๐๐ผ๐ต๐ผ๐ฟ๐ ๐๐๐๐ฑ๐ ๐ผ๐ณ ๐๐ต๐ฒ ๐๐ฒ๐ฒ๐ธ - The article โ๐๐ช๐ด๐ฆ๐ข๐ด๐ฆ ๐ฅ๐ช๐ข๐จ๐ฏ๐ฐ๐ด๐ต๐ช๐ค๐ด ๐ถ๐ด๐ช๐ฏ๐จ ๐ฎ๐ข๐ค๐ฉ๐ช๐ฏ๐ฆ ๐ญ๐ฆ๐ข๐ณ๐ฏ๐ช๐ฏ๐จ ๐ฐ๐ง ๐ ๐ค๐ฆ๐ญ๐ญ ๐ข๐ฏ๐ฅ ๐ ๐ค๐ฆ๐ญ๐ญ ๐ณ๐ฆ๐ค๐ฆ๐ฑ๐ต๐ฐ๐ณ ๐ด๐ฆ๐ฒ๐ถ๐ฆ๐ฏ๐ค๐ฆ๐ดโ published in
@ScienceMagazine from corresponding authors Anshul Kundaje,
@ScottBoydLab and first authors
@zazius and Erin Craig developed an integrative model, Mal-ID (Machine Learning for Immunological Diagnosis), to address a gap in current diagnostic medical practices, which do not yet utilize the adaptive immune systemโs B cell receptor (BCR) and T cell receptor (TCR) sequences in diagnosis. Mal-ID integrates three models: analysis of overall repertoire composition, convergent clustering of antigen-specific sequences by edit distance and immune receptor sequence features extracted from a large protein language model.
Mal-ID outperformed similar classification approaches with AUROCs up to 0.98, representing a major advancement in diagnostic accuracy when applied to a study cohort of 542 samples. This high level of performance was supportive of the modelโs analysis of both BCR and TCR repertoire data, which provided more accurate classification than either receptor type alone, potentially reflecting variation in the roles of B cell and T cell responses in different diseases.
A diagram of a diagram of a computer model
๐๐ฒ๐ ๐๐ถ๐ป๐ฑ๐ถ๐ป๐ด๐:
๐๐ถ๐ด๐ต ๐๐ถ๐ฎ๐ด๐ป๐ผ๐๐๐ถ๐ฐ ๐๐ฐ๐ฐ๐๐ฟ๐ฎ๐ฐ๐: Mal-ID uses BCR and TCR repertoire data to effectively distinguish six distinct immune statesโCOVID-19, HIV, lupus, type 1 diabetes, recent influenza vaccination responses, and healthy controlsโin a study involving 542 individuals.
๐จ๐๐ฒ ๐ผ๐ณ ๐๐ฎ๐ฟ๐ด๐ฒ-๐ฆ๐ฐ๐ฎ๐น๐ฒ ๐ฃ๐ฟ๐ผ๐๐ฒ๐ถ๐ป ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐ ๐ณ๐ผ๐ฟ ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐
๐๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป: Mal-ID leveraged advanced protein language models to extract meaningful immune receptor sequence features for improved diagnostic classification.
๐ง๐ฎ๐ธ๐ฒ ๐ต๐ผ๐บ๐ฒ: By applying protein language models to BCR and TCR sequence data, Mal-ID demonstrates that AI can be used to realize powerful repertoire-based diagnostics.
science.org/doi/epdf/10.1126โฆ