Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Trait and State Self-Esteem02:08

Trait and State Self-Esteem

11.4K
The term self-esteem is often used generically, to refer to how people feel about themselves. However, according to research, there are three distinct constructs that should not be used interchangeably (Brown & Marshall, 2006). 
11.4K
Polygenic Traits01:18

Polygenic Traits

69.0K
When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
69.0K
Multiple Allele Traits01:49

Multiple Allele Traits

38.0K
The Concept of Multiple Allelism
38.0K
Traits and States01:17

Traits and States

532
Personality traits represent consistent patterns in behavior, thoughts, and emotions, reflecting an individual's tendencies across various situations. For example, extraversion, a well-known trait, manifests in individuals as talkative, energetic, and enthusiastic behaviors. These traits are stable over time, offering a reliable framework for predicting how people might act in different contexts. However, they do not define every moment of an individual's life. In contrast to traits,...
532
X-linked Traits01:19

X-linked Traits

58.3K
In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.
58.3K
Trait Centrality01:21

Trait Centrality

180
Trait centrality refers to the degree to which a particular characteristic influences the overall impression of an individual. Some traits exert a disproportionately strong impact on perception, shaping how people interpret other attributes of a person. Solomon Asch first systematically studied this phenomenon in 1946.Asch’s Experiment on Trait CentralityAsch's seminal study demonstrated the centrality of certain traits through a controlled experiment. Participants were presented with a...
180

You might also read

Related Articles

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

Sort by
Same author

A guide to plant breeding for animal breeders.

G3 (Bethesda, Md.)·2026
Same author

Correction: A functional regulatory variant of MYH3 influences muscle fiber-type composition and intramuscular fat content in pigs.

PLoS genetics·2025
Same author

Evaluation of deep learning for predicting rice traits using structural and single-nucleotide genomic variants.

Plant methods·2024
Same author

On the holobiont 'predictome' of immunocompetence in pigs.

Genetics, selection, evolution : GSE·2023
Same author

Transposable element polymorphisms improve prediction of complex agronomic traits in rice.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2022
Same author

Modeling microRNA-driven post-transcriptional regulation using exon-intron split analysis in pigs.

Animal genetics·2022

Related Experiment Video

Updated: Jan 21, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.7K

A Guide for Using Deep Learning for Complex Trait Genomic Prediction.

Miguel Pérez-Enciso1,2, Laura M Zingaretti3

  • 1Catalan Institution for Research and Advanced Studies (ICREA), Passeig de Lluís Companys 23, 08010 Barcelona, Spain. miguel.perez@uab.es.

Genes
|July 24, 2019
PubMed
Summary

Deep learning (DL) shows promise for predicting phenotypic values from molecular data, outperforming traditional methods. Researchers provide a foundational code for genomic prediction using DL, emphasizing hyperparameter optimization for accuracy.

Keywords:
deep learninggenomic predictionmachine learning

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.6K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

Related Experiment Videos

Last Updated: Jan 21, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.7K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.6K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

Area of Science:

  • Genomics and Bioinformatics
  • Machine Learning and Artificial Intelligence
  • Computational Biology

Background:

  • Deep learning (DL) excels at complex data prediction (images, text, video).
  • DL's application in predicting phenotypic values from molecular data is underexplored.
  • Genomic prediction is crucial for breeding and disease risk assessment.

Purpose of the Study:

  • To explore the theoretical foundations of DL for genomic prediction.
  • To provide a flexible, generic code for implementing DL in genomic prediction.
  • To compare DL architectures, specifically Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs).

Main Methods:

  • Theoretical overview of deep learning algorithms and hyperparameter optimization.
  • Implementation of a Keras-based code, adaptable for specific genomic prediction tasks.
  • Comparative analysis of DL architectures, highlighting CNNs' potential over MLPs.

Main Results:

  • Convolutional Neural Networks (CNNs) demonstrate greater promise than Multilayer Perceptrons (MLPs) in genomic prediction.
  • Careful hyperparameter tuning is essential to prevent overfitting and ensure accurate predictions.
  • DL models are implementable using accessible software like Keras and TensorFlow.

Conclusions:

  • Deep learning offers a powerful approach for predicting phenotypic values from molecular data.
  • The provided Keras-based code facilitates DL implementation in genomic prediction.
  • While DL interpretation can be challenging, its predictive power is valuable for breeding and genetic risk assessment.