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1,395 results on '"Harrison, David J."'

1. SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers

Author

Myles, Craig, Um, In Hwa, Marshall, Craig, Harris-Birtill, David, and Harrison, David J.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

$\textbf{Background}$: Cancer remains one of the leading causes of morbidity and mortality worldwide. Comprehensive datasets that combine histopathological images with genetic and survival data across various tumour sites are essential for advancing computational pathology and personalised medicine. $\textbf{Results}$: We present SurGen, a dataset comprising 1,020 H&E-stained whole slide images (WSIs) from 843 colorectal cancer cases. The dataset includes detailed annotations for key genetic mutations (KRAS, NRAS, BRAF) and mismatch repair status, as well as survival data for 426 cases. To demonstrate SurGen's practical utility, we conducted a proof-of-concept machine learning experiment predicting mismatch repair status from the WSIs, achieving a test AUROC of 0.8316. These preliminary results underscore the dataset's potential to facilitate research in biomarker discovery, prognostic modelling, and advanced machine learning applications in colorectal cancer. $\textbf{Conclusions}$: SurGen offers a valuable resource for the scientific community, enabling studies that require high-quality WSIs linked with comprehensive clinical and genetic information on colorectal cancer. Our initial findings affirm the dataset's capacity to advance diagnostic precision and foster the development of personalised treatment strategies in colorectal oncology. Data available online at https://doi.org/10.6019/S-BIAD1285., Comment: To download the dataset, see https://doi.org/10.6019/S-BIAD1285. See https://github.com/CraigMyles/SurGen-Dataset for GitHub repository and additional info

Published
2025

4. Benchmarking Pathology Feature Extractors for Whole Slide Image Classification

Author

Wölflein, Georg, Ferber, Dyke, Meneghetti, Asier R., Nahhas, Omar S. M. El, Truhn, Daniel, Carrero, Zunamys I., Harrison, David J., Arandjelović, Ognjen, and Kather, Jakob Nikolas

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Weakly supervised whole slide image classification is a key task in computational pathology, which involves predicting a slide-level label from a set of image patches constituting the slide. Constructing models to solve this task involves multiple design choices, often made without robust empirical or conclusive theoretical justification. To address this, we conduct a comprehensive benchmarking of feature extractors to answer three critical questions: 1) Is stain normalisation still a necessary preprocessing step? 2) Which feature extractors are best for downstream slide-level classification? 3) How does magnification affect downstream performance? Our study constitutes the most comprehensive evaluation of publicly available pathology feature extractors to date, involving more than 10,000 training runs across 14 feature extractors, 9 tasks, 5 datasets, 3 downstream architectures, 2 levels of magnification, and various preprocessing setups. Our findings challenge existing assumptions: 1) We observe empirically, and by analysing the latent space, that skipping stain normalisation and image augmentations does not degrade performance, while significantly reducing memory and computational demands. 2) We develop a novel evaluation metric to compare relative downstream performance, and show that the choice of feature extractor is the most consequential factor for downstream performance. 3) We find that lower-magnification slides are sufficient for accurate slide-level classification. Contrary to previous patch-level benchmarking studies, our approach emphasises clinical relevance by focusing on slide-level biomarker prediction tasks in a weakly supervised setting with external validation cohorts. Our findings stand to streamline digital pathology workflows by minimising preprocessing needs and informing the selection of feature extractors., Comment: For the conference version see: arXiv:2311.11772v4. For the longer journal version with additional experiments see arXiv:2311.11772v5

Published
2023

6. Deep Multiple Instance Learning with Distance-Aware Self-Attention

Author

Wölflein, Georg, Magister, Lucie Charlotte, Liò, Pietro, Harrison, David J., and Arandjelović, Ognjen

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Traditional supervised learning tasks require a label for every instance in the training set, but in many real-world applications, labels are only available for collections (bags) of instances. This problem setting, known as multiple instance learning (MIL), is particularly relevant in the medical domain, where high-resolution images are split into smaller patches, but labels apply to the image as a whole. Recent MIL models are able to capture correspondences between patches by employing self-attention, allowing them to weigh each patch differently based on all other patches in the bag. However, these approaches still do not consider the relative spatial relationships between patches within the larger image, which is especially important in computational pathology. To this end, we introduce a novel MIL model with distance-aware self-attention (DAS-MIL), which explicitly takes into account relative spatial information when modelling the interactions between patches. Unlike existing relative position representations for self-attention which are discrete, our approach introduces continuous distance-dependent terms into the computation of the attention weights, and is the first to apply relative position representations in the context of MIL. We evaluate our model on a custom MNIST-based MIL dataset that requires the consideration of relative spatial information, as well as on CAMELYON16, a publicly available cancer metastasis detection dataset, where we achieve a test AUROC score of 0.91. On both datasets, our model outperforms existing MIL approaches that employ absolute positional encodings, as well as existing relative position representation schemes applied to MIL. Our code is available at https://anonymous.4open.science/r/das-mil.

Published
2023

7. HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks

Author

Wölflein, Georg, Um, In Hwa, Harrison, David J, and Arandjelović, Ognjen

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

The presence and density of specific types of immune cells are important to understand a patient's immune response to cancer. However, immunofluorescence staining required to identify T cell subtypes is expensive, time-consuming, and rarely performed in clinical settings. We present a framework to virtually stain Hoechst images (which are cheap and widespread) with both CD3 and CD8 to identify T cell subtypes in clear cell renal cell carcinoma using generative adversarial networks. Our proposed method jointly learns both staining tasks, incentivising the network to incorporate mutually beneficial information from each task. We devise a novel metric to quantify the virtual staining quality, and use it to evaluate our method., Comment: Accepted at IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023

Published
2022

8. Leveraging Foundation Models for Enhanced Detection of Colorectal Cancer Biomarkers in Small Datasets

Author

Myles, Craig, Um, In Hwa, Harrison, David J., Harris-Birtill, David, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yap, Moi Hoon, editor, Kendrick, Connah, editor, Behera, Ardhendu, editor, Cootes, Timothy, editor, and Zwiggelaar, Reyer, editor

Published
2024
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9. Adapting the design of the ongoing RAMPART trial in response to external evidence: An example for trials which take many years to run and report

Author

Meade, Angela, Frangou, Elena, Choodari-Oskooei, Babak, Larkin, James, Powles, Tom, Stewart, Grant D., Albiges, Laurence, Bex, Axel, Choueiri, Toni K., Davis, Ian D., Eisen, Tim, Fielding, Alison, Gedye, Craig, Harrison, David J., Kaplan, Rick, Mulhere, Salena, Nathan, Paul, Patel, Grisma, Patel, Jay, Plant, Hannah, Ritchie, Alastair, Rush, Hannah, Shakeshaft, Clare, Stockler, Martin R., Suarez, Cristina, Thompson, Jemima, Thorogood, Nat, Venugopal, Balaji, and Parmar, Mahesh K.B.

Published
2024
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11. Hoechst Is All You Need: Lymphocyte Classification with Deep Learning

Author

Cooper, Jessica, Um, In Hwa, Arandjelović, Ognjen, and Harrison, David J

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Multiplex immunofluorescence and immunohistochemistry benefit patients by allowing cancer pathologists to identify several proteins expressed on the surface of cells, enabling cell classification, better understanding of the tumour micro-environment, more accurate diagnoses, prognoses, and tailored immunotherapy based on the immune status of individual patients. However, they are expensive and time consuming processes which require complex staining and imaging techniques by expert technicians. Hoechst staining is much cheaper and easier to perform, but is not typically used in this case as it binds to DNA rather than to the proteins targeted by immunofluorescent techniques, and it was not previously thought possible to differentiate cells expressing these proteins based only on DNA morphology. In this work we show otherwise, training a deep convolutional neural network to identify cells expressing three proteins (T lymphocyte markers CD3 and CD8, and the B lymphocyte marker CD20) with greater than 90% precision and recall, from Hoechst 33342 stained tissue only. Our model learns previously unknown morphological features associated with expression of these proteins which can be used to accurately differentiate lymphocyte subtypes for use in key prognostic metrics such as assessment of immune cell infiltration,and thereby predict and improve patient outcomes without the need for costly multiplex immunofluorescence., Comment: 15 pages, 4 figures

Published
2021

13. Believe The HiPe: Hierarchical Perturbation for Fast, Robust, and Model-Agnostic Saliency Mapping

Author

Cooper, Jessica, Arandjelović, Ognjen, and Harrison, David J

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Understanding the predictions made by Artificial Intelligence (AI) systems is becoming more and more important as deep learning models are used for increasingly complex and high-stakes tasks. Saliency mapping -- a popular visual attribution method -- is one important tool for this, but existing formulations are limited by either computational cost or architectural constraints. We therefore propose Hierarchical Perturbation, a very fast and completely model-agnostic method for interpreting model predictions with robust saliency maps. Using standard benchmarks and datasets, we show that our saliency maps are of competitive or superior quality to those generated by existing model-agnostic methods -- and are over 20 times faster to compute., Comment: github.com/jessicamarycooper/Hierarchical-Perturbation

Published
2021

14. What it is like to be a bit: an integrated information decomposition account of emergent mental phenomena

Author

Luppi, Andrea I, Mediano, Pedro AM, Rosas, Fernando E, Harrison, David J, Carhart-Harris, Robin L, Bor, Daniel, and Stamatakis, Emmanuel A

Subjects
Neurosciences, consciousness, information decomposition, integrated information theory, phenomenology, selfhood, Philosophy
Abstract

A central question in neuroscience concerns the relationship between consciousness and its physical substrate. Here, we argue that a richer characterization of consciousness can be obtained by viewing it as constituted of distinct information-theoretic elements. In other words, we propose a shift from quantification of consciousness-viewed as integrated information-to its decomposition. Through this approach, termed Integrated Information Decomposition (ΦID), we lay out a formal argument that whether the consciousness of a given system is an emergent phenomenon depends on its information-theoretic composition-providing a principled answer to the long-standing dispute on the relationship between consciousness and emergence. Furthermore, we show that two organisms may attain the same amount of integrated information, yet differ in their information-theoretic composition. Building on ΦID's revised understanding of integrated information, termed ΦR, we also introduce the notion of ΦR-ing ratio to quantify how efficiently an entity uses information for conscious processing. A combination of ΦR and ΦR-ing ratio may provide an important way to compare the neural basis of different aspects of consciousness. Decomposition of consciousness enables us to identify qualitatively different 'modes of consciousness', establishing a common space for mapping the phenomenology of different conscious states. We outline both theoretical and empirical avenues to carry out such mapping between phenomenology and information-theoretic modes, starting from a central feature of everyday consciousness: selfhood. Overall, ΦID yields rich new ways to explore the relationship between information, consciousness, and its emergence from neural dynamics.

Published
2021

18. The renal lineage factor PAX8 controls oncogenic signalling in kidney cancer

Author

Patel, Saroor A., Hirosue, Shoko, Rodrigues, Paulo, Vojtasova, Erika, Richardson, Emma K., Ge, Jianfeng, Syafruddin, Saiful E., Speed, Alyson, Papachristou, Evangelia K., Baker, David, Clarke, David, Purvis, Stephenie, Wesolowski, Ludovic, Dyas, Anna, Castillon, Leticia, Caraffini, Veronica, Bihary, Dóra, Yong, Cissy, Harrison, David J., Stewart, Grant D., Machiela, Mitchell J., Purdue, Mark P., Chanock, Stephen J., Warren, Anne Y., Samarajiwa, Shamith A., Carroll, Jason S., and Vanharanta, Sakari

Published
2022
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19. Unusual structures inherent in point pattern data predict colon cancer patient survival

Author

Jones-Todd, Charlotte M., Caie, Peter, Illian, Janine, Stevenson, Ben C., Savage, Anne, Harrison, David J., and Bown, James L.

Subjects
Statistics - Methodology
Abstract

Cancer patient diagnosis and prognosis is informed by assessment of morphological properties observed in patient tissue. Pathologists normally carry out this assessment, yet advances in computational image analysis provide opportunities for quantitative assessment of tissue. A key aspect of that quantitative assessment is the development of algorithms able to link image data to patient survival. Here, we develop a point process methodology able to describe patterns in cell distribution within cancerous tissue samples. In particular, we consider the Palm intensities of two Neyman Scott point processes, and a void process developed herein to reflect the spatial patterning of the cells. An approximate-likelihood technique is taken in order to fit point process models to patient data and the predictive performance of each model is determined. We demonstrate that based solely on the spatial arrangement of cells we are able to predict patient survival., Comment: 20 pages including Appendix

Published
2017

23. RAMPART: A model for a regulatory-ready academic-led phase III trial in the adjuvant renal cell carcinoma setting

Author

Meade, Angela, Oza, Bhavna, Frangou, Eleni, Smith, Ben, Bryant, Hanna, Kaplan, Rick, Choodari-Oskooei, Babak, Powles, Tom, Stewart, Grant D., Albiges, Laurence, Bex, Axel, Choueiri, Toni K., Davis, Ian D., Eisen, Tim, Fielding, Alison, Harrison, David J., McWhirter, Anita, Mulhere, Salena, Nathan, Paul, Rini, Brian, Ritchie, Alastair, Scovell, Sarah, Shakeshaft, Clare, Stockler, Martin R., Thorogood, Nat, Larkin, James, and Parmar, Mahesh K.B.

Published
2021
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24. Assessing Experiences with Online Educational Videos: Converting Multiple Constructed Responses to Quantifiable Data

Author

Harrison, David J.

Abstract

Online educational videos disseminated content for a university pre-service teacher internship program. Placed within an online course management system, the videos were accessed by 202 interns located in several Western U.S. states. To ascertain the interns' subjective experiences of the online course and videos to assist in the evaluation of the course, the researcher analyzed qualitative survey data in the form of constructed response items using a new qualitative-to-quantitative protocol. Based on phenomenological and grounded theory methods, this protocol was designed to handle the large amount of subjective constructed responses, allowing the inductive understanding of the overall experiences of a common phenomenon. The responses provided critical information that is useful for instructional designers, online educators, and educational video producers. The data suggest: 1) Universities must carefully consider video hosting options to ensure access. 2) Online videos should be carefully planned to create high quality, concise videos of less than ten minutes in length, yet contain enough educational content to reduce the overall number of required videos. 3) Students appreciate the flexibility online course delivery offered in terms of scheduling and eliminating the need to come to campus to attend seminars. 4) Self-paced online courses require progress indicators to alleviate confusion.

Published
2015

35. Somatic cancer genetics in the UK: real-world data from phase I of the Cancer Research UK Stratified Medicine Programme

Author

Lindsay, Colin R., Shaw, Emily C., Blackhall, Fiona, Blyth, Kevin G., Brenton, James D., Chaturvedi, Anshuman, Clarke, Noel, Dick, Craig, Evans, Thomas R.J., Hall, Geoff, Hanby, Andrew M., Harrison, David J., Johnston, Stephen R.D., Mason, Malcolm D., Morton, Dion, Newton-Bishop, Julia, Nicholson, Andrew G., Oien, Karin A., Popat, Sanjay, Rassl, Doris, Sharpe, Rowena, Taniere, Phillipe, Walker, Ian, Wallace, William A., West, Nicholas P., Butler, Rachel, Gonzalez de Castro, David, Griffiths, Mike, Johnson, Peter W.M., Rehal, Pauline, Butler, Samantha, Smith, Matthew, Doak, Rachel, Tanska, Anna, Halford, Graham, James, Lisa, Kotara, Chris, Masson, Gareth, Clokie, Sam, Bell, Jennie, Macdonald, Fiona, De Castro, David Gonzalez, Thompson, Lisa, Mair, Debbie, Lillis, Suzanne, Wren, Dorte, Hollifield, Robert, Dover, Keeda, Maurya, Manisha, Brooks, Damian, Gomez, Belen, Grady, Lisa, Jones, Thomas, Hooper, Chantal, Webster, Daphne, Travis, Jolyon, Ogwuru, Stephanie, Gazdova, Jana, Collins, Denise, Chapman, Elaine, Leavey, Lisa, Proszek, Paula, Hulkki, Sanna, Collins, V.Peter, Ibrahim, Ash, Brown, Kat, Burge, Jo, Burnett, Karen, Devonshire, Ginny, Moseley, Ellen, Haynes, Bev, Hodgkin, Charlotte, Jimenez-linan, Merche, Jones, Linda, Kenyon, Gilly, Mahler-araujo, Betania, Payne, Karen, Piper, Jo, Richardson, Sue, Rytina, Ed, Warren, Anne, Coker, Liz, Godsall, Gemma, Arends, Mark, O’Neill, Amanda, Rintoul, Katy, Goymer, Donna, Taylor, Julie, Matthews, Claire, Bhayani, Harshil, Osalador, Tina, Niwaz, Zakiya, Higgins, Anna, Bamsey, Olivia, Salter, Janine, Renouf, Louise, Noel-Storr, Glenn, Roberts, Helen, Gierejko, Kasia, Knapman, Paola, Wotherspoon, Andrew, Stamp, Gordon, Attygailye, Ayoma, Hazell, Steve, Osin, Peter, Nerurkar, Ash, Francis, Steven, Runde, Marion, Arch, Jo, Chitnis, Xavier, Siu, Bernard, Townsend, Debra, Hennelly, Laura, Taylor, Natalie, Johnson, Bernadette, Banerjee, Susie, Pyle, Lynda, Hamill, Monica, Gyertson, Jenny, George, Angela, Patel, Krishna, Pearce, Karla, Edmonds, Kim, Sarker, Sarah, Eeles, Rosalind, Bancroft, Liz, Thomas, Sarah, Kano, Yukie, Rowland, Lisa, Brooks, Karen, O’brien, Mary, Bhosle, Jaishree, Priest, Kathy, Ayite, Bee, Severn, Jo, Beedham, Helen, Lucas, Nicky, Tye, Kim, Lorentzos, Alison, Webb, Janine, Kerr, Sarah, Corestav, Lisa, Bottero, Diego, Jell, Laura, Thomas, Janet, Marriott, Cheryl, Rajah, Neil, Cole, Andy, Ly, Dieu, Taniere, Philippe, O’sullivan, Brendan, Swift, Clare, Hughes, Frances, Neil, Desley, Hanby, Andrew, Banks, Roz, Ajayi, Dolapo, Barclay, Alison, Bishop, Julia Newton, Beirne, Debbie, Bernard, Andrew, Berry, Maxine, Bentley, Jo, Bishop, Tim, Chambers, Amy, Clarke, Jude, Crossley, Anne, Gahir, Narinder, Gibson, Debbie, Good, Rona, Grosios, Konstantina, Harnden, Pat, Hasler, Kate, Hindmarch, Damien, Jackson, Sharon, Johnstone, Colin, Jones, Anne-marie, Lambert, Gil, Lane, Sally, Mcnicholas, Nicola, Millican-Slater, Rebecca, Moriaty, Cath, Newsham, Alex, O’connell, Kara, Ripley, Lisa, Sebag-Montefiore, David, Simpson, Mary, Speirs, Val, Sugden, Joh, Tate, Lauren, Tidswell, Emma, Twelves, Chris, Walker, Christy, Waterhouse, Barry, Waugh, Martin, White, Louise, Wright, Elizabeth, Rogan, Jane, Ashton, Garry, Abbey, Caron, Greenhalgh, Michelle, Nonaka, Daisuke, Shing, Elwyn, Gibbard, Carmen, Burton, Georgina, Fawkes, Naomi, Marsden, Angela, Waddington, Rachael, Harrison, Phil, Moghadam, Shahrzad, Murray, Kate, Brown, Sarah, Mitchinson, Christy, Booton, Richard, Shah, Rajesh, Harrison, David, Oniscu, Anca, Wallace, William, Rae, Frances, Marshall, Craig, Mcleod, Linda, Charles, Morag, Sutherland, Sarah Jane, Dawson, Carol, Mitchell, Paul, Maclellan, Alex, Muir, Sandra, Johnstone, Lynne, O’connor, John, Johnstone, Shirley, Mcpherson, Jim, Hair, Jane, Pignatelli, Massimo, Armstrong, Roma, Oien, Karin, Evans, Jeff, Burgoyne, Margaret, Blessing, Karen, Duthie, Fraser, Moyes, Colin, Mallon, Elizabeth, Millan, David, Roberts, Fiona, Seywright, Morag, Fraser, Siobhan, Ford, Ian, Kean, Sharon, Flood, Marion, Grant, David, Mcdonald, Claire, Moffat, Tom, Mclelland, Hugh, Kyle, Alistair, Cameron, Graham, Wright, Martin, Kenny, Stephen, Mcauslan, Karen, Jones, Andrew, Fitzsimons, Ted, Graham, Fiona, Bell, Alexandra, Duffy, Phil, Fisher, Alec, Smith, Alexis, Shannon, Elaine, Woods, Bryan, Hutchison, Colin, Booth, Angela, Duffy, Lyndsay, Mcculloch, Gillian, Sadiq, Hudda, Deakin, Susan, Haywood, Steven, Mason, Malcolm, Chester, John, Parry-jones, Alison, Macarthur, Abby, Williams, Suzanne, Griffiths, David, Morgan, Fiona, and Bailey, Hazel

Published
2018
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38. Assessing the Effectiveness of a Hybrid-Flipped Model of Learning on Fluid Mechanics Instruction: Overall Course Performance, Homework, and Far- and Near-Transfer of Learning

Author

Harrison, David J., Saito, Laurel, Markee, Nancy, and Herzog, Serge

Abstract

To examine the impact of a hybrid-flipped model utilising active learning techniques, the researchers inverted one section of an undergraduate fluid mechanics course, reduced seat time, and engaged in active learning sessions in the classroom. We compared this model to the traditional section on four performance measures. We employed a propensity score method entailing a two-stage regression analysis that considered eight covariates to address the potential bias of treatment selection. First, we estimated the probability score based on the eight covariates, and second, we used the inverse of the probability score as a regression weight on the performance of learners who did not select into the hybrid course. Results suggest that enrolment in the hybrid-flipped section had a marginally significant negative impact on the total course score and a significant negative impact on homework performance, possibly because of poor video usage by the hybrid-flipped learners. Suggested considerations are also discussed.

Published
2017
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47. Microenvironment of metastatic site reveals key predictors of PD-1 blockade response in renal cell carcinoma

Author

Jeanneret, Florian, primary, Bazelle, Pauline, additional, Schoch, Sarah, additional, Pillet, Catherine, additional, Um, In Hwa, additional, Bouzit, Assilah, additional, Evrard, Bertrand, additional, Seffar, Evan, additional, Chalmel, Frederic, additional, Alfaro, Javier A, additional, Pesquita, Catia, additional, Zanzotto, Fabio Massimo, additional, Stares, Mark, additional, Symeonides, Stefan N, additional, Laird, Alexander, additional, Long, Jean-Alexandre, additional, Descotes, Jean Luc, additional, Pflieger, Delphine, additional, Harrison, David J, additional, Filhol, Odile, additional, Axelson, Hakan, additional, and Battail, Christophe, additional

Published
2023
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48. Supplementary Table 3 from The Immunopeptidome from a Genomic Perspective: Establishing the Noncanonical Landscape of MHC Class I–Associated Peptides

Author

Bedran, Georges, primary, Gasser, Hans-Christof, primary, Weke, Kenneth, primary, Wang, Tongjie, primary, Bedran, Dominika, primary, Laird, Alexander, primary, Battail, Christophe, primary, Zanzotto, Fabio Massimo, primary, Pesquita, Catia, primary, Axelson, Håkan, primary, Rajan, Ajitha, primary, Harrison, David J., primary, Palkowski, Aleksander, primary, Pawlik, Maciej, primary, Parys, Maciej, primary, O'Neill, J. Robert, primary, Brennan, Paul M., primary, Symeonides, Stefan N., primary, Goodlett, David R., primary, Litchfield, Kevin, primary, Fahraeus, Robin, primary, Hupp, Ted R., primary, Kote, Sachin, primary, and Alfaro, Javier A., primary

Published
2023
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49. Figure 3 from The Immunopeptidome from a Genomic Perspective: Establishing the Noncanonical Landscape of MHC Class I–Associated Peptides

Author

Bedran, Georges, primary, Gasser, Hans-Christof, primary, Weke, Kenneth, primary, Wang, Tongjie, primary, Bedran, Dominika, primary, Laird, Alexander, primary, Battail, Christophe, primary, Zanzotto, Fabio Massimo, primary, Pesquita, Catia, primary, Axelson, Håkan, primary, Rajan, Ajitha, primary, Harrison, David J., primary, Palkowski, Aleksander, primary, Pawlik, Maciej, primary, Parys, Maciej, primary, O'Neill, J. Robert, primary, Brennan, Paul M., primary, Symeonides, Stefan N., primary, Goodlett, David R., primary, Litchfield, Kevin, primary, Fahraeus, Robin, primary, Hupp, Ted R., primary, Kote, Sachin, primary, and Alfaro, Javier A., primary

Published
2023
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50. Figure 4 from The Immunopeptidome from a Genomic Perspective: Establishing the Noncanonical Landscape of MHC Class I–Associated Peptides

Author

Bedran, Georges, primary, Gasser, Hans-Christof, primary, Weke, Kenneth, primary, Wang, Tongjie, primary, Bedran, Dominika, primary, Laird, Alexander, primary, Battail, Christophe, primary, Zanzotto, Fabio Massimo, primary, Pesquita, Catia, primary, Axelson, Håkan, primary, Rajan, Ajitha, primary, Harrison, David J., primary, Palkowski, Aleksander, primary, Pawlik, Maciej, primary, Parys, Maciej, primary, O'Neill, J. Robert, primary, Brennan, Paul M., primary, Symeonides, Stefan N., primary, Goodlett, David R., primary, Litchfield, Kevin, primary, Fahraeus, Robin, primary, Hupp, Ted R., primary, Kote, Sachin, primary, and Alfaro, Javier A., primary

Published
2023
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