| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A comparison of clustering models for inference of T cell receptor antigen specificity
|
Hudson, Dan
|
|
|
1-2 |
C |
p.
|
artikel
|
| 2 |
A computational and experimental approach to studying NFkB signaling in response to single, dual, and triple TLR signaling
|
Newman, Thalia
|
|
|
1-2 |
C |
p.
|
artikel
|
| 3 |
AIRR community curation and standardised representation for immunoglobulin and T cell receptor germline sets
|
Lees, William D.
|
|
|
1-2 |
C |
p.
|
artikel
|
| 4 |
A Nextflow pipeline for T-cell receptor repertoire reconstruction and analysis from RNA sequencing data
|
Rubio, Teresa
|
|
|
1-2 |
C |
p.
|
artikel
|
| 5 |
Association of pyroptosis and severeness of COVID-19 as revealed by integrated single-cell transcriptome data analysis
|
Xu, Qian
|
|
|
1-2 |
C |
p.
|
artikel
|
| 6 |
Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report
|
Meysman, Pieter
|
|
|
1-2 |
C |
p.
|
artikel
|
| 7 |
CelltrackR: An R package for fast and flexible analysis of immune cell migration data
|
Wortel, Inge M.N.
|
|
|
1-2 |
C |
p.
|
artikel
|
| 8 |
Computational modelling of immunological mechanisms: From statistical approaches to interpretable machine learning
|
Rodríguez Martínez, María
|
|
|
1-2 |
C |
p.
|
artikel
|
| 9 |
Corrigendum to “T-cell receptor binding prediction: A machine learning revolution” [ImmunoInformatics, Volume 15, September 2024, 100040]
|
Rodríguez Martínez, Prof. María
|
|
|
1-2 |
C |
p.
|
artikel
|
| 10 |
Data mining antibody sequences for database searching in bottom-up proteomics
|
Trinh, Xuan-Tung
|
|
|
1-2 |
C |
p.
|
artikel
|
| 11 |
Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review
|
Echle, Amelie
|
|
|
1-2 |
C |
p.
|
artikel
|
| 12 |
Do domain-specific protein language models outperform general models on immunology-related tasks?
|
Deutschmann, Nicolas
|
|
|
1-2 |
C |
p.
|
artikel
|
| 13 |
Epitopedia: identifying molecular mimicry between pathogens and known immune epitopes
|
Balbin, Christian A
|
|
|
1-2 |
C |
p.
|
artikel
|
| 14 |
Epitope identification of SARS-CoV-2 structural proteins using in silico approaches to obtain a conserved rational immunogenic peptide
|
Araújo, Leonardo Pereira de
|
|
|
1-2 |
C |
p.
|
artikel
|
| 15 |
Guiding a language-model based protein design method towards MHC Class-I immune-visibility targets in vaccines and therapeutics
|
Gasser, Hans-Christof
|
|
|
1-2 |
C |
p.
|
artikel
|
| 16 |
Immune checkpoint therapy modeling of PD-1/PD-L1 blockades reveals subtle difference in their response dynamics and potential synergy in combination
|
Kaveh, Kamran
|
|
|
1-2 |
C |
p.
|
artikel
|
| 17 |
ImmunoInformatics: at the crossroads between immunology and informatics, and beyond
|
Halama, Niels
|
|
|
1-2 |
C |
p.
|
artikel
|
| 18 |
Immunoinformatics: Pushing the boundaries of immunology research and medicine
|
Chatanaka, Miyo K.
|
|
|
1-2 |
C |
p.
|
artikel
|
| 19 |
Improving immunovirotherapies: the intersection of mathematical modelling and experiments
|
Engeland, Christine E.
|
|
|
1-2 |
C |
p.
|
artikel
|
| 20 |
In silico design and evaluation of a multi-epitope and multi-antigenic African swine fever vaccine
|
Buan, Ara Karizza G.
|
|
|
1-2 |
C |
p.
|
artikel
|
| 21 |
In silico modelling of CD8 T cell immune response links genetic regulation to population dynamics
|
Nguyen, Thi Nhu Thao
|
|
|
1-2 |
C |
p.
|
artikel
|
| 22 |
In silico single-cell metabolism analysis unravels a new transition stage of CD8 T cells 4 days post-infection
|
Arpin, Christophe
|
|
|
1-2 |
C |
p.
|
artikel
|
| 23 |
Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response
|
Zhang, Shuming
|
|
|
1-2 |
C |
p.
|
artikel
|
| 24 |
Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interaction predictions
|
Dens, Ceder
|
|
|
1-2 |
C |
p.
|
artikel
|
| 25 |
Lessons learned from the IMMREP23 TCR-epitope prediction challenge
|
Nielsen, Morten
|
|
|
1-2 |
C |
p.
|
artikel
|
| 26 |
Machine-learning-based analytics for risk forecasting of anaphylaxis during general anesthesia
|
Liu, Shuang
|
|
|
1-2 |
C |
p.
|
artikel
|
| 27 |
Modeling interaction of Glioma cells and CAR T-cells considering multiple CAR T-cells bindings
|
Li, Runpeng
|
|
|
1-2 |
C |
p.
|
artikel
|
| 28 |
Modelling rheumatoid arthritis: A hybrid modelling framework to describe pannus formation in a small joint
|
Macfarlane, Fiona R.
|
|
|
1-2 |
C |
p.
|
artikel
|
| 29 |
Molecular mimicry impact of the COVID-19 pandemic: Sequence homology between SARS-CoV-2 and autoimmune diseases epitopes
|
Maldonado-Catala, Pablo
|
|
|
1-2 |
C |
p.
|
artikel
|
| 30 |
Multicohort analysis identifies conserved transcriptional interactions between humans and Plasmodium falciparum
|
Silva, Bárbara Fernandes
|
|
|
1-2 |
C |
p.
|
artikel
|
| 31 |
Navigating the immunosuppressive brain tumor microenvironment using spatial biology
|
Widodo, Samuel S.
|
|
|
1-2 |
C |
p.
|
artikel
|
| 32 |
NetMHCphosPan - Pan-specific prediction of MHC class I antigen presentation of phosphorylated ligands
|
Refsgaard, Carina Thusgaard
|
|
|
1-2 |
C |
p.
|
artikel
|
| 33 |
Recent advances in T-cell receptor repertoire analysis: Bridging the gap with multimodal single-cell RNA sequencing
|
Valkiers, Sebastiaan
|
|
|
1-2 |
C |
p.
|
artikel
|
| 34 |
Recent computational image workflows advance the spatio-phenotypic analysis of the tumor immune microenvironment
|
Valous, Nektarios A.
|
|
|
1-2 |
C |
p.
|
artikel
|
| 35 |
SARS-CoV-2-identical protein regions found in mammalian coronaviruses have immunogenic potential and can imply cross-protection
|
Lopes, Luciano Rodrigo
|
|
|
1-2 |
C |
p.
|
artikel
|
| 36 |
SARS-CoV-2 Omicron (BA.1 and BA.2) specific novel CD8+ and CD4+ T cell epitopes targeting spike protein
|
Parn, Simone
|
|
|
1-2 |
C |
p.
|
artikel
|
| 37 |
Scifer: An R/Bioconductor package for large-scale integration of Sanger sequencing and flow cytometry data of index-sorted single cells
|
Arcoverde Cerveira, Rodrigo
|
|
|
1-2 |
C |
p.
|
artikel
|
| 38 |
Structural pre-training improves physical accuracy of antibody structure prediction using deep learning.
|
Kończak, Jarosław
|
|
|
1-2 |
C |
p.
|
artikel
|
| 39 |
T-cell receptor binding prediction: A machine learning revolution
|
Weber, Anna
|
|
|
1-2 |
C |
p.
|
artikel
|
| 40 |
The journey towards complete and accurate prediction of HLA antigen presentation
|
Nilsson, Jonas Birkelund
|
|
|
1-2 |
C |
p.
|
artikel
|
| 41 |
The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions
|
Gazeau, Sonia
|
|
|
1-2 |
C |
p.
|
artikel
|
| 42 |
To what extent does MHC binding translate to immunogenicity in humans?
|
Lee, Chloe H.
|
|
|
1-2 |
C |
p.
|
artikel
|
| 43 |
Transfer learning improves pMHC kinetic stability and immunogenicity predictions
|
Fasoulis, Romanos
|
|
|
1-2 |
C |
p.
|
artikel
|
| 44 |
Using in silico models to predict lymphocyte activation and development in a data rich era
|
Khakoo, Salim I
|
|
|
1-2 |
C |
p.
|
artikel
|
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