Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

Hypothesis: Accept or Fail to Reject?

Hypothesis: Accept or Fail to Reject?

The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null hypothesis and 'fail to...

Natural and Artificial Concepts

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint Vincent in...

Prediction Intervals

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...

Inductive Reasoning

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

An integrated, scaled approach to resolve TSC2 variants of uncertain significance.

Nature communications·2026

Same author

Saturation Genome Editing reveals the functional impact of RAD51D <i>and</i> XRCC2 variants.

bioRxiv : the preprint server for biology·2026

Same author

Improving splice site usage prediction with SPLAIRE.

bioRxiv : the preprint server for biology·2026

Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026

Same author

Gene- and domain-aware calibration increases the clinical utility of variant effect predictors.

Research square·2026

Same author

Advances in Protein Function Prediction from the Fifth CAFA Challenge.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: May 10, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Information-theoretic evaluation of predicted ontological annotations.

Wyatt T Clark¹, Predrag Radivojac

¹Department of Computer Science and Informatics, Indiana University, Bloomington, IN 47405, USA. predrag@indiana.edu

Bioinformatics (Oxford, England)

|July 2, 2013

Summary

This summary is machine-generated.

This study introduces a novel information-theoretic framework for evaluating protein function prediction tools. It offers a more robust method for assessing computational biology algorithms, addressing limitations in current performance metrics.

Related Experiment Videos

Last Updated: May 10, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Computational biology
Bioinformatics
Genomics

Background:

Evaluating computational biology tools for protein function prediction is challenging due to complex ontologies and data limitations.
Existing performance metrics for gene annotation and disease prioritization have inherent weaknesses.
Accurate prediction of ontological annotations is crucial for advancing biological research.

Purpose of the Study:

To develop a novel information-theoretic framework for evaluating computational protein function prediction.
To address the limitations of current metrics used in assessing biological annotation tools.
To provide a more reliable method for ranking protein function prediction models.

Main Methods:

Utilized a Bayesian network, structured by ontology, to model protein function probabilities.
Introduced 'misinformation' and 'remaining uncertainty' as information-theoretic analogs of precision and recall.
Developed a 'semantic distance' statistic for ranking classification models.

Main Results:

The proposed framework effectively evaluates computational protein function prediction.
The semantic distance metric provides a single statistic for ranking prediction models.
Analysis of three Gene Ontology term predictors demonstrated the framework's advantages over existing metrics.

Conclusions:

The information-theoretic framework offers valuable insights into the performance of protein function prediction tools.
This approach provides a more robust evaluation method compared to current metrics.
The framework has the potential to improve the reliability of computational biology predictions.