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Year Number of Results
1954 2
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2002 2
2007 1
2008 2
2009 1
2013 1
2016 1
2017 3
2018 5
2019 8
2020 1
2021 6
2022 10
2023 9
2024 7

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55 results

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Page 1
Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes.
Racle J, Guillaume P, Schmidt J, Michaux J, Larabi A, Lau K, Perez MAS, Croce G, Genolet R, Coukos G, Zoete V, Pojer F, Bassani-Sternberg M, Harari A, Gfeller D. Racle J, et al. Immunity. 2023 Jun 13;56(6):1359-1375.e13. doi: 10.1016/j.immuni.2023.03.009. Epub 2023 Apr 5. Immunity. 2023. PMID: 37023751 Free article.
Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse-binding mode in HLA-DP ligands. We then developed a machine-learning framework to accurate …
Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-I …
Artificial intelligence in lung cancer: current applications and perspectives.
Chassagnon G, De Margerie-Mellon C, Vakalopoulou M, Marini R, Hoang-Thi TN, Revel MP, Soyer P. Chassagnon G, et al. Jpn J Radiol. 2023 Mar;41(3):235-244. doi: 10.1007/s11604-022-01359-x. Epub 2022 Nov 9. Jpn J Radiol. 2023. PMID: 36350524 Free PMC article. Review.
The more recent rise of deep learning and the availability of large annotated lung nodule datasets have allowed the development of new CADe tools with fewer false-positive results per examination. Classical machine learning and deep-learning methods were also used for pulm …
The more recent rise of deep learning and the availability of large annotated lung nodule datasets have allowed the development of new CADe …
Tumor-specific cytolytic CD4 T cells mediate immunity against human cancer.
Cachot A, Bilous M, Liu YC, Li X, Saillard M, Cenerenti M, Rockinger GA, Wyss T, Guillaume P, Schmidt J, Genolet R, Ercolano G, Protti MP, Reith W, Ioannidou K, de Leval L, Trapani JA, Coukos G, Harari A, Speiser DE, Mathis A, Gfeller D, Altug H, Romero P, Jandus C. Cachot A, et al. Sci Adv. 2021 Feb 26;7(9):eabe3348. doi: 10.1126/sciadv.abe3348. Print 2021 Feb. Sci Adv. 2021. PMID: 33637530 Free PMC article.
We performed an integrated phenotypic and functional characterization of these cells, down to the single-cell level, through a high-throughput nanobiochip consisting of massive arrays of picowells and machine learning. We demonstrated a direct, contact-, and granzyme-depen …
We performed an integrated phenotypic and functional characterization of these cells, down to the single-cell level, through a high-throughp …
Harnessing human microbiomes for disease prediction.
Liu Y, Fachrul M, Inouye M, Méric G. Liu Y, et al. Trends Microbiol. 2024 Jan 20:S0966-842X(23)00339-6. doi: 10.1016/j.tim.2023.12.004. Online ahead of print. Trends Microbiol. 2024. PMID: 38246848 Review.
Predicting disease-causing variant combinations.
Papadimitriou S, Gazzo A, Versbraegen N, Nachtegael C, Aerts J, Moreau Y, Van Dooren S, Nowé A, Smits G, Lenaerts T. Papadimitriou S, et al. Proc Natl Acad Sci U S A. 2019 Jun 11;116(24):11878-11887. doi: 10.1073/pnas.1815601116. Epub 2019 May 24. Proc Natl Acad Sci U S A. 2019. PMID: 31127050 Free PMC article.
We present here the Variant Combinations Pathogenicity Predictor (VarCoPP), a machine-learning approach that identifies pathogenic variant combinations in gene pairs (called digenic or bilocus variant combinations). ...
We present here the Variant Combinations Pathogenicity Predictor (VarCoPP), a machine-learning approach that identifies pathogenic va …
A machine learning approach to infant distress calls and maternal behaviour of wild chimpanzees.
Dezecache G, Zuberbühler K, Davila-Ross M, Dahl CD. Dezecache G, et al. Anim Cogn. 2021 May;24(3):443-455. doi: 10.1007/s10071-020-01437-5. Epub 2020 Oct 22. Anim Cogn. 2021. PMID: 33094407
Their presumed function is to recruit help, but there has been much debate on whether the nature of the disturbance can be inferred from the acoustics of distress calls. We used machine learning to analyse episodes of distress calls of wild infant chimpanzees. ...
Their presumed function is to recruit help, but there has been much debate on whether the nature of the disturbance can be inferred from the …
Machine learning for predicting psychotic relapse at 2 years in schizophrenia in the national FACE-SZ cohort.
Fond G, Bulzacka E, Boucekine M, Schürhoff F, Berna F, Godin O, Aouizerate B, Capdevielle D, Chereau I, D'Amato T, Dubertret C, Dubreucq J, Faget C, Leignier S, Lançon C, Mallet J, Misdrahi D, Passerieux C, Rey R, Schandrin A, Urbach M, Vidailhet P, Leboyer M; FACE-SZ (FondaMental Academic Centers of Expertise for Schizophrenia) group; Boyer L, Llorca PM. Fond G, et al. Prog Neuropsychopharmacol Biol Psychiatry. 2019 Jun 8;92:8-18. doi: 10.1016/j.pnpbp.2018.12.005. Epub 2018 Dec 12. Prog Neuropsychopharmacol Biol Psychiatry. 2019. PMID: 30552914
OBJECTIVES: To determine the predictors of psychotic relapse and follow-up withdrawal in a non-selected national sample of stabilized community-dwelling SZ subjects with a machine learning approach. METHODS: Participants were consecutively included in the network of the Fo …
OBJECTIVES: To determine the predictors of psychotic relapse and follow-up withdrawal in a non-selected national sample of stabilized commun …
55 results