167 related articles for article (PubMed ID: 38702624)
41. Bayesian variable selection with graphical structure learning: Applications in integrative genomics.
Kundu S; Cheng Y; Shin M; Manyam G; Mallick BK; Baladandayuthapani V
PLoS One; 2018; 13(7):e0195070. PubMed ID: 30059495
[TBL] [Abstract][Full Text] [Related]
42. Tutorial on survival modeling with applications to omics data.
Zhao Z; Zobolas J; Zucknick M; Aittokallio T
Bioinformatics; 2024 Mar; 40(3):. PubMed ID: 38445722
[TBL] [Abstract][Full Text] [Related]
43. Extend mixed models to multilayer neural networks for genomic prediction including intermediate omics data.
Zhao T; Zeng J; Cheng H
Genetics; 2022 May; 221(1):. PubMed ID: 35212766
[TBL] [Abstract][Full Text] [Related]
44. Identification of influential observations in high-dimensional survival data through robust penalized Cox regression based on trimming.
Sun H; Gao Q; Zhu G; Han C; Yan H; Wang T
Math Biosci Eng; 2023 Jan; 20(3):5352-5378. PubMed ID: 36896549
[TBL] [Abstract][Full Text] [Related]
45. Framework for personalized prediction of treatment response in relapsing remitting multiple sclerosis.
Stühler E; Braune S; Lionetto F; Heer Y; Jules E; Westermann C; Bergmann A; van Hövell P;
BMC Med Res Methodol; 2020 Feb; 20(1):24. PubMed ID: 32028898
[TBL] [Abstract][Full Text] [Related]
46. Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data.
Zhao X; Cheung LW
BMC Bioinformatics; 2007 Feb; 8():67. PubMed ID: 17328811
[TBL] [Abstract][Full Text] [Related]
47. A supervised Bayesian factor model for the identification of multi-omics signatures.
Gygi JP; Konstorum A; Pawar S; Aron E; Kleinstein SH; Guan L
Bioinformatics; 2024 May; 40(5):. PubMed ID: 38603606
[TBL] [Abstract][Full Text] [Related]
48. Computationally scalable regression modeling for ultrahigh-dimensional omics data with ParProx.
Ko S; Li GX; Choi H; Won JH
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34254998
[TBL] [Abstract][Full Text] [Related]
49. A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets.
Jiang X; Cai B; Xue D; Lu X; Cooper GF; Neapolitan RE
J Am Med Inform Assoc; 2014 Oct; 21(e2):e312-9. PubMed ID: 24737607
[TBL] [Abstract][Full Text] [Related]
50. Gradient lasso for Cox proportional hazards model.
Sohn I; Kim J; Jung SH; Park C
Bioinformatics; 2009 Jul; 25(14):1775-81. PubMed ID: 19447787
[TBL] [Abstract][Full Text] [Related]
51. A Simulation Study to Compare the Predictive Performance of Survival Neural Networks with Cox Models for Clinical Trial Data.
Kantidakis G; Biganzoli E; Putter H; Fiocco M
Comput Math Methods Med; 2021; 2021():2160322. PubMed ID: 34880930
[TBL] [Abstract][Full Text] [Related]
52. A Novel Cox Proportional Hazards Model for High-Dimensional Genomic Data in Cancer Prognosis.
Huang HH; Liang Y
IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(5):1821-1830. PubMed ID: 31870990
[TBL] [Abstract][Full Text] [Related]
53. Bayesian penalized cumulative logit model for high-dimensional data with an ordinal response.
Zhang Y; Archer KJ
Stat Med; 2021 Mar; 40(6):1453-1481. PubMed ID: 33336826
[TBL] [Abstract][Full Text] [Related]
54. Comparison of pathway and gene-level models for cancer prognosis prediction.
Zheng X; Amos CI; Frost HR
BMC Bioinformatics; 2020 Feb; 21(1):76. PubMed ID: 32111152
[TBL] [Abstract][Full Text] [Related]
55. Robust artificial neural network for reliability and sensitivity analyses of complex non-linear systems.
Oparaji U; Sheu RJ; Bankhead M; Austin J; Patelli E
Neural Netw; 2017 Dec; 96():80-90. PubMed ID: 28987979
[TBL] [Abstract][Full Text] [Related]
56. Robust estimation of the expected survival probabilities from high-dimensional Cox models with biomarker-by-treatment interactions in randomized clinical trials.
Ternès N; Rotolo F; Michiels S
BMC Med Res Methodol; 2017 May; 17(1):83. PubMed ID: 28532387
[TBL] [Abstract][Full Text] [Related]
57. IPF-LASSO: Integrative
Boulesteix AL; De Bin R; Jiang X; Fuchs M
Comput Math Methods Med; 2017; 2017():7691937. PubMed ID: 28546826
[TBL] [Abstract][Full Text] [Related]
58. Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models.
Okut H; Wu XL; Rosa GJ; Bauck S; Woodward BW; Schnabel RD; Taylor JF; Gianola D
Genet Sel Evol; 2013 Sep; 45(1):34. PubMed ID: 24024641
[TBL] [Abstract][Full Text] [Related]
59. A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.
Cai B; Jiang X
J Biomed Inform; 2014 Apr; 48():114-21. PubMed ID: 24361387
[TBL] [Abstract][Full Text] [Related]
60. Overlapping group screening for detection of gene-environment interactions with application to TCGA high-dimensional survival genomic data.
Wang JH; Wang KH; Chen YH
BMC Bioinformatics; 2022 May; 23(1):202. PubMed ID: 35637439
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]