Demonstration of two novel methods for predicting functional siRNA efficiency

BMC Bioinformatics

Volumn 7, Issue , 2006, Pages

  Jia, Peilin ^a,b,c   Shi, Tieliu ^a   Cai, Yudong ^a,d   Li, Yixue ^b,e  

^a Shanghai Jiao Tong University Affiliated Sixth People s Hospital   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c SHANGHAI CENTER FOR BIOINFORMATION TECHNOLOGY   (China)

^d UNIVERSITY OF MANCHESTER   (United Kingdom)

^e SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

MACHINE LEARNING MODELS; MATHEMATICAL LANGUAGES; NUCLEOTIDE COMPOSITION; PREDICTION TOOLS; SEQUENCE DETERMINANTS; SEQUENCE INFORMATIONS; STATISTICAL MODELING; THERMODYNAMIC PROFILES;

BINARY SEQUENCES; EFFICIENCY; FORECASTING;

RNA;

SMALL INTERFERING RNA;

ACCURACY; ARTICLE; COMPUTER PROGRAM; CONTROLLED STUDY; CORRELATION COEFFICIENT; GENE SILENCING; GENE TARGETING; HUMAN; INTERMETHOD COMPARISON; LEARNING; MATHEMATICAL COMPUTING; NONHUMAN; NUCLEOTIDE SEQUENCE; PREDICTION; RECEIVER OPERATING CHARACTERISTIC; RELIABILITY; RNA INTERFERENCE; RNA SEQUENCE; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; THERMODYNAMICS; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPARATIVE STUDY; COMPUTER SIMULATION; EVALUATION STUDY; GENETIC DATABASE; GENETICS; PHYSIOLOGY; PROCEDURES; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATABASES, GENETIC; MODELS, GENETIC; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; RNA INTERFERENCE; RNA, SMALL INTERFERING; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, RNA;

EID: 33746772726     PISSN: 14712105     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-7-271     Document Type: Article

References (20)

1. Mello CC, Jr DC: Revealing the world of RNA interference. Nature 2004, 431:338-341.
2. Hannon GJ, Rossi JJ: Unlocking the potential of the human genome with RNA interference. Nature 2004, 431:371-378.
3. Wang L, Mu FY: A Web-based design center for vector-based siRNA and siRNA cassette. Bioinformatics 2004, 20(11):1818-1820.
4. Reynolds A, Leake D, Boese Q, Scaringe S, Marshall WS, khvorova A: Rational siRNA design for RNA interference. Nature Biotechnology 2004, 22:326-330.
5. Ui-Tei K, Naito Y, Takahashi F, Haraguchi T, Ohki-Hamazaki H, Juni A, Ueda R, Saigo K: Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference. Nucl Acids Res 2004, 32(3):936-948.
6. Henschel A, Buchholz F, Habermann B: DEQOR: a web-based tool for the design and quality control of siRNAs. Nucl Acids Res 2004, 32(suppl 2):W113-120.
7. Satrom P, Snove JO: A comparison of siRNA efficacy predictors. Biochemical and Biophysical Research Communications 2004, 321(1):247-253.
8. Saetrom P: Predicting the efficacy of short oligonucleotides in antisense and RNAi experiments with boosted genetic programming. Bioinformatics 2004, 20(17):3055-3063.
9. Chou KC: Using subsite coupling to predict signal peptides. Protein Engineering 2001, 14(2):75-79.
10. Vapnik: The Nature of Statistical Learning Theory. In Springer New York; 1995.
11. Brown MPS, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, Ares M, Jr., Haussler D: Knowledge-based analysis of microarray gene expression data by using support vector machines. PNAS 2000, 97(1):262-267.
12. Cai YD, Liu XJ, Xu X, Zhou GP: Support Vector Machines for predicting protein structural class. BMC Bioinformatics 2001, 2(1):3.
13. Cai YD, Lin S, Chou KC: Support vector machines for prediction of protein signal sequences and their cleavage sites. Peptides 2003, 24(1):159-161.
14. Cai YD, Liu XJ, Xu XB, Chou KC: Support Vector Machines for Prediction of Protein Subcellular Location. Molecular Cell Biology Research Communication 2000, 4:230-233.
15. Huesken D, Lange J, Mickanin C, Weiler J, Asselbergs F, Warner J, Meloon B, Engel S, Rosenberg A, Cohen D, Labow M, Reinhardt M, Natt F, Hall J: Design of a genome-wide siRNA library using an artificial neural network. Nat Biotech 2005, 23(8):995-1001.
16. Khvorova A, Reynolds A, Jayasena SD: Functional siRNAs and miRNAs Exhibit Strand Bias. Cell 2003, 115(2):209-216.
17. Chou KC: Predicting of Protein Signal Sequences and Their Cleavage Sites. PROTEINS. Structure, Function, and Genetics 2001, 42:136-139.
18. Cai YD, Liu XJ, Xu X, Chou KC: Support vector machines for predicting HIV protease cleavage sites in protein. Journal of Computational Chemistry 2001, 23(2):267-274.
19. Hua S, Sun Z: Support vector machine approach for protein subcellular localization prediction. Bioinformatics 2001, 17(8):721-728.
20. M. FREIER SUSAN, KIERZEK RYSZARD, A. JAEGER JOHN, SUGIMOTO NAOKI, H. CARUTHERS MARVIN, NEILSON THOMAS, H. TURNER DOUGLAS: Improved free-eneregy parameters for predictions of RNA duplex stability. PNAS 1986, 83:9373-9377.