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Related Concept Videos

Experimental RNAi02:15

Experimental RNAi

RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Updated: Jun 23, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

A framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction.

Shibin Qiu1, Terran Lane

  • 1Pathwork Diagnostics, Inc, Sunnyvale, Sunnyvale, CA 94089, USA. sqiu@pathworkdx.com

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|May 2, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces multiple kernel regression to predict RNA interference (RNAi) efficacy. This method improves accuracy and computational speed for selecting effective small interfering RNA (siRNA) molecules for gene silencing applications.

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MISSION esiRNA for RNAi Screening in Mammalian Cells
15:31

MISSION esiRNA for RNAi Screening in Mammalian Cells

Published on: May 12, 2010

Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • RNA interference (RNAi) is a crucial cellular mechanism with therapeutic potential for human diseases.
  • Accurate prediction of small interfering RNA (siRNA) silencing efficacy is vital for gene function analysis and therapeutic development.
  • Current computational methods often rely on numerical kernels, potentially overlooking valuable string-based information from siRNA and target mRNA sequences.

Purpose of the Study:

  • To develop a novel computational framework for predicting siRNA silencing efficacy.
  • To integrate both string and numerical data for enhanced prediction accuracy.
  • To improve the efficiency and reduce the complexity of siRNA screening processes.

Main Methods:

  • Proposed a multiple kernel regression framework to unify string kernel functions and numerical descriptors.
  • Formulated multiple kernel learning as a quadratically constrained quadratic programming (QCQP) problem.
  • Developed three heuristic approaches based on kernel-target alignment and predictive accuracy to address computational demands.

Main Results:

  • Multiple kernel regression significantly improved prediction accuracy for siRNA efficacy.
  • The proposed method reduced model complexity by decreasing the number of support vectors.
  • Computational performance was dramatically accelerated, making it more practical for large-scale screening.

Conclusions:

  • Multiple kernel regression offers a superior approach to predicting siRNA silencing efficacy by leveraging diverse data types.
  • This framework provides insights into the relative importance of different siRNA design rules.
  • The developed heuristics offer computationally efficient solutions for optimizing siRNA selection in research and therapy.