Dataset Information

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires.

ABSTRACT: Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel deep learning method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

SUBMITTER: Pavlovic M

PROVIDER: S-EPMC10312379 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires.

Pavlović Milena M Scheffer Lonneke L Motwani Keshav K Kanduri Chakravarthi C Kompova Radmila R Vazov Nikolay N Waagan Knut K Bernal Fabian L M FLM Costa Alexandre Almeida AA Corrie Brian B Akbar Rahmad R Al Hajj Ghadi S GS Balaban Gabriel G Brusko Todd M TM Chernigovskaya Maria M Christley Scott S Cowell Lindsay G LG Frank Robert R Grytten Ivar I Gundersen Sveinung S Haff Ingrid Hobæk IH Hovig Eivind E Hsieh Ping-Han PH Klambauer Günter G Kuijjer Marieke L ML Lund-Andersen Christin C Martini Antonio A Minotto Thomas T Pensar Johan J Rand Knut K Riccardi Enrico E Robert Philippe A PA Rocha Artur A Slabodkin Andrei A Snapkov Igor I Sollid Ludvig M LM Titov Dmytro D Weber Cédric R CR Widrich Michael M Yaari Gur G Greiff Victor V Sandve Geir Kjetil GK

Nature machine intelligence 20211116 11

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by impl ...[more]

PMID: 37396030

Similar Datasets

Project description:Microbes affect each other's growth in multiple, often elusive, ways. The ensuing interdependencies form complex networks, believed to reflect taxonomic composition as well as community-level functional properties and dynamics. The elucidation of these networks is often pursued by measuring pairwise interactions in coculture experiments. However, the combinatorial complexity precludes an exhaustive experimental analysis of pairwise interactions, even for moderately sized microbial communities. Here, we used a machine learning random forest approach to address this challenge. In particular, we show how partial knowledge of a microbial interaction network, combined with trait-level representations of individual microbial species, can provide accurate inference of missing edges in the network and putative mechanisms underlying the interactions. We applied our algorithm to three case studies: an experimentally mapped network of interactions between auxotrophic Escherichia coli strains, a community of soil microbes, and a large in silico network of metabolic interdependencies between 100 human gut-associated bacteria. For this last case, 5% of the network was sufficient to predict the remaining 95% with 80% accuracy, and the mechanistic hypotheses produced by the algorithm accurately reflected known metabolic exchanges. Our approach, broadly applicable to any microbial or other ecological network, may drive the discovery of new interactions and new molecular mechanisms, both for therapeutic interventions involving natural communities and for the rational design of synthetic consortia. IMPORTANCE Different organisms in a microbial community may drastically affect each other's growth phenotypes, significantly affecting the community dynamics, with important implications for human and environmental health. Novel culturing methods and the decreasing costs of sequencing will gradually enable high-throughput measurements of pairwise interactions in systematic coculturing studies. However, a thorough characterization of all interactions that occur within a microbial community is greatly limited both by the combinatorial complexity of possible assortments and by the limited biological insight that interaction measurements typically provide without laborious specific follow-ups. Here, we show how a simple and flexible formal representation of microbial pairs can be used for the classification of interactions via machine learning. The approach we propose predicts with high accuracy the outcome of yet-to-be performed experiments and generates testable hypotheses about the mechanisms of specific interactions.

Dataset Information

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires.

Publications

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets