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

Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.

Behavioral Genetics and Its Designs

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...

You might also read

Related Articles

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

Sort by

Same author

LSTM-attention-guided graph neural networks for integrated genotype-Environment modeling in maize yield prediction.

PLoS computational biology·2026

Same author

Regression augmentation with data-driven segmentation.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Obscured-ensemble models for genomic prediction.

PloS one·2025

Same author

Conditional probabilistic diffusion model driven synthetic radiogenomic applications in breast cancer.

PLoS computational biology·2024

Same author

Crop genomic selection with deep learning and environmental data: A survey.

Frontiers in artificial intelligence·2023

Same author

Minimizing features while maintaining performance in data classification problems.

PeerJ. Computer science·2022

Search research articles

Related Experiment Video

Updated: Aug 23, 2025

Competitive Genomic Screens of Barcoded Yeast Libraries

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Sparse bayesian learning for genomic selection in yeast.

Maryam Ayat¹, Mike Domaratzki²

¹Lactanet, Sainte-Anne-deBellevue, QC, Canada.

Frontiers in Bioinformatics

|October 28, 2022

Summary

Sparse Bayesian learning offers a powerful machine learning approach for genomic selection in crops. These methods accurately predict complex traits and identify key genetic markers influencing crop performance.

Keywords:

bayesian learning explainable artificial intelligence genomic selection kernel learning machine learning marker assisted selection relevance vector machine

More Related Videos

Mating-based Overexpression Library Screening in Yeast

Mating-based Overexpression Library Screening in Yeast

Published on: July 6, 2018

A Quantitative Fitness Analysis Workflow

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

Related Experiment Videos

Last Updated: Aug 23, 2025

Competitive Genomic Screens of Barcoded Yeast Libraries

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Mating-based Overexpression Library Screening in Yeast

Mating-based Overexpression Library Screening in Yeast

Published on: July 6, 2018

A Quantitative Fitness Analysis Workflow

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

Area of Science:

Genomics
Machine Learning
Biotechnology

Background:

Genomic selection predicts crop phenotypes using genomic markers.
Machine learning techniques are increasingly used for complex trait prediction.
Identifying influential genetic markers is crucial for crop improvement.

Purpose of the Study:

To explore sparse Bayesian learning and ensemble learning for genomic selection.
To develop methods for predicting phenotypes and ranking genetic markers.
To assess the performance of these methods on a Saccharomyces cerevisiae dataset.

Main Methods:

Utilized sparse Bayesian learning for phenotype prediction.
Employed ensemble learning for marker relevance ranking.
Applied methods to a Saccharomyces cerevisiae dataset for analysis.

Main Results:

Sparse Bayesian methods demonstrated competitive accuracy in predicting yeast growth.
The methods successfully identified influential genetic markers with both positive and negative effects.
Results were compared against existing machine learning techniques and trait heritability.

Conclusions:

Sparse Bayesian learning is effective for genomic selection and marker identification.
The proposed ensemble of sparse Bayesian learners is valuable for ranking markers by trait relevance.
These findings provide insights for biologists and advance crop breeding strategies.