prediction of siRNA knockdown efficiency using artificial neural network models. Biochem Biophys Res Commun 336: 723–728.
13 rule scoring regression 361-19mers 14 2005 Takasaki S, Kotani S, Konagaya A (2005) Selecting effective siRNA target sequences for mammalian genes. RNA Biol 2: 21-27.
14 Rule, DT classification 601-19mers 55 2005 Jagla B, Aulner N, Kelly PD, Song D, Volchuk A, et al. (2005) Sequence characteristics of functional siRNAs. RNA 11: 864–872.
15 GSK SVM classification 94-19mers 84 2005 Teramoto R, Aoki M, Kimura T, Kanaoka M (2005) Prediction of siRNA functionality using generalized string kernel and support vector machine. FEBS Lett 579: 2878–2882.
16 Rule DT, SVM classification 33-21mers 4 2005 Yiu SM, Wong PW, Lam TW, Mui YC, Kung HF, et al. (2005) Filtering of ineffective siRNAs and improved siRNA design tool. Bioinformatics 21: 144–151.
17 SVM classification 2431-21mers, 581-19mers 84+15+20 2006 Jia P, Shi T, Cai Y, Li Y (2006) Demonstration of two novel methods for predicting functional siRNA efficiency. BMC Bioinformatics 7: 271.
18 ANN regression 581-19mers-train 2431-21mers-test 200 2006 Shabalina SA, Spiridonov AN, Ogurtsov AY (2006) Computational models with thermodynamic and composition features improve siRNA design. BMC Bioinformatics 7: 65.
19 linear regression 526-19mers 84 2006 Holen T (2006) Efficient prediction of siRNAs with siRNArules 1.0: an opensource JAVA approach to siRNA algorithms. Rna 12: 1620–1625.
20 linear regression 2431-21mers, 653-19mers 84+84 2006 Vert JP, Foveau N, Lajaunie C, Vandenbrouck Y (2006) An accurate and interpretable model for siRNA efficacy prediction. BMC Bioinformatics 7: 1–17.
21 rule scoring regression 860 siRNAs 19 2006 Takasaki S, Konagaya A (2006) Comparative analyses for selecting effective siRNA sequences. Chem-Bio Informatics Journal 6: 69-84.
22 DRM classification 3277 276-initial 21-final 2006 Gong W, Ren Y, Xu Q, Wang Y, Lin D, et al. (2006) Integrated siRNA design based on surveying of features associated with high RNAi effectiveness. BMC Bioinformatics 7: 1–21.
23 rule classification 420 and 1220 6+4+16+64 2007 Bradac I, Svobodova Varekova R, Wacenovsky M, Skrdla M, Plchut M, et al. (2007) siRNA selection criteria–statistical analyses of applicability and significance. Biochem Biophys Res Commun 359: 83–87.
24 SVM class/reg 2252-21mers 240-19mers 572 2007 Ladunga I (2007) More complete gene silencing by fewer siRNAs: transparent optimized design and biophysical signature. Nucleic Acids Res 35: 433–440.
25 linear regression 2431-21mers 84+ 2007 Ichihara M, Murakumo Y, Masuda A, Matsuura T, Asai N, et al. (2007) Thermodynamic instability of siRNA duplex is a prerequisite for dependable prediction of siRNA activities. Nucleic Acids Res 35: e123.
26 weighted scoring classification ~1000-training, 1000 subset of 2431-21mers testing and 2578 from SIRecords 12 2007 Shah JK, Garner HR, White MA, Shames DS, Minna JD (2007) sIR: siRNA Information Resource, a web-based tool for siRNA sequence design and analysis and an open access siRNA database. BMC Bioinformatics 8: 178.
27 SVM regression 2431-21mers, 579-19mers 1566 2007 Peek AS (2007) Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features. BMC Bioinformatics 8: 182.
28 Rule, DT, GPBoost, linear class/reg 2431-21mers, 601-19mers, 238-19mers, 67-19mers 84+84, 22-final 2007 Matveeva O, Nechipurenko Y, Rossi L, Moore B, Saetrom P, et al. (2007) Comparison of approaches for rational siRNA design leading to a new efficient and transparent method. Nucleic Acids Res 35: e63.
29 SVM classification 2431-21mers, 653-19mers 28 2007 Lu ZJ, Mathews DH (2007) Efficient siRNA selection using hybridization thermodynamics. Nucleic Acids Res.
30 Rule, SVM, RFR regression 3589 41 2007 Jiang P, Wu H, Da Y, Sang F, Wei J, et al. (2007) RFRCDB-siRNA: improveddesign of siRNAs by random forest regression model coupled with databasesearching. Comput Methods Programs Biomed 87: 230–238.
31 linear regression 702-19mers 76+3 2007 Katoh T, Suzuki T (2007) Specific residues at every third position of siRNA shape its efficient RNAi activity. Nucleic Acids Res 35: e27.
32 string kernal SVM regression 70, 560, 600 2431 19mers 8, 9, 16, 8, 78 2008 Qiu S, Lane T (2008) Multiple Kernel Support Vector Regression for siRNA Efficacy Prediction. In: Mandoiu RS, Zelikovsky A, editors. Lecture Notes in Computer Science. Berlin: Springer. pp. 367-378.
33 Rule HS classification 474 subset of 2433-21mers, 99 subset of 294-19mers, 360 21mers 4 2008 Tafer H, Ameres SL, Obernosterer G, Gebeshuber CA, Schroeder R, et al. (2008) The impact of target site accessibility on the design of effective siRNAs. Nat Biotechnol 26: 578–583.
34 Rule DT classification 62 21mers 8 2008 de Almeida RS, Keita D, Libeau G, Albina E (2008) Structure and sequence motifs of siRNA linked with in vitro down-regulation of morbillivirus gene expression. Antiviral Res 79: 37–48.
35 BMCMC-FS logistic regression regression 6483 siRNAs 497-initial 19-final 2009 Klingelhoefer JW, Moutsianas L, Holmes C (2009) Approximate Bayesian feature selection on a large meta-dataset offers novel insights on factors that effect siRNA potency. Bioinformatics 25: 1594-1601.
36 ANN, GLM, SVM regression 2431-21mers 168-ANN, 84-GLM, 1444-SVM 2009 McQuisten KA, Peek AS (2009) Comparing artificial neural networks, general linear models and support vector machines in building predictive models for small interfering RNAs. PLoS One 4: e7522.
37 SVM classification 200 most and 200 least potent from 2431-21mers 89 2009 Wang X, Wang X, Varma RK, Beauchamp L, Magdaleno S, et al. (2009) Selection of hyperfunctional siRNAs with improved potency and specificity. Nucleic Acids Res 37: e152.
38 linear + multi-task learning regression 16 distinct experiments with 655 siRNAs 19 2010 Liu Q, Xu Q, Zheng VW, Xue H, Cao Z, et al. (2010) Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study. BMC Bioinformatics 11: 181.
39 SVM regression 2431-21mers 63 2010 Ebalunode JO, Jagun C, Zheng W (2010) Informatics approach to the rational design of siRNA libraries. Methods Mol Biol 672: 341-358.
40 RBF Network, DT classification 833-effective, 847-ineffective 19 2010 Takasaki S (2010) Efficient prediction methods for selecting effective siRNA sequences. Comput Biol Med 40: 149-158.