MysiRNA: Improving siRNA efficacy prediction using a machine-learning model combining multi-tools and whole stacking energy (ΔG)

Journal of Biomedical Informatics

Volumn 45, Issue 3, 2012, Pages 528-534

  Mysara, Mohamed ^a,b,c   Elhefnawi, Mahmoud ^a,b   Garibaldi, Jonathan M ^c  

^a NATIONAL RESEARCH CENTRE   (Egypt)

^b Information Technology Institute   (Egypt)

^c UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

G; Artificial neural network; Gibbs energy; SiRNA efficiency prediction; SiRNA functionality prediction; Whole stacking energy

Indexed keywords

ARTIFICIAL NEURAL NETWORK MODELS; COMBINATORIAL STUDIES; CORRELATION COEFFICIENT; DATA SETS; DEVELOPED MODEL; EVALUATION STUDY; GENE-REGULATION; INHIBITION EFFICIENCY; LINEAR REGRESSION MODELS; MACHINE-LEARNING; MULTI-CLASSIFIER; POST-TRANSCRIPTIONAL; PREDICTION ACCURACY; PREDICTION MODEL; RECEIVER OPERATING CHARACTERISTICS ANALYSIS; RESEARCH TOPICS; SCORING ALGORITHMS; SENSITIVITY AND SPECIFICITY; SMALL INTERFERING RNA (SIRNA); STACKING ENERGY; STATE OF THE ART; THERAPEUTIC BENEFITS; THREE PARAMETERS;

BIOINFORMATICS; GENES; GIBBS FREE ENERGY; MATHEMATICAL MODELS; NEURAL NETWORKS; REGRESSION ANALYSIS; RNA;

FORECASTING;

SMALL INTERFERING RNA;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOINFORMATICS; ENERGY; EVALUATION; MACHINE LEARNING; POSTTRANSCRIPTIONAL GENE SILENCING; PREDICTION; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SCORING SYSTEM; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; RNA, SMALL INTERFERING; SEQUENCE ANALYSIS, RNA; THERMODYNAMICS;

EID: 84861894210     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2012.02.005     Document Type: Article

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