Cargando…
Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification
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 penali...
Autores principales: | Kanduri, Chakravarthi, Pavlović, Milena, Scheffer, Lonneke, Motwani, Keshav, Chernigovskaya, Maria, Greiff, Victor, Sandve, Geir K |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9154052/ https://www.ncbi.nlm.nih.gov/pubmed/35639633 http://dx.doi.org/10.1093/gigascience/giac046 |
Ejemplares similares
-
simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods
por: Kanduri, Chakravarthi, et al.
Publicado: (2023) -
CompAIRR: ultra-fast comparison of adaptive immune receptor repertoires by exact and approximate sequence matching
por: Rognes, Torbjørn, et al.
Publicado: (2022) -
immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking
por: Weber, Cédric R, et al.
Publicado: (2020) -
In silico proof of principle of machine learning-based antibody design at unconstrained scale
por: Akbar, Rahmad, et al.
Publicado: (2022) -
Individualized VDJ recombination predisposes the available Ig sequence space
por: Slabodkin, Andrei, et al.
Publicado: (2021)