Identification of natural selection in genomic data with deep convolutional neural network.

ABSTRACT:

Background

With the increase in the size of genomic datasets describing variability in populations, extracting relevant information becomes increasingly useful as well as complex. Recently, computational methodologies such as Supervised Machine Learning and specifically Convolutional Neural Networks have been proposed to make inferences on demographic and adaptive processes using genomic data. Even though it was already shown to be powerful and efficient in different fields of investigation, Supervised Machine Learning has still to be explored as to unfold its enormous potential in evolutionary genomics.

Results

The paper proposes a method based on Supervised Machine Learning for classifying genomic data, represented as windows of genomic sequences from a sample of individua

SUBMITTER: Nguembang Fadja A 

PROVIDER: S-EPMC8642854 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature