DeepBreaks identifies and prioritizes genotype-phenotype associations using machine learning.

ABSTRACT: Sequence data, such as nucleotides or amino acids, are crucial in advancing our understanding of biology. However, investigating and analyzing sequencing data and genotype-phenotype associations present several challenges, including noise components that arise from the sequencing, nonlinear genotype-phenotype associations, collinearity between input features, and high dimensionality of the input data. Machine learning (ML) algorithms have proven to be effective in detecting intricate and nonstructural patterns, making them a valuable tool for studies focused on genotype-phenotype associations. Yet, there needs to be more user-friendly ML implementations that leverage the unique features of high-volume DNA sequence data. Here, we introduce deepBreaks, a generic approach that detects importa

SUBMITTER: Baghbanzadeh M 

PROVIDER: S-EPMC12594848 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature