Pan-Peptide Meta Learning for T-cell receptor–antigen binding recognition

The pitfalls of negative data bias for the T-cell epitope specificity challenge

“…The same is true for the T-cell epitope prediction challenge, as recently tackled by Gao et al 5 T-cells are a critical part of the adaptive immune system, as they recognize intruders from self, induce immune responses, and retain memory. The recognition of foreign intruders is mediated…”

“…Even small biases within a dataset often suffice for a machine learning model to overfit on bogus data characteristics and drive its predictive behavior. Crucially, if the same bias persists in any held-out test data, this issue will remain undetected.The same is true for the T-cell epitope prediction challenge, as recently tackled by Gao et al 5 T-cells are a critical part of the adaptive immune system, as they recognize intruders from self, induce immune responses, and retain memory. The recognition of foreign intruders is mediated…”

“…However, because of the limited number of epitopes with known TCRs, it is complex to design a held-out negative dataset using this approach. The second strategy, applied by Gao et al 5 , is using background TCR data. In this case, epitopes from known positive samples are paired with random TCRs from a background set, which is often obtained from a broad sequencing experiment without epitope specificity.…”

Reply to: The pitfalls of negative data bias for the T-cell epitope specificity challenge

“…efforts to raise this point for T-cell epitope specificity modeling, which is known clearly by the community that different negative data sampling strategy will influence the prediction results 2,3 . Therefore, proper negative data sampling strategy should be carefully selected, and this is exactly what PanPep has noticed, emphasized and performed 4 . In short, as for the two commonly used negative sampling strategy, i.e., reshuffling based on positive pairs (first strategy) and randomly drawing from background repertories (second strategy), PanPep prefers to select the second strategy, and the rational has been clearly indicated in the manuscript.…”

“…In sum, modeling is challenging, seen as the 'holy grail' of immunology as indicated 11 . Now we would like to emphasize again the novelty of PanPep in addressing such zero-shot prediction facing the very challenging "long-tail" issue 12 , and this conceptual methodology novelty should not be overlooked.…”