medical-6

Immune Repertoire Classification

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Hopfield Networks is All You Need

Sepp Hochreiter, G. Klambauer, David P. Kreil, Michael Kopp, Johannes Brandstetter, Michael Widrich, Hubert Ramsauer, Philipp Seidl, Bernhard Schafl, Johannes Lehner, Lukas Gruber, Markus Holzleitner, Milena Pavlovi'c, G. K. Sandve, V. Greiff•Wed Jul 15 2020

A new PyTorch layer is provided, called "Hopfield", which allows to equip deep learning architectures with modern Hopfield networks as a new powerful concept comprising pooling, memory, and attention.

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A Noisy-Label-Learning Formulation for Immune Repertoire Classification and Disease-Associated Immune Receptor Sequence Identification

Jianhua Yao, Mingcai Chen, Yu Zhao, Zhonghuang Wang, Bing He•Fri Jul 28 2023

A noisy-label-learning formulation to solve the immune repertoire classification task and two models with the same architecture but different parameter initialization are co-trained simultaneously to remedy the known ``confirmation bias'' problem in the self-training-like schema.

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Modern Hopfield Networks and Attention for Immune Repertoire Classification

Sepp Hochreiter, G. Klambauer, Johannes Brandstetter, Michael Widrich, Hubert Ramsauer, Lukas Gruber, Markus Holzleitner, G. K. Sandve, V. Greiff, Bernhard Schäfl, Milena Pavlović•Sun Apr 12 2020

This work presents a novel method DeepRC that integrates transformer-like attention, or equivalently modern Hopfield networks, into deep learning architectures for massive MIL such as immune repertoire classification, and demonstrates that DeepRC outperforms all other methods with respect to predictive performance on large-scale experiments.

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