Field-theoretic density estimation for biological sequence space with applications to 5' splice site diversity and aneuploidy in cancer.

ABSTRACT: Density estimation in sequence space is a fundamental problem in machine learning that is also of great importance in computational biology. Due to the discrete nature and large dimensionality of sequence space, how best to estimate such probability distributions from a sample of observed sequences remains unclear. One common strategy for addressing this problem is to estimate the probability distribution using maximum entropy (i.e., calculating point estimates for some set of correlations based on the observed sequences and predicting the probability distribution that is as uniform as possible while still matching these point estimates). Building on recent advances in Bayesian field-theoretic density estimation, we present a generalization of this maximum entropy approach that provides gr

SUBMITTER: Chen WC 

PROVIDER: S-EPMC8501885 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature