Machine learning predicts immunoglobulin light chain toxicity through somatic mutations

Abstract excerpt

In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LCs) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage. However, delays in diagnosis are common, with a consequent poor patient’s prognosis, as symptoms usually appear only after strong organ involvement. Here, we present LICTOR, a machine learning appro...

Topics

Open a Topic to create a Post that cites this publication.

• Algorithms
• Antibodies
• Caenorhabditis elegans
• Databases, Genetic
• Gene Expression
• Immunoglobulin Light Chains
• Machine Learning
• Recombinant Proteins

Identifiers and source

Literature Corpus work

85921f45-c1ad-537f-a32c-dc5def971b1f

DOI

10.1101/849901