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Related Concept Videos

Ranks01:02

Ranks

Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
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Unusual Results

Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
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Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Weighted Mean00:57

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

How different from random are docking predictions when ranked by scoring functions?

Elisenda Feliu1, Baldomero Oliva

  • 1Algebra and Geometry Department, Mathematics Faculty, Universitat de Barcelona, Spain.

Proteins
|September 18, 2010
PubMed
Summary
This summary is machine-generated.

Statistical assessment of protein-protein interaction scoring functions reveals limitations in current methods. This protein docking analysis suggests improvements are needed for the Critical Assessment of PRedicted Interactions (CAPRI) scoring experiment to ensure reliability.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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Last Updated: Jun 8, 2026

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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Biophysics

Background:

  • Protein-protein interactions are crucial for biological processes.
  • Docking algorithms predict these interactions by sampling orientations and ranking predictions.
  • Scoring functions are vital for ranking near-native protein complex structures.

Purpose of the Study:

  • To statistically assess the effectiveness of scoring functions in protein-protein docking.
  • To evaluate the reliability of current scoring in protein interaction prediction.
  • To propose improvements for the Critical Assessment of PRedicted Interactions (CAPRI) scoring experiment.

Main Methods:

  • Statistical analysis of scoring functions applied to a benchmark dataset of protein complex decoys.
  • Assessment of statistical significance using P-values based on near-native structure counts.
  • Evaluation of filtering redundant structures and pair-potentials from ZDock and ZRank.

Main Results:

  • For many targets, distinguishing successful reranking from random chance is not possible.
  • The statistical significance of scoring function performance in protein docking was quantified.
  • The current CAPRI scoring experiment may not reliably differentiate true successes from random outcomes.

Conclusions:

  • Current scoring functions in protein docking may not be sufficiently robust.
  • The design of the CAPRI scoring experiment requires modification to enhance reliability.
  • Incorporating statistical assessment into CAPRI's preprocessing or evaluation steps is recommended.