Machine learning for the analysis of adaptive immune receptors and repertoires (On Demand)

Adaptive immune receptor repertoires (AIRRs) capture past and present immune responses and therefore represent a powerful resource for developing diagnostics and therapeutics. Machine learning (ML) has the ability to discover complex sequence patterns and help further these diagnostic and therapeutic aims. However, to exploit these opportunities, it is necessary to overcome the intrinsic challenges of AIRR data: unknown rules determining antigen binding, high diversity and specificity of receptors with low overlap between AIRRs, and low signal-to-noise ratio. Further, different ML approaches need to be validated and compared before they could be deployed in practice. In this webinar, we will focus on standardized and reproducible ML workflows, benchmarking, and comparison of AIRR ML approaches. We will argue for the use of simulation for validation and benchmarking of ML methods before moving to experimental datasets.

Seminar Information
Date Presented:
November 15, 2022 11:00 AM Eastern
Length:
1 hour
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Machine learning for the analysis of adaptive immune receptors and repertoires

Adaptive immune receptor repertoires (AIRRs) capture past and present immune responses and therefore represent a powerful resource for developing diagnostics and therapeutics. Machine learning (ML) has the ability to discover complex sequence patterns and help further these diagnostic and therapeutic aims. However, to exploit these opportunities, it is necessary to overcome the intrinsic challenges of AIRR data: unknown rules determining antigen binding, high diversity and specificity of receptors with low overlap between AIRRs, and low signal-to-noise ratio. Further, different ML approaches need to be validated and compared before they could be deployed in practice. In this webinar, we will focus on standardized and reproducible ML workflows, benchmarking, and comparison of AIRR ML approaches. We will argue for the use of simulation for validation and benchmarking of ML methods before moving to experimental datasets.

Speaker Information
Maria Chernigovskaya  [ view bio ]
Milena Pavlovic  [ view bio ]
Individual topic purchase: Selected