Robust classification using average correlations as features (ACF).

ABSTRACT:

Motivation

In single-cell transcriptomics and other omics technologies, large fractions of missing values commonly occur. Researchers often either consider only those features that were measured for each instance of their dataset, thereby accepting severe loss of information, or use imputation which can lead to erroneous results. Pairwise metrics allow for imputation-free classification with minimal loss of data.

Results

Using pairwise correlations as metric, state-of-the-art approaches to classification would include the K-nearest-neighbor- (KNN) and distribution-based-classification-classifier. Our novel method, termed average correlations as features (ACF), significantly outperforms those approaches by training tunable machine learning models on inter-class and intra-clas

SUBMITTER: Schumann Y 

PROVIDER: S-EPMC10026437 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature