Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification.

ABSTRACT:

Background

Machine learning (ML) methodology development for the classification of immune states in adaptive immune receptor repertoires (AIRRs) has seen a recent surge of interest. However, so far, there does not exist a systematic evaluation of scenarios where classical ML methods (such as penalized logistic regression) already perform adequately for AIRR classification. This hinders investigative reorientation to those scenarios where method development of more sophisticated ML approaches may be required.

Results

To identify those scenarios where a baseline ML method is able to perform well for AIRR classification, we generated a collection of synthetic AIRR benchmark data sets encompassing a wide range of data set architecture-associated and immune state-associated seque

SUBMITTER: Kanduri C 

PROVIDER: S-EPMC9154052 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature