thomamin

• Riccardi, Enrico; Minotto, Thomas; Haff, Ingrid Hobæk & Sandve, Geir Kjetil Ferkingstad (2025). Connecting instance and model sparsity in multiple instance learning. Machine Learning with Applications (MLWA). 21, s. 100679–100679. doi: 10.1016/j.mlwa.2025.100679. Fulltekst i vitenarkiv
• Minotto, Thomas; Haff, Ingrid Hobæk; Riccardi, Enrico & Sandve, Geir Kjetil Ferkingstad (2025). Detecting statistical interactions in immune receptor data: a comparative study. Journal of Applied Statistics. ISSN 0266-4763. s. 22–22. doi: 10.1080/02664763.2025.2533483. Fulltekst i vitenarkiv
• Minotto, Thomas; Haff, Ingrid Hobæk & Sandve, Geir Kjetil Ferkingstad (2022). Detecting statistical interactions in immune receptor datasets. I Torelli, Nicola; BELLIO, RUGGERO & MUGGEO, VITO (Red.), Proceedings of the 36th International Workshop on Statistical Modelling. EUT Edizioni Università di Trieste. ISSN 9788855113090. s. 253–257. Fulltekst i vitenarkiv Vis sammendrag

  Recent progress in the understanding of immune receptors suggests that complex interactions between amino acids are important in determining binding to antigens. Yet, current methods focus mostly on constructing good predictors for the data with complex models, and less on the understanding of the underlying interaction effects. Here, we attempt to retrieve high-order statistical interactions in immune receptor data with different methods. We study performance at this task in a large simulation study, and how it depends on the order, amount and strength of the interactions, witness rate and sample size. The results show that pairwise interactions are easily retrieved, but model complexity harms detection. Interactions are better detected in larger samples, but the process is then slower.

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