TY - JOUR

T1 - Deep learning approach to the estimation of the ratio of reproductive modes in a partially clonal population

AU - Nikolaeva, T. A.

AU - Poroshina, A. A.

AU - Sherbakov, D. Yu

N1 - Nikolaeva T.A., Poroshina A.A., Sherbakov D.Yu. Deep learning approach to the estimation of the ratio of reproductive modes in a partially clonal population. Vavilovskii Zhurnal Genetiki i Selektsii=Vavilov J Genet Breed. 2025; 29(3):467-473. doi 10.18699/vjgb-25-50 Funding. The study was carried out within the framework of the state budget theme No. 0279-2021-0010 “Genetics of Baikal organism communities: the gene pool structure, conservation strategies”

PY - 2025

Y1 - 2025

N2 - Genetic diversity among biological entities, including populations, species, and communities, serves as a fundamental source of information for understanding their structure and functioning. However, many ecological and evolutionary problems arise from limited and complex datasets, complicating traditional analytical approaches. In this context, our study applies a deep learningbased approach to address a crucial question in evolutionary biology: the balance between sexual and asexual reproduction. Sexual reproduction often disrupts advantageous gene combinations favored by selection, whereas asexual reproduction allows faster proliferation without the need for males, effectively maintaining beneficial genotypes. This research focuses on exploring the coexistence patterns of sexual and asexual reproduction within a single species. We developed a convolutional neural network model specifically designed to analyze the dynamics of populations exhibiting mixed reproductive strategies within changing environments. The model developed here allows one to estimate the ratio of population members who originate from sexual reproduction to the clonal organisms produced by parthenogenetic females. This model assumes the reproductive ratio remains constant over time in populations with dual reproductive strategies and stable population sizes. The approach proposed is suitable for neutral multiallelic marker traits such as microsatellite repeats. Our results demonstrate that the model estimates the ratio of reproductive modes with an accuracy as high as 0.99, effectively handling the complexities posed by small sample sizes. When the training dataset’s dimensionality aligns with the actual data, the model converges to the minimum error much faster, highlighting the significance of dataset design in predictive performance. This work contributes to the understanding of reproductive strategy dynamics in evolutionary biology, showcasing the potential of deep learning to enhance genetic data analysis. Our findings pave the way for future research examining the nuances of genetic diversity and reproductive modes in fluctuating ecological contexts, emphasizing the importance of advanced computational methods in evolutionary studies.

AB - Genetic diversity among biological entities, including populations, species, and communities, serves as a fundamental source of information for understanding their structure and functioning. However, many ecological and evolutionary problems arise from limited and complex datasets, complicating traditional analytical approaches. In this context, our study applies a deep learningbased approach to address a crucial question in evolutionary biology: the balance between sexual and asexual reproduction. Sexual reproduction often disrupts advantageous gene combinations favored by selection, whereas asexual reproduction allows faster proliferation without the need for males, effectively maintaining beneficial genotypes. This research focuses on exploring the coexistence patterns of sexual and asexual reproduction within a single species. We developed a convolutional neural network model specifically designed to analyze the dynamics of populations exhibiting mixed reproductive strategies within changing environments. The model developed here allows one to estimate the ratio of population members who originate from sexual reproduction to the clonal organisms produced by parthenogenetic females. This model assumes the reproductive ratio remains constant over time in populations with dual reproductive strategies and stable population sizes. The approach proposed is suitable for neutral multiallelic marker traits such as microsatellite repeats. Our results demonstrate that the model estimates the ratio of reproductive modes with an accuracy as high as 0.99, effectively handling the complexities posed by small sample sizes. When the training dataset’s dimensionality aligns with the actual data, the model converges to the minimum error much faster, highlighting the significance of dataset design in predictive performance. This work contributes to the understanding of reproductive strategy dynamics in evolutionary biology, showcasing the potential of deep learning to enhance genetic data analysis. Our findings pave the way for future research examining the nuances of genetic diversity and reproductive modes in fluctuating ecological contexts, emphasizing the importance of advanced computational methods in evolutionary studies.

KW - Hardy-Weinberg equilibrium

KW - convolutional neural network (CNN)

KW - deep learning

KW - microsatellites

KW - partially clonal population

UR - https://www.scopus.com/pages/publications/105012299865

UR - https://www.mendeley.com/catalogue/22126c04-214b-31a8-815a-41cead77c6bd/

U2 - 10.18699/vjgb-25-50

DO - 10.18699/vjgb-25-50

M3 - Article

C2 - 40556975

VL - 29

SP - 467

EP - 473

JO - Вавиловский журнал генетики и селекции

JF - Вавиловский журнал генетики и селекции

SN - 2500-0462

IS - 3

M1 - 14

ER -