Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data.

ABSTRACT: Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under nonconvex settings and f

SUBMITTER: Amin MR 

PROVIDER: S-EPMC10581699 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature