These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

146 related articles for article (PubMed ID: 35347750)

  • 1. Deep learning for the dynamic prediction of multivariate longitudinal and survival data.
    Lin J; Luo S
    Stat Med; 2022 Jul; 41(15):2894-2907. PubMed ID: 35347750
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Dynamic Prediction in Clinical Survival Analysis Using Temporal Convolutional Networks.
    Jarrett D; Yoon J; van der Schaar M
    IEEE J Biomed Health Inform; 2020 Feb; 24(2):424-436. PubMed ID: 31331898
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease.
    Spasov S; Passamonti L; Duggento A; Liò P; Toschi N;
    Neuroimage; 2019 Apr; 189():276-287. PubMed ID: 30654174
    [TBL] [Abstract][Full Text] [Related]  

  • 4. VGG-TSwinformer: Transformer-based deep learning model for early Alzheimer's disease prediction.
    Hu Z; Wang Z; Jin Y; Hou W
    Comput Methods Programs Biomed; 2023 Feb; 229():107291. PubMed ID: 36516516
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker.
    Pickett KL; Suresh K; Campbell KR; Davis S; Juarez-Colunga E
    BMC Med Res Methodol; 2021 Oct; 21(1):216. PubMed ID: 34657597
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Dynamic prediction of Alzheimer's disease progression using features of multiple longitudinal outcomes and time-to-event data.
    Li K; Luo S
    Stat Med; 2019 Oct; 38(24):4804-4818. PubMed ID: 31386218
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Deep recurrent model for individualized prediction of Alzheimer's disease progression.
    Jung W; Jun E; Suk HI;
    Neuroimage; 2021 Aug; 237():118143. PubMed ID: 33991694
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Multi-speed transformer network for neurodegenerative disease assessment and activity recognition.
    Cheriet M; Dentamaro V; Hamdan M; Impedovo D; Pirlo G
    Comput Methods Programs Biomed; 2023 Mar; 230():107344. PubMed ID: 36706617
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework.
    Bussy S; Veil R; Looten V; Burgun A; Gaïffas S; Guilloux A; Ranque B; Jannot AS
    BMC Med Res Methodol; 2019 Mar; 19(1):50. PubMed ID: 30841867
    [TBL] [Abstract][Full Text] [Related]  

  • 10. phyLoSTM: a novel deep learning model on disease prediction from longitudinal microbiome data.
    Sharma D; Xu W
    Bioinformatics; 2021 Nov; 37(21):3707-3714. PubMed ID: 34213529
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer's disease.
    Li K; Luo S
    Stat Methods Med Res; 2019 Feb; 28(2):327-342. PubMed ID: 28750578
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.
    Shirwaikar RD; Acharya U D; Makkithaya K; M S; Srivastava S; Lewis U LES
    Artif Intell Med; 2019 Jul; 98():59-76. PubMed ID: 31521253
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks.
    Ramzan F; Khan MUG; Rehmat A; Iqbal S; Saba T; Rehman A; Mehmood Z
    J Med Syst; 2019 Dec; 44(2):37. PubMed ID: 31853655
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Genome-wide association study-based deep learning for survival prediction.
    Sun T; Wei Y; Chen W; Ding Y
    Stat Med; 2020 Dec; 39(30):4605-4620. PubMed ID: 32974946
    [TBL] [Abstract][Full Text] [Related]  

  • 15. TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture.
    Wang X; Zhang Z; Zhang C; Meng X; Shi X; Qu P
    Int J Mol Sci; 2022 Apr; 23(8):. PubMed ID: 35457080
    [TBL] [Abstract][Full Text] [Related]  

  • 16. [The current applicating state of neural network-based electroencephalogram diagnosis of Alzheimer's disease].
    Liu Y; Li Z; Wei Z; Xu Y; Xie P; Wang Y; Liu Q; Li X
    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2022 Dec; 39(6):1233-1239. PubMed ID: 36575093
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Functional survival forests for multivariate longitudinal outcomes: Dynamic prediction of Alzheimer's disease progression.
    Lin J; Li K; Luo S
    Stat Methods Med Res; 2021 Jan; 30(1):99-111. PubMed ID: 32726189
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis With Competing Risks Based on Longitudinal Data.
    Lee C; Yoon J; Schaar MV
    IEEE Trans Biomed Eng; 2020 Jan; 67(1):122-133. PubMed ID: 30951460
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Alzheimer's disease detection using depthwise separable convolutional neural networks.
    Liu J; Li M; Luo Y; Yang S; Li W; Bi Y
    Comput Methods Programs Biomed; 2021 May; 203():106032. PubMed ID: 33713959
    [TBL] [Abstract][Full Text] [Related]  

  • 20. RNN-based longitudinal analysis for diagnosis of Alzheimer's disease.
    Cui R; Liu M;
    Comput Med Imaging Graph; 2019 Apr; 73():1-10. PubMed ID: 30763637
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.