257 related articles for article (PubMed ID: 35389435)
1. Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence.
Chen L; Wang Y; Zhao F
Bioinformatics; 2022 Jun; 38(12):3164-3172. PubMed ID: 35389435
[TBL] [Abstract][Full Text] [Related]
2. DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome.
Wang Y; Chen L
Bioinformatics; 2022 Dec; 38(24):5340-5351. PubMed ID: 36271868
[TBL] [Abstract][Full Text] [Related]
3. TSVM: Transfer Support Vector Machine for Predicting MPRA Validated Regulatory Variants.
Li M; Zhou S; Liu T; Liu C; Zang M; Wang Q
IEEE/ACM Trans Comput Biol Bioinform; 2024; 21(3):472-479. PubMed ID: 38451770
[TBL] [Abstract][Full Text] [Related]
4. TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.
Yang H; Chen R; Wang Q; Wei Q; Ji Y; Zhong X; Li B
Bioinformatics; 2022 Oct; 38(20):4697-4704. PubMed ID: 36063453
[TBL] [Abstract][Full Text] [Related]
5. transferGWAS: GWAS of images using deep transfer learning.
Kirchler M; Konigorski S; Norden M; Meltendorf C; Kloft M; Schurmann C; Lippert C
Bioinformatics; 2022 Jul; 38(14):3621-3628. PubMed ID: 35640976
[TBL] [Abstract][Full Text] [Related]
6. Chromatin accessibility prediction via a hybrid deep convolutional neural network.
Liu Q; Xia F; Yin Q; Jiang R
Bioinformatics; 2018 Mar; 34(5):732-738. PubMed ID: 29069282
[TBL] [Abstract][Full Text] [Related]
7. Quantifying functional impact of non-coding variants with multi-task Bayesian neural network.
Xu C; Liu Q; Zhou J; Xie M; Feng J; Jiang T
Bioinformatics; 2020 Mar; 36(5):1397-1404. PubMed ID: 31693090
[TBL] [Abstract][Full Text] [Related]
8. Combining artificial intelligence: deep learning with Hi-C data to predict the functional effects of non-coding variants.
Meng XH; Xiao HM; Deng HW
Bioinformatics; 2021 Jun; 37(10):1339-1344. PubMed ID: 33196774
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype.
Yin B; Balvert M; van der Spek RAA; Dutilh BE; Bohté S; Veldink J; Schönhuth A
Bioinformatics; 2019 Jul; 35(14):i538-i547. PubMed ID: 31510706
[TBL] [Abstract][Full Text] [Related]
11. parSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants.
Petrini A; Mesiti M; Schubach M; Frasca M; Danis D; Re M; Grossi G; Cappelletti L; Castrignanò T; Robinson PN; Valentini G
Gigascience; 2020 May; 9(5):. PubMed ID: 32444882
[TBL] [Abstract][Full Text] [Related]
12. Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes.
Nguyen ND; Jin T; Wang D
Bioinformatics; 2021 Jul; 37(12):1772-1775. PubMed ID: 33031552
[TBL] [Abstract][Full Text] [Related]
13. Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction.
Yang X; Yang S; Lian X; Wuchty S; Zhang Z
Bioinformatics; 2021 Dec; 37(24):4771-4778. PubMed ID: 34273146
[TBL] [Abstract][Full Text] [Related]
14. DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach.
Agarwal A; Chen L
Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36495179
[TBL] [Abstract][Full Text] [Related]
15. Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning.
Lai B; Qian S; Zhang H; Zhang S; Kozlova A; Duan J; Xu J; He X
PLoS Comput Biol; 2022 May; 18(5):e1010011. PubMed ID: 35576194
[TBL] [Abstract][Full Text] [Related]
16. A simple convolutional neural network for prediction of enhancer-promoter interactions with DNA sequence data.
Zhuang Z; Shen X; Pan W
Bioinformatics; 2019 Sep; 35(17):2899-2906. PubMed ID: 30649185
[TBL] [Abstract][Full Text] [Related]
17. DNA language models are powerful predictors of genome-wide variant effects.
Benegas G; Batra SS; Song YS
Proc Natl Acad Sci U S A; 2023 Oct; 120(44):e2311219120. PubMed ID: 37883436
[TBL] [Abstract][Full Text] [Related]
18. sscNOVA: a semi-supervised convolutional neural network for predicting functional regulatory variants in autoimmune diseases.
Li H; Yu Z; Du F; Song L; Gao Y; Shi F
Front Immunol; 2024; 15():1323072. PubMed ID: 38380333
[TBL] [Abstract][Full Text] [Related]
19. GPDBN: deep bilinear network integrating both genomic data and pathological images for breast cancer prognosis prediction.
Wang Z; Li R; Wang M; Li A
Bioinformatics; 2021 Sep; 37(18):2963-2970. PubMed ID: 33734318
[TBL] [Abstract][Full Text] [Related]
20. Openness weighted association studies: leveraging personal genome information to prioritize non-coding variants.
Song S; Shan N; Wang G; Yan X; Liu JS; Hou L
Bioinformatics; 2021 Dec; 37(24):4737-4743. PubMed ID: 34260700
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
