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Related Concept Videos

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...

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Related Experiment Video

Updated: Jul 15, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Improved residue contact prediction using support vector machines and a large feature set.

Jianlin Cheng1, Pierre Baldi

  • 1School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816-2362, USA. jianlin.cheng@gmail.com

BMC Bioinformatics
|April 5, 2007
PubMed
Summary

SVMcon, a novel protein contact map predictor, enhances medium- and long-range contact prediction accuracy using support vector machines (SVMs). This method shows improved performance over existing tools and ranks highly in protein structure prediction assessments.

Related Experiment Videos

Last Updated: Jul 15, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Science

Background:

  • Protein residue-residue contact prediction is crucial for understanding protein folding and ab initio structure prediction.
  • Despite advancements, accurately predicting protein contacts remains a significant challenge in bioinformatics.

Purpose of the Study:

  • To develop a new computational tool, SVMcon, for predicting medium- and long-range protein residue-residue contacts.
  • To improve the accuracy and performance of contact map prediction methods.

Main Methods:

  • Developed SVMcon, a contact map predictor utilizing support vector machines (SVMs).
  • Integrated diverse features including sequence profiles, secondary structure, solvent accessibility, and contact potentials.
  • Evaluated SVMcon's performance against existing predictors on standard datasets.

Main Results:

  • SVMcon demonstrated a 4% higher accuracy compared to the CMAPpro predictor on the same test dataset.
  • In the CASP7 experiment, SVMcon ranked among the top predictors for medium- and long-range contacts.
  • Achieved the second-best coverage and accuracy for contacts with sequence separation >= 12 across 13 de novo domains.

Conclusions:

  • SVMcon is an effective new tool for predicting protein residue-residue contacts, particularly for medium- and long-range interactions.
  • The predictor's performance is attributed to the integration of a comprehensive set of informative features.
  • SVMcon can be seamlessly integrated into existing protein structure prediction pipelines.