Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity

Systemic light chain amyloidosis (AL) is caused by the production of toxic light chains and can be fatal, yet effective treatments are often not possible due to delayed diagnosis. Here the authors show that a machine learning platform analyzing light chain somatic mutations allows the prediction of...

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Autores principales:	Maura Garofalo, Luca Piccoli, Margherita Romeo, Maria Monica Barzago, Sara Ravasio, Mathilde Foglierini, Milos Matkovic, Jacopo Sgrignani, Raoul De Gasparo, Marco Prunotto, Luca Varani, Luisa Diomede, Olivier Michielin, Antonio Lanzavecchia, Andrea Cavalli
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/5850900427bf4ded89d2135be99bb200
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spelling	oai:doaj.org-article:5850900427bf4ded89d2135be99bb2002021-12-02T17:34:47ZMachine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity10.1038/s41467-021-23880-92041-1723https://doaj.org/article/5850900427bf4ded89d2135be99bb2002021-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23880-9https://doaj.org/toc/2041-1723Systemic light chain amyloidosis (AL) is caused by the production of toxic light chains and can be fatal, yet effective treatments are often not possible due to delayed diagnosis. Here the authors show that a machine learning platform analyzing light chain somatic mutations allows the prediction of light chain toxicity to serve as a possible tool for early diagnosis of AL.Maura GarofaloLuca PiccoliMargherita RomeoMaria Monica BarzagoSara RavasioMathilde FoglieriniMilos MatkovicJacopo SgrignaniRaoul De GasparoMarco PrunottoLuca VaraniLuisa DiomedeOlivier MichielinAntonio LanzavecchiaAndrea CavalliNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Maura Garofalo Luca Piccoli Margherita Romeo Maria Monica Barzago Sara Ravasio Mathilde Foglierini Milos Matkovic Jacopo Sgrignani Raoul De Gasparo Marco Prunotto Luca Varani Luisa Diomede Olivier Michielin Antonio Lanzavecchia Andrea Cavalli Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity
description	Systemic light chain amyloidosis (AL) is caused by the production of toxic light chains and can be fatal, yet effective treatments are often not possible due to delayed diagnosis. Here the authors show that a machine learning platform analyzing light chain somatic mutations allows the prediction of light chain toxicity to serve as a possible tool for early diagnosis of AL.
format	article
author	Maura Garofalo Luca Piccoli Margherita Romeo Maria Monica Barzago Sara Ravasio Mathilde Foglierini Milos Matkovic Jacopo Sgrignani Raoul De Gasparo Marco Prunotto Luca Varani Luisa Diomede Olivier Michielin Antonio Lanzavecchia Andrea Cavalli
author_facet	Maura Garofalo Luca Piccoli Margherita Romeo Maria Monica Barzago Sara Ravasio Mathilde Foglierini Milos Matkovic Jacopo Sgrignani Raoul De Gasparo Marco Prunotto Luca Varani Luisa Diomede Olivier Michielin Antonio Lanzavecchia Andrea Cavalli
author_sort	Maura Garofalo
title	Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity
title_short	Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity
title_full	Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity
title_fullStr	Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity
title_full_unstemmed	Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity
title_sort	machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5850900427bf4ded89d2135be99bb200
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Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity

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