105 related articles for article (PubMed ID: 33381817)
1. DeepSELEX: inferring DNA-binding preferences from HT-SELEX data using multi-class CNNs.
Asif M; Orenstein Y
Bioinformatics; 2020 Dec; 36(Suppl_2):i634-i642. PubMed ID: 33381817
[TBL] [Abstract][Full Text] [Related]
2. BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data.
Kähärä J; Lähdesmäki H
Bioinformatics; 2015 Sep; 31(17):2852-9. PubMed ID: 25957350
[TBL] [Abstract][Full Text] [Related]
3. High-Throughput Protein Production Combined with High- Throughput SELEX Identifies an Extensive Atlas of Ciona robusta Transcription Factor DNA-Binding Specificities.
Nitta KR; Vincentelli R; Jacox E; Cimino A; Ohtsuka Y; Sobral D; Satou Y; Cambillau C; Lemaire P
Methods Mol Biol; 2019; 2025():487-517. PubMed ID: 31267468
[TBL] [Abstract][Full Text] [Related]
4. Discovering epistatic feature interactions from neural network models of regulatory DNA sequences.
Greenside P; Shimko T; Fordyce P; Kundaje A
Bioinformatics; 2018 Sep; 34(17):i629-i637. PubMed ID: 30423062
[TBL] [Abstract][Full Text] [Related]
5. Improved linking of motifs to their TFs using domain information.
Baumgarten N; Schmidt F; Schulz MH
Bioinformatics; 2020 Mar; 36(6):1655-1662. PubMed ID: 31742324
[TBL] [Abstract][Full Text] [Related]
6. A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data.
Orenstein Y; Shamir R
Nucleic Acids Res; 2014 Apr; 42(8):e63. PubMed ID: 24500199
[TBL] [Abstract][Full Text] [Related]
7. APTANI: a computational tool to select aptamers through sequence-structure motif analysis of HT-SELEX data.
Caroli J; Taccioli C; De La Fuente A; Serafini P; Bicciato S
Bioinformatics; 2016 Jan; 32(2):161-4. PubMed ID: 26395772
[TBL] [Abstract][Full Text] [Related]
8. BEESEM: estimation of binding energy models using HT-SELEX data.
Ruan S; Swamidass SJ; Stormo GD
Bioinformatics; 2017 Aug; 33(15):2288-2295. PubMed ID: 28379348
[TBL] [Abstract][Full Text] [Related]
9. SELEX-Seq: A Method to Determine DNA Binding Specificities of Plant Transcription Factors.
Smaczniak C; Angenent GC; Kaufmann K
Methods Mol Biol; 2017; 1629():67-82. PubMed ID: 28623580
[TBL] [Abstract][Full Text] [Related]
10. Better estimation of protein-DNA interaction parameters improve prediction of functional sites.
Nagaraj VH; O'Flanagan RA; Sengupta AM
BMC Biotechnol; 2008 Dec; 8():94. PubMed ID: 19105805
[TBL] [Abstract][Full Text] [Related]
11. Predicting transcription factor binding motifs from DNA-binding domains, chromatin accessibility and gene expression data.
Zamanighomi M; Lin Z; Wang Y; Jiang R; Wong WH
Nucleic Acids Res; 2017 Jun; 45(10):5666-5677. PubMed ID: 28472398
[TBL] [Abstract][Full Text] [Related]
12. DeepZF: improved DNA-binding prediction of C2H2-zinc-finger proteins by deep transfer learning.
Aizenshtein-Gazit S; Orenstein Y
Bioinformatics; 2022 Sep; 38(Suppl_2):ii62-ii67. PubMed ID: 36124796
[TBL] [Abstract][Full Text] [Related]
13. Modeling protein-DNA binding via high-throughput in vitro technologies.
Orenstein Y; Shamir R
Brief Funct Genomics; 2017 May; 16(3):171-180. PubMed ID: 27497616
[TBL] [Abstract][Full Text] [Related]
14. Neural networks with circular filters enable data efficient inference of sequence motifs.
Blum CF; Kollmann M
Bioinformatics; 2019 Oct; 35(20):3937-3943. PubMed ID: 30918943
[TBL] [Abstract][Full Text] [Related]
15. Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape.
Dai H; Umarov R; Kuwahara H; Li Y; Song L; Gao X
Bioinformatics; 2017 Nov; 33(22):3575-3583. PubMed ID: 28961686
[TBL] [Abstract][Full Text] [Related]
16. A deep neural network approach for learning intrinsic protein-RNA binding preferences.
Ben-Bassat I; Chor B; Orenstein Y
Bioinformatics; 2018 Sep; 34(17):i638-i646. PubMed ID: 30423078
[TBL] [Abstract][Full Text] [Related]
17. SELEX-seq: a method for characterizing the complete repertoire of binding site preferences for transcription factor complexes.
Riley TR; Slattery M; Abe N; Rastogi C; Liu D; Mann RS; Bussemaker HJ
Methods Mol Biol; 2014; 1196():255-78. PubMed ID: 25151169
[TBL] [Abstract][Full Text] [Related]
18. Inferring protein-DNA interaction parameters from SELEX experiments.
Djordjevic M
Methods Mol Biol; 2010; 674():195-211. PubMed ID: 20827593
[TBL] [Abstract][Full Text] [Related]
19. Quantitative modeling of transcription factor binding specificities using DNA shape.
Zhou T; Shen N; Yang L; Abe N; Horton J; Mann RS; Bussemaker HJ; Gordân R; Rohs R
Proc Natl Acad Sci U S A; 2015 Apr; 112(15):4654-9. PubMed ID: 25775564
[TBL] [Abstract][Full Text] [Related]
20. In vitro DNA-binding profile of transcription factors: methods and new insights.
Wang J; Lu J; Gu G; Liu Y
J Endocrinol; 2011 Jul; 210(1):15-27. PubMed ID: 21389103
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]