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Related Concept Videos

Correlation01:09

Correlation

In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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...
Correspondence Bias01:17

Correspondence Bias

Correspondence bias, also referred to as the fundamental attribution error, describes the tendency to attribute another person’s behavior to internal characteristics rather than situational influences. This cognitive bias leads individuals to overlook external factors that may be influencing actions, thereby fostering potentially inaccurate assessments of others’ intentions and dispositions.Empirical Evidence for Correspondence BiasResearch has consistently demonstrated the prevalence of...
Correlations02:20

Correlations

Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
Coefficient of Correlation01:12

Coefficient of Correlation

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...
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...

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Related Experiment Videos

Improving consensus contact prediction via server correlation reduction.

Xin Gao1, Dongbo Bu, Jinbo Xu

  • 1David R, Cheriton School of Computer Science, University of Waterloo, N2L3G1, Canada. x4gao@cs.uwaterloo.ca

BMC Structural Biology
|May 8, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new integer linear programming model to improve protein contact prediction, especially for new fold targets. The method significantly enhances accuracy by optimally combining predictions from different servers.

Related Experiment Videos

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Machine Learning in Biology

Background:

  • Protein inter-residue contacts are vital for determining and predicting protein structures.
  • Existing consensus methods excel with known templates but struggle with novel protein folds.
  • Threading programs generate models with true contacts for new folds, but identifying them is challenging.

Purpose of the Study:

  • To develop an improved computational method for protein contact prediction, particularly for novel protein structures.
  • To address the limitations of traditional consensus methods in identifying true contacts from threading models for new fold targets.

Main Methods:

  • Developed an integer linear programming (ILP) model for consensus contact prediction.
  • Evaluated server correlations using maximum likelihood estimation.
  • Extracted independent latent servers via principal component analysis.
  • Applied ILP to weight latent servers, maximizing true contact identification.

Main Results:

  • Achieved 73% average accuracy on the CASP7 dataset for the top L/5 predicted contacts (L=protein size), surpassing previous methods.
  • Demonstrated superior performance on 15 new fold CASP7 targets, reaching 37% average accuracy.
  • Outperformed majority voting, SVM-LOMETS, SVM-SEQ, and SAM-T06 on new fold targets.

Conclusions:

  • Reducing server correlation and optimally combining latent servers significantly improves protein contact prediction accuracy.
  • The developed ILP approach offers a powerful tool for protein structure refinement and prediction.
  • This method shows particular promise for tackling the challenge of predicting structures for novel protein folds.