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Rudi Agius

Showing results (1-10 of 17) with videos related to

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Leukemia & Lymphoma|October 6, 2021
Artificial intelligence models in chronic lymphocytic leukemia - recommendations toward state-of-the-artRudi Agius, Mehdi Parviz, Carsten Utoft Niemann
Bioinformatics (Oxford, England)|September 10, 2011
Protein-protein binding affinity prediction on a diverse set of structuresIain H Moal, Rudi Agius, Paul A Bates
Blood Advances|April 25, 2022
Prediction of clinical outcome in CLL based on recurrent gene mutations, CLL-IPI variables, and (para)clinical dataMehdi Parviz, Christian Brieghel, Rudi Agius, et al.
Briefings in Functional Genomics|July 20, 2012
Understanding cancer mechanisms through network dynamicsTammy M K Cheng, Sakshi Gulati, Rudi Agius, et al.
Hemasphere|April 16, 2026
Machine learning enhances risk stratification and treatment failure prediction in diffuse large B-cell lymphomaMikkel Werling, Alexander D Fuglkjær, Peter Brown, et al.
Plos Computational Biology|September 17, 2013
Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organizationRudi Agius, Mieczyslaw Torchala, Iain H Moal, et al.
Leukemia & Lymphoma|January 5, 2024
Identifying CLL patients at high risk of atrial fibrillation on treatment using machine learningMehdi Parviz, Rudi Agius, Emelie Curovic Rotbain, et al.
Proteins|August 1, 2013
A Markov-chain model description of binding funnels to enhance the ranking of docked solutionsMieczyslaw Torchala, Iain H Moal, Raphael A G Chaleil, et al.
Acta Oncologica (Stockholm, Sweden)|February 19, 2026
Post-treatment infection prediction in CLL using domain adaptation of lymphoma electronic health recordsMehdi Parviz, Christian Brieghel, Mikkel Werling, et al.
Scientific Reports|August 16, 2022
Personalized survival probabilities for SARS-CoV-2 positive patients by explainable machine learningAdrian G Zucco, Rudi Agius, Rebecka Svanberg, et al.
Pageof 2

Showing results (1-10 of 17) with videos related to

Sort By:
Pageof 2
Leukemia & Lymphoma|October 6, 2021
Artificial intelligence models in chronic lymphocytic leukemia - recommendations toward state-of-the-artRudi Agius, Mehdi Parviz, Carsten Utoft Niemann
Bioinformatics (Oxford, England)|September 10, 2011
Protein-protein binding affinity prediction on a diverse set of structuresIain H Moal, Rudi Agius, Paul A Bates
Blood Advances|April 25, 2022
Prediction of clinical outcome in CLL based on recurrent gene mutations, CLL-IPI variables, and (para)clinical dataMehdi Parviz, Christian Brieghel, Rudi Agius, et al.
Briefings in Functional Genomics|July 20, 2012
Understanding cancer mechanisms through network dynamicsTammy M K Cheng, Sakshi Gulati, Rudi Agius, et al.
Hemasphere|April 16, 2026
Machine learning enhances risk stratification and treatment failure prediction in diffuse large B-cell lymphomaMikkel Werling, Alexander D Fuglkjær, Peter Brown, et al.
Plos Computational Biology|September 17, 2013
Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organizationRudi Agius, Mieczyslaw Torchala, Iain H Moal, et al.
Leukemia & Lymphoma|January 5, 2024
Identifying CLL patients at high risk of atrial fibrillation on treatment using machine learningMehdi Parviz, Rudi Agius, Emelie Curovic Rotbain, et al.
Proteins|August 1, 2013
A Markov-chain model description of binding funnels to enhance the ranking of docked solutionsMieczyslaw Torchala, Iain H Moal, Raphael A G Chaleil, et al.
Acta Oncologica (Stockholm, Sweden)|February 19, 2026
Post-treatment infection prediction in CLL using domain adaptation of lymphoma electronic health recordsMehdi Parviz, Christian Brieghel, Mikkel Werling, et al.
Scientific Reports|August 16, 2022
Personalized survival probabilities for SARS-CoV-2 positive patients by explainable machine learningAdrian G Zucco, Rudi Agius, Rebecka Svanberg, et al.
Pageof 2