155 related articles for article (PubMed ID: 31090248)
1. Predicting the impact of single nucleotide variants on splicing via sequence-based deep neural networks and genomic features.
Naito T
Hum Mutat; 2019 Sep; 40(9):1261-1269. PubMed ID: 31090248
[TBL] [Abstract][Full Text] [Related]
2. CADD-Splice-improving genome-wide variant effect prediction using deep learning-derived splice scores.
Rentzsch P; Schubach M; Shendure J; Kircher M
Genome Med; 2021 Feb; 13(1):31. PubMed ID: 33618777
[TBL] [Abstract][Full Text] [Related]
3. Genomic features defining exonic variants that modulate splicing.
Woolfe A; Mullikin JC; Elnitski L
Genome Biol; 2010; 11(2):R20. PubMed ID: 20158892
[TBL] [Abstract][Full Text] [Related]
4. CAGI experiments: Modeling sequence variant impact on gene splicing using predictions from computational tools.
Gotea V; Margolin G; Elnitski L
Hum Mutat; 2019 Sep; 40(9):1252-1260. PubMed ID: 31066132
[TBL] [Abstract][Full Text] [Related]
5. Performance evaluation of computational methods for splice-disrupting variants and improving the performance using the machine learning-based framework.
Liu H; Dai J; Li K; Sun Y; Wei H; Wang H; Zhao C; Wang DW
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35976049
[TBL] [Abstract][Full Text] [Related]
6. Assessing predictions of the impact of variants on splicing in CAGI5.
Mount SM; Avsec Ž; Carmel L; Casadio R; Çelik MH; Chen K; Cheng J; Cohen NE; Fairbrother WG; Fenesh T; Gagneur J; Gotea V; Holzer T; Lin CF; Martelli PL; Naito T; Nguyen TYD; Savojardo C; Unger R; Wang R; Yang Y; Zhao H
Hum Mutat; 2019 Sep; 40(9):1215-1224. PubMed ID: 31301154
[TBL] [Abstract][Full Text] [Related]
7. Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.
Zhang W; Zhu X; Fu Y; Tsuji J; Weng Z
BMC Bioinformatics; 2017 Dec; 18(Suppl 13):464. PubMed ID: 29219070
[TBL] [Abstract][Full Text] [Related]
8. CAGI 5 splicing challenge: Improved exon skipping and intron retention predictions with MMSplice.
Cheng J; Çelik MH; Nguyen TYD; Avsec Ž; Gagneur J
Hum Mutat; 2019 Sep; 40(9):1243-1251. PubMed ID: 31070280
[TBL] [Abstract][Full Text] [Related]
9. MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing.
Mort M; Sterne-Weiler T; Li B; Ball EV; Cooper DN; Radivojac P; Sanford JR; Mooney SD
Genome Biol; 2014 Jan; 15(1):R19. PubMed ID: 24451234
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Systematic Computational Identification of Variants That Activate Exonic and Intronic Cryptic Splice Sites.
Lee M; Roos P; Sharma N; Atalar M; Evans TA; Pellicore MJ; Davis E; Lam AN; Stanley SE; Khalil SE; Solomon GM; Walker D; Raraigh KS; Vecchio-Pagan B; Armanios M; Cutting GR
Am J Hum Genet; 2017 May; 100(5):751-765. PubMed ID: 28475858
[TBL] [Abstract][Full Text] [Related]
12. Future directions for high-throughput splicing assays in precision medicine.
Rhine CL; Neil C; Glidden DT; Cygan KJ; Fredericks AM; Wang J; Walton NA; Fairbrother WG
Hum Mutat; 2019 Sep; 40(9):1225-1234. PubMed ID: 31297895
[TBL] [Abstract][Full Text] [Related]
13. Combining genetic constraint with predictions of alternative splicing to prioritize deleterious splicing in rare disease studies.
Cormier MJ; Pedersen BS; Bayrak-Toydemir P; Quinlan AR
BMC Bioinformatics; 2022 Nov; 23(1):482. PubMed ID: 36376793
[TBL] [Abstract][Full Text] [Related]
14. Machine learning random forest for predicting oncosomatic variant NGS analysis.
Pellegrino E; Jacques C; Beaufils N; Nanni I; Carlioz A; Metellus P; Ouafik L
Sci Rep; 2021 Nov; 11(1):21820. PubMed ID: 34750410
[TBL] [Abstract][Full Text] [Related]
15. Familial adenomatous polyposis: aberrant splicing due to missense or silent mutations in the APC gene.
Aretz S; Uhlhaas S; Sun Y; Pagenstecher C; Mangold E; Caspari R; Möslein G; Schulmann K; Propping P; Friedl W
Hum Mutat; 2004 Nov; 24(5):370-80. PubMed ID: 15459959
[TBL] [Abstract][Full Text] [Related]
16. Functional analysis of a large set of BRCA2 exon 7 variants highlights the predictive value of hexamer scores in detecting alterations of exonic splicing regulatory elements.
Di Giacomo D; Gaildrat P; Abuli A; Abdat J; Frébourg T; Tosi M; Martins A
Hum Mutat; 2013 Nov; 34(11):1547-57. PubMed ID: 23983145
[TBL] [Abstract][Full Text] [Related]
17. Predicting the change of exon splicing caused by genetic variant using support vector regression.
Chen K; Lu Y; Zhao H; Yang Y
Hum Mutat; 2019 Sep; 40(9):1235-1242. PubMed ID: 31070294
[TBL] [Abstract][Full Text] [Related]
18. SPiP: Splicing Prediction Pipeline, a machine learning tool for massive detection of exonic and intronic variant effects on mRNA splicing.
Leman R; Parfait B; Vidaud D; Girodon E; Pacot L; Le Gac G; Ka C; Ferec C; Fichou Y; Quesnelle C; Aucouturier C; Muller E; Vaur D; Castera L; Boulouard F; Ricou A; Tubeuf H; Soukarieh O; Gaildrat P; Riant F; Guillaud-Bataille M; Caputo SM; Caux-Moncoutier V; Boutry-Kryza N; Bonnet-Dorion F; Schultz I; Rossing M; Quenez O; Goldenberg L; Harter V; Parsons MT; Spurdle AB; Frébourg T; Martins A; Houdayer C; Krieger S
Hum Mutat; 2022 Dec; 43(12):2308-2323. PubMed ID: 36273432
[TBL] [Abstract][Full Text] [Related]
19. Splicing impact of deep exonic missense variants in CAPN3 explored systematically by minigene functional assay.
Dionnet E; Defour A; Da Silva N; Salvi A; Lévy N; Krahn M; Bartoli M; Puppo F; Gorokhova S
Hum Mutat; 2020 Oct; 41(10):1797-1810. PubMed ID: 32668095
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
