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Related Concept Videos

Frequency-dependent Selection01:21

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.Positive Frequency-Dependent SelectionIn positive...
Types of Selection01:46

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
Unrealistic Optimism Bias01:30

Unrealistic Optimism Bias

Unrealistic optimism bias is the tendency to overestimate the likelihood of positive outcomes. This cognitive bias makes individuals believe they are less likely to experience failures, setbacks, or risks and more likely to succeed than others. For example, people may assume they are less prone to health issues, accidents, or financial struggles than their peers, even when they share similar risk factors.One key component of this bias is the above-average effect, where individuals perceive...
Negative and Positive Feedback01:18

Negative and Positive Feedback

Animal organs and organ systems constantly adjust to internal and external changes through a process called homeostasis ("steady state"). Examples of these changes include regulation of the level of glucose or calcium in the blood or internal responses to external temperatures. Homeostasis requires  maintaining an internal dynamic equilibrium:

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Related Experiment Videos

Negative frequency-dependent selection: a positive outlook with deep learning.

Cindy Gilda Santander1,2, Andre Luiz Campelo Dos Santos1, Sandipan Paul Arnab1,2

  • 1Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL33431, USA.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|June 11, 2026
PubMed
Summary
This summary is machine-generated.

Negative frequency-dependent selection (NFDS) maintains genetic diversity but is hard to distinguish from other selection types. New deep learning models can now effectively detect NFDS using genomic data, aiding evolutionary studies.

Keywords:
balancing selectionfeature extractiontransfer learning

Related Experiment Videos

Area of Science:

  • Evolutionary genetics
  • Population genetics
  • Genomics

Background:

  • Balancing selection preserves genetic diversity via mechanisms like negative frequency-dependent selection (NFDS).
  • Distinguishing the genomic footprint of NFDS from other balancing selection modes (e.g., overdominance) is a significant challenge.
  • Accurate identification of NFDS is crucial for understanding evolutionary processes and genetic diversity maintenance.

Purpose of the Study:

  • To outline strategies for improving models of genomic patterns under NFDS.
  • To enhance the differentiation of NFDS signatures from neutrality and other selection processes.
  • To provide practical recommendations for detecting NFDS in genomic data.

Main Methods:

  • Utilizing resource-efficient deep transfer learning.
  • Implementing novel data preprocessing techniques.
  • Modelling genomic autocovariation for pattern detection.
  • Applying methods to phased/unphased genotypes and ancient DNA (aDNA) data.

Main Results:

  • Demonstrated effective detection and characterization of NFDS using advanced deep learning.
  • Showcased the utility of genomic autocovariation modelling.
  • Validated the approach with diverse genotype data and temporal information from aDNA.
  • Improved ability to distinguish NFDS from neutral and other selection signals.

Conclusions:

  • Deep transfer learning offers a powerful approach for identifying NFDS signatures in genomic data.
  • Improved modelling strategies are essential for resolving complex evolutionary selection patterns.
  • Recommendations are provided for researchers to advance NFDS detection in empirical and computational studies.