165 related articles for article (PubMed ID: 38588559)
1. Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation.
Duncan AG; Mitchell JA; Moses AM
Bioinformatics; 2024 Mar; 40(4):. PubMed ID: 38588559
[TBL] [Abstract][Full Text] [Related]
2. A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica.
Calderon-Ramirez S; Murillo-Hernandez D; Rojas-Salazar K; Elizondo D; Yang S; Moemeni A; Molina-Cabello M
Med Biol Eng Comput; 2022 Apr; 60(4):1159-1175. PubMed ID: 35239108
[TBL] [Abstract][Full Text] [Related]
3. Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences.
Mourad R
BMC Bioinformatics; 2023 May; 24(1):186. PubMed ID: 37147561
[TBL] [Abstract][Full Text] [Related]
4. Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.
Li Y; Shi W; Wasserman WW
BMC Bioinformatics; 2018 May; 19(1):202. PubMed ID: 29855387
[TBL] [Abstract][Full Text] [Related]
5. A semi-supervised deep learning approach for predicting the functional effects of genomic non-coding variations.
Jia H; Park SJ; Nakai K
BMC Bioinformatics; 2021 Jun; 22(Suppl 6):128. PubMed ID: 34078253
[TBL] [Abstract][Full Text] [Related]
6. A self-supervised deep learning method for data-efficient training in genomics.
Gündüz HA; Binder M; To XY; Mreches R; Bischl B; McHardy AC; Münch PC; Rezaei M
Commun Biol; 2023 Sep; 6(1):928. PubMed ID: 37696966
[TBL] [Abstract][Full Text] [Related]
7. Mantis-ml: Disease-Agnostic Gene Prioritization from High-Throughput Genomic Screens by Stochastic Semi-supervised Learning.
Vitsios D; Petrovski S
Am J Hum Genet; 2020 May; 106(5):659-678. PubMed ID: 32386536
[TBL] [Abstract][Full Text] [Related]
8. Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification.
Otálora S; Marini N; Müller H; Atzori M
BMC Med Imaging; 2021 May; 21(1):77. PubMed ID: 33964886
[TBL] [Abstract][Full Text] [Related]
9. Deep learning: new computational modelling techniques for genomics.
Eraslan G; Avsec Ž; Gagneur J; Theis FJ
Nat Rev Genet; 2019 Jul; 20(7):389-403. PubMed ID: 30971806
[TBL] [Abstract][Full Text] [Related]
10. Assessing the reliability of point mutation as data augmentation for deep learning with genomic data.
Lee H; Ozbulak U; Park H; Depuydt S; De Neve W; Vankerschaver J
BMC Bioinformatics; 2024 Apr; 25(1):170. PubMed ID: 38689247
[TBL] [Abstract][Full Text] [Related]
11. Genomic benchmarks: a collection of datasets for genomic sequence classification.
Grešová K; Martinek V; Čechák D; Šimeček P; Alexiou P
BMC Genom Data; 2023 May; 24(1):25. PubMed ID: 37127596
[TBL] [Abstract][Full Text] [Related]
12. EvoAug-TF: extending evolution-inspired data augmentations for genomic deep learning to TensorFlow.
Yu Y; Muthukumar S; Koo PK
Bioinformatics; 2024 Mar; 40(3):. PubMed ID: 38366935
[TBL] [Abstract][Full Text] [Related]
13. DeepOM: single-molecule optical genome mapping via deep learning.
Nogin Y; Detinis Zur T; Margalit S; Barzilai I; Alalouf O; Ebenstein Y; Shechtman Y
Bioinformatics; 2023 Mar; 39(3):. PubMed ID: 36929928
[TBL] [Abstract][Full Text] [Related]
14. A review of medical image data augmentation techniques for deep learning applications.
Chlap P; Min H; Vandenberg N; Dowling J; Holloway L; Haworth A
J Med Imaging Radiat Oncol; 2021 Aug; 65(5):545-563. PubMed ID: 34145766
[TBL] [Abstract][Full Text] [Related]
15. Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data.
Lourenço VM; Ogutu JO; Rodrigues RAP; Posekany A; Piepho HP
BMC Genomics; 2024 Feb; 25(1):152. PubMed ID: 38326768
[TBL] [Abstract][Full Text] [Related]
16. FMixCutMatch for semi-supervised deep learning.
Wei X; Wei X; Kong X; Lu S; Xing W; Lu W
Neural Netw; 2021 Jan; 133():166-176. PubMed ID: 33217685
[TBL] [Abstract][Full Text] [Related]
17. Off the deep end: What can deep learning do for the gene expression field?
Raicu AM; Fay JC; Rohner N; Zeitlinger J; Arnosti DN
J Biol Chem; 2023 Jan; 299(1):102760. PubMed ID: 36462664
[TBL] [Abstract][Full Text] [Related]
18. Semi-supervised task-driven data augmentation for medical image segmentation.
Chaitanya K; Karani N; Baumgartner CF; Erdil E; Becker A; Donati O; Konukoglu E
Med Image Anal; 2021 Feb; 68():101934. PubMed ID: 33385699
[TBL] [Abstract][Full Text] [Related]
19. Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning.
Desai AD; Ozturkler BM; Sandino CM; Boutin R; Willis M; Vasanawala S; Hargreaves BA; Ré C; Pauly JM; Chaudhari AS
Magn Reson Med; 2023 Nov; 90(5):2052-2070. PubMed ID: 37427449
[TBL] [Abstract][Full Text] [Related]
20. Increasing the accuracy of single sequence prediction methods using a deep semi-supervised learning framework.
Moffat L; Jones DT
Bioinformatics; 2021 Nov; 37(21):3744-3751. PubMed ID: 34213528
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]