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A central mechanism in machine learning is to identify, store, and recognize patterns.
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Tetramer-visualized gluten-specific CD4+ T cells in blood as a potential diagnostic marker for coeliac disease without oral gluten challenge
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The promise and challenge of high-throughput sequencing of the antibody repertoire
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A bioinformatic framework for immune repertoire diversity profiling enables detection of immunological status
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VDJtools: unifying post-analysis of T cell receptor repertoires
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Quantifiable predictive features define epitope-specific T cell receptor repertoires
Learning embedding adaptation for few-shot learning
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A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding
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Explaining and interpreting LSTMs
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Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires
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Dash, P., Fiore-Gartland, A. J., Hertz, T., Wang, G. C., Sharma, S., Souquette, A., Crawford, J. C., Clemens, E. B., Nguyen, T. H., Kedzierska, K., et al · 2017
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On a model of associative memory with huge storage capacity
Demircigil, M., Heusel, J., Löwe, M., Upgang, S., and Vermet, F · 2017
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Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire
Emerson, R. O., DeWitt, W. S., Vignali, M., Gravley, J., Hu, J. K., Osborne, E. J., Desmarais, C., Klinger, M., Carlson, C. S., Hansen, J. A., et al · 2017
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Identifying specificity groups in the T cell receptor repertoire
Glanville, J., Huang, H., Nau, A., Hatton, O., Wagar, L. E., Rubelt, F., Ji, X., Han, A., Krams, S. M., Pettus, C., et al · 2017
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Learning the high-dimensional immunogenomic features that predict public and private antibody repertoires
Greiff, V., Weber, C. R., Palme, J., Bodenhofer, U., Miho, E., Menzel, U., and Reddy, S. T · 2017
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Self-normalizing neural networks
Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S · 2017
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Pointnet: deep learning on point sets for 3D classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 2017
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Predicting antigen-specificity of single T-cells based on TCR CDR3 regions
Fischer, D. S., Wu, Y., Schubert, B., and Theis, F. J · 2019
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TCRex: detection of enriched T cell epitope specificity in full T cell receptor sequence repertoires
Gielis, S., Moris, P., Bittremieux, W., De Neuter, N., Ogunjimi, B., Laukens, K., and Meysman, P · 2019
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Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images
Kimeswenger, S., Rumetshofer, E., Hofmarcher, M., Tschandl, P., Kittler, H., Hochreiter, S., Hötzenecker, W., and Klambauer, G · 2019
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Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning
Konishi, H., Komura, D., Katoh, H., Atsumi, S., Koda, H., Yamamoto, A., Seto, Y., Fukayama, M., Yamaguchi, R., Imoto, S., et al · 2019
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Set transformer: a framework for attention-based permutation-invariant neural networks
Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., and Teh, Y. W · 2019
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Layer-wise relevance propagation: an overview
Montavon, G., Binder, A., Lapuschkin, S., Samek, W., and Müller, K.-R · 2019
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How many different clonotypes do immune repertoires contain?
Mora, T. and Walczak, A. M · 2019
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Treating biomolecular interaction as an image classification problem – a case study on T-cell receptor-epitope recognition prediction
Moris, P., De Pauw, J., Postovskaya, A., Ogunjimi, B., Laukens, K., and Meysman, P · 2019
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sumrep: a summary statistic framework for immune receptor repertoire comparison and model validation
Olson, B. J., Moghimi, P., Schramm, C., Obraztsova, A., Ralph, D. K., Vander Heiden, J. A., Shugay, M., Shepherd, A. J., Lees, W. D., Matsen, I., et al · 2019
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Biophysicochemical motifs in T-cell receptor sequences distinguish repertoires from tumor-infiltrating lymphocyte and adjacent healthy tissue
Ostmeyer, J., Christley, S., Toby, I. T., and Cowell, L. G · 2019
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Pytorch: an imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
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Needles in haystacks: on classifying tiny objects in large images
Pawlowski, N., Bhooshan, S., Ballas, N., Ciompi, F., Glocker, B., and Drozdzal, M · 2019
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Interpretable deep learning in drug discovery
Preuer, K., Klambauer, G., Rippmann, F., Hochreiter, S., and Unterthiner, T · 2019
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High-throughput mapping of B cell receptor sequences to antigen specificity
Setliff, I., Shiakolas, A. R., Pilewski, K. A., Murji, A. A., Mapengo, R. E., Janowska, K., Richardson, S., Oosthuysen, C., Raju, N., Ronsard, L., et al · 2019
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DeepTCR: a deep learning framework for understanding T-cell receptor sequence signatures within complex T-cell repertoires
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Attention-based deep neural networks for detection of cancerous and precancerous esophagus tissue on histopathological slides
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Learning with sets in multiple instance regression applied to remote sensing
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Deep sequencing of B cell receptor repertoires from COVID-19 patients reveals strong convergent immune signatures
Galson, J. D., Schaetzle, S., Bashford-Rogers, R. J. M., Raybould, M. I. J., Kovaltsuk, A., Kilpatrick, G. J., Minter, R., Finch, D. K., Dias, J., James, L., Thomas, G., Lee, W.-Y. J., Betley, J., Cavlan, O., Leech, A., Deane, C. M., Seoane, J., Caldas, C., Pennington, D., Pfeffer, P., and Osbourn, J · 2020
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Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T cell memory formation after mild COVID-19 infection
Minervina, A. A., Komech, E. A., Titov, A., Koraichi, M. B., Rosati, E., Mamedov, I. Z., Franke, A., Efimov, G. A., Chudakov, D. M., Mora, T., Walczak, A. M., Lebedev, Y. B., and Pogorelyy, M. V · 2020
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Hopfield networks is all you need
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G. K., Greiff, V., Kreil, D., Kopp, M., Klambauer, G., Brandstetter, J., and Hochreiter, S · 2020
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CoV-AbDab: the coronavirus antibody database
Raybould, M. I. J., Kovaltsuk, A., Marks, C., and Deane, C. M · 2020
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Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs
Springer, I., Besser, H., Tickotsky-Moskovitz, N., Dvorkin, S., and Louzoun, Y · 2020
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immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking
Weber, C. R., Akbar, R., Yermanos, A., Pavlović, M., Snapkov, I., Sandve, G. K., Reddy, S. T., and Greiff, V · 2020
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PIRD: pan immune repertoire database
Zhang, W., Wang, L., Liu, K., Wei, X., Yang, K., Du, W., Wang, S., Guo, N., Ma, C., Luo, L., et al · 2020
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