211 related articles for article (PubMed ID: 32696716)
1. EDeepSSP: Explainable deep neural networks for exact splice sites prediction.
Amilpur S; Bhukya R
J Bioinform Comput Biol; 2020 Aug; 18(4):2050024. PubMed ID: 32696716
[TBL] [Abstract][Full Text] [Related]
2. SpliceFinder: ab initio prediction of splice sites using convolutional neural network.
Wang R; Wang Z; Wang J; Li S
BMC Bioinformatics; 2019 Dec; 20(Suppl 23):652. PubMed ID: 31881982
[TBL] [Abstract][Full Text] [Related]
3. Evaluating the performance of sequence encoding schemes and machine learning methods for splice sites recognition.
Meher PK; Sahu TK; Gahoi S; Satpathy S; Rao AR
Gene; 2019 Jul; 705():113-126. PubMed ID: 31009682
[TBL] [Abstract][Full Text] [Related]
4. DRANetSplicer: A Splice Site Prediction Model Based on Deep Residual Attention Networks.
Liu X; Zhang H; Zeng Y; Zhu X; Zhu L; Fu J
Genes (Basel); 2024 Mar; 15(4):. PubMed ID: 38674339
[TBL] [Abstract][Full Text] [Related]
5. Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA.
Albaradei S; Magana-Mora A; Thafar M; Uludag M; Bajic VB; Gojobori T; Essack M; Jankovic BR
Gene; 2020 Dec; 763S():100035. PubMed ID: 34493371
[TBL] [Abstract][Full Text] [Related]
6. MaTPIP: A deep-learning architecture with eXplainable AI for sequence-driven, feature mixed protein-protein interaction prediction.
Ghosh S; Mitra P
Comput Methods Programs Biomed; 2024 Feb; 244():107955. PubMed ID: 38064959
[TBL] [Abstract][Full Text] [Related]
7. Human Splice-Site Prediction with Deep Neural Networks.
Naito T
J Comput Biol; 2018 Aug; 25(8):954-961. PubMed ID: 29668310
[TBL] [Abstract][Full Text] [Related]
8. EnsembleSplice: ensemble deep learning model for splice site prediction.
Akpokiro V; Martin T; Oluwadare O
BMC Bioinformatics; 2022 Oct; 23(1):413. PubMed ID: 36203144
[TBL] [Abstract][Full Text] [Related]
9. An automated framework for evaluation of deep learning models for splice site predictions.
Zabardast A; Tamer EG; Son YA; Yılmaz A
Sci Rep; 2023 Jun; 13(1):10221. PubMed ID: 37353532
[TBL] [Abstract][Full Text] [Related]
10. Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences.
Chen Z; Zhao P; Li F; Wang Y; Smith AI; Webb GI; Akutsu T; Baggag A; Bensmail H; Song J
Brief Bioinform; 2020 Sep; 21(5):1676-1696. PubMed ID: 31714956
[TBL] [Abstract][Full Text] [Related]
11. SpliceRover: interpretable convolutional neural networks for improved splice site prediction.
Zuallaert J; Godin F; Kim M; Soete A; Saeys Y; De Neve W
Bioinformatics; 2018 Dec; 34(24):4180-4188. PubMed ID: 29931149
[TBL] [Abstract][Full Text] [Related]
12. Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA.
Albaradei S; Magana-Mora A; Thafar M; Uludag M; Bajic VB; Gojobori T; Essack M; Jankovic BR
Gene X; 2020 Dec; 5():100035. PubMed ID: 32550561
[TBL] [Abstract][Full Text] [Related]
13. A computational approach for prediction of donor splice sites with improved accuracy.
Meher PK; Sahu TK; Rao AR; Wahi SD
J Theor Biol; 2016 Sep; 404():285-294. PubMed ID: 27302911
[TBL] [Abstract][Full Text] [Related]
14. DeepDSSR: Deep Learning Structure for Human Donor Splice Sites Recognition.
Alam T; Islam MT; Househ M; Bouzerdoum A; Kawsar FA
Stud Health Technol Inform; 2019 Jul; 262():236-239. PubMed ID: 31349311
[TBL] [Abstract][Full Text] [Related]
15. Representation learning of genomic sequence motifs with convolutional neural networks.
Koo PK; Eddy SR
PLoS Comput Biol; 2019 Dec; 15(12):e1007560. PubMed ID: 31856220
[TBL] [Abstract][Full Text] [Related]
16. Predicting the effect of variants on splicing using Convolutional Neural Networks.
Thanapattheerakul T; Engchuan W; Chan JH
PeerJ; 2020; 8():e9470. PubMed ID: 32704450
[TBL] [Abstract][Full Text] [Related]
17. Scale-space approximated convolutional neural networks for retinal vessel segmentation.
Noh KJ; Park SJ; Lee S
Comput Methods Programs Biomed; 2019 Sep; 178():237-246. PubMed ID: 31416552
[TBL] [Abstract][Full Text] [Related]
18. Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture.
Li Z; Fang J; Wang S; Zhang L; Chen Y; Pian C
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35189635
[TBL] [Abstract][Full Text] [Related]
19. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.
Pan X; Shen HB
BMC Bioinformatics; 2017 Feb; 18(1):136. PubMed ID: 28245811
[TBL] [Abstract][Full Text] [Related]
20. COSSMO: predicting competitive alternative splice site selection using deep learning.
Bretschneider H; Gandhi S; Deshwar AG; Zuberi K; Frey BJ
Bioinformatics; 2018 Jul; 34(13):i429-i437. PubMed ID: 29949959
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]