生物制造中核酸元件的智能设计
作者:
基金项目:

国家重点研发计划(2022YFC2106200)


Intelligent design of nucleic acid elements in biomanufacturing
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [99]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    核酸元件是重要的功能性核酸序列,在生物制造中通过基因表达调控、代谢途径优化和基因编辑等方面来影响目标产物的合成,因此核酸元件的设计和优化对细胞工厂的构建有重要作用。通过人工智能技术可以准确有效地预测功能性核酸元件,设计和优化功能稳定的元件序列,同时解析其作用机制,为生物制造提供强大的技术支持。近年来,人工智能技术在生物制造中通过设计启动子、核糖体结合位点和终止子等核酸元件及其组合,可以大幅度减少实验工作量,加快生物制造进程。但是由于生物系统的复杂性和高质量训练数据不足等问题,导致核酸元件的智能设计在生物制造中的应用较为单一。本文综述了应用于生物制造的各种DNA和RNA核酸元件,基于人工智能算法构建的核酸元件预测和设计工具及人工智能技术在生物制造中的应用案例。未来通过整合人工智能技术、合成生物学和高通量技术等,有望开发更高效准确的核酸元件设计方法,加速其在生物制造中的应用。

    Abstract:

    Nucleic acid elements are essential functional sequences that play critical roles in regulating gene expression, optimizing pathways, and enabling gene editing to enhance the production of target products in biomanufacturing. Therefore, the design and optimization of these elements are crucial in constructing efficient cell factories. Artificial intelligence (AI) provides robust support for biomanufacturing by accurately predicting functional nucleic acid elements, designing and optimizing sequences with quantified functions, and elucidating the operating mechanisms of these elements. In recent years, AI has significantly accelerated the progress in biomanufacturing by reducing experimental workloads through the design and optimization of promoters, ribosome-binding sites, terminators, and their combinations. Despite these advancements, the application of AI in biomanufacturing remains limited due to the complexity of biological systems and the lack of highly quantified training data. This review summarizes the various nucleic acid elements utilized in biomanufacturing, the tools developed for predicting and designing these elements based on AI algorithms, and the case studies showcasing the applications of AI in biomanufacturing. By integrating AI with synthetic biology and high-throughput techniques, we anticipate the development of more efficient tools for designing nucleic acid elements and accelerating the application of AI in biomanufacturing.

    参考文献
    [1] KAIKKONEN MU, LAM MT, GLASS CK. Non-coding RNAs as regulators of gene expression and epigenetics[J]. Cardiovascular Research, 2011, 90(3): 430.
    [2] CHUANG LY, TSAI JH, YANG CH. Binary particle swarm optimization for operon prediction[J]. Nucleic Acids Research, 2010, 38(12): e128.
    [3] JIANG YR, LUO J, HUANG DQ, LIU Y, LI DD. Machine learning advances in microbiology: a review of methods and applications[J]. Frontiers in Microbiology, 2022, 13: 925454.
    [4] YANG RT, WU F, ZHANG CJ, ZHANG LN. iEnhancer-GAN: a deep learning framework in combination with word embedding and sequence generative adversarial net to identify enhancers and their strength[J]. International Journal of Molecular Sciences, 2021, 22(7): 3589.
    [5] QUANG D, XIE XH. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences[J]. Nucleic Acids Research, 2016, 44(11): e107.
    [6] VORLÄNDER MK, PACHECO-FIALLOS B, PLASCHKA C. Structural basis of mRNA maturation: time to put it together[J]. Current Opinion in Structural Biology, 2022, 75: 102431.
    [7] SOLLER M. Pre-messenger RNA processing and its regulation: a genomic perspective[J]. Cellular and Molecular Life Sciences, 2006, 63(7/8): 796-819.
    [8] BHUKYA R, KUMARI A, AMILPUR S, DASARI CM. PPred-PCKSM: a multi-layer predictor for identifying promoter and its variants using position based features[J]. Computational Biology and Chemistry, 2022, 97: 107623.
    [9] STRUHL K. Fundamentally different logic of gene regulation in eukaryotes and prokaryotes[J]. Cell, 1999, 98(1): 1-4.
    [10] RANGEL-CHAVEZ C, GALAN-VASQUEZ E, MARTINEZ-ANTONIO A. Consensus architecture of promoters and transcription units in Escherichia coli: design principles for synthetic biology[J]. Molecular BioSystems, 2017, 13(4): 665-676.
    [11] KRISHNAMURTHY S, HAMPSEY M. Eukaryotic transcription initiation[J]. Current Biology, 2009, 19(4): R153-R156.
    [12] RINALDI AJ, LUND PE, BLANCO MR, WALTER NG. The Shine-Dalgarno sequence of riboswitch-regulated single mRNAs shows ligand-dependent accessibility bursts[J]. Nature Communications, 2016, 7: 8976.
    [13] KOZAK M. Structural features in eukaryotic mRNAs that modulate the initiation of translation[J]. Journal of Biological Chemistry, 1991, 266(30): 19867-19870.
    [14] BULGER M, GROUDINE M. Functional and mechanistic diversity of distal transcription enhancers[J]. Cell, 2011, 144(3): 327-339.
    [15] KLEFTOGIANNIS D, KALNIS P, BAJIC VB. Progress and challenges in bioinformatics approaches for enhancer identification[J]. Briefings in Bioinformatics, 2016, 17(6): 967-979.
    [16] TOGNON M, GIUGNO R, PINELLO L. A survey on algorithms to characterize transcription factor binding sites[J]. Briefings in Bioinformatics, 2023, 24(3): bbad156.
    [17] FISCHER V, SCHUMACHER K, TORA L, DEVYS D. Global role for coactivator complexes in RNA polymerase II transcription[J]. Transcription, 2019, 10(1): 29-36.
    [18] LAWSON MR, BERGER JM. Tuning the sequence specificity of a transcription terminator[J]. Current Genetics, 2019, 65(3): 729-733.
    [19] SALVAIL H, BREAKER RR. Riboswitches[J]. Current Biology, 2023, 33(9): R343-R348.
    [20] SCHMIDT CM, SMOLKE CD. RNA switches for synthetic biology[J]. Cold Spring Harbor Perspectives in Biology, 2019, 11(1): a032532.
    [21] KAVITA K, BREAKER RR. Discovering riboswitches: the past and the future[J]. Trends in Biochemical Sciences, 2023, 48(2): 119-141.
    [22] GREEN AA, SILVER PA, COLLINS JJ, YIN P. Toehold switches: de-novo-designed regulators of gene expression[J]. Cell, 2014, 159(4): 925-939.
    [23] HANNON GJ. RNA interference[J]. Nature, 2002, 418(6894): 244-251.
    [24] SALMENA L, POLISENO L, TAY Y, KATS L, PANDOLFI PP. A ceRNA hypothesis: the Rosetta stone of a hidden RNA language?[J]. Cell, 2011, 146(3): 353-358.
    [25] TAY Y, RINN J, PANDOLFI PP. The multilayered complexity of ceRNA crosstalk and competition[J]. Nature, 2014, 505(7483): 344-352.
    [26] SEN R, GHOSAL S, DAS S, BALTI S, CHAKRABARTI J. Competing endogenous RNA: the key to posttranscriptional regulation[J]. The Scientific World Journal, 2014, 2014: 896206.
    [27] HESS GT, TYCKO J, YAO D, BASSIK MC. Methods and applications of CRISPR-mediated base editing in eukaryotic genomes[J]. Molecular Cell, 2017, 68(1): 26-43.
    [28] NISHIDA K, KONDO A. CRISPR-derived genome editing technologies for metabolic engineering[J]. Metabolic Engineering, 2021, 63: 141-147.
    [29] KILDEGAARD KR, TRAMONTIN LRR, CHEKINA K, LI MJ, GOEDECKE TJ, KRISTENSEN M, BORODINA I. CRISPR/Cas9-RNA interference system for combinatorial metabolic engineering of Saccharomyces cerevisiae[J]. Yeast, 2019, 36(5): 237-247.
    [30] LIU D, MANNAN AA, HAN YC, OYARZÚN DA, ZHANG FZ. Dynamic metabolic control: towards precision engineering of metabolism[J]. Journal of Industrial Microbiology & Biotechnology, 2018, 45(7): 535-543.
    [31] ZHANG YP, SUN JB, MA YH. Biomanufacturing: history and perspective[J]. Journal of Industrial Microbiology & Biotechnology, 2017, 44(4-5): 773-784.
    [32] LIEBAL UW, KÖBBING S, NETZE L, SCHWEIDTMANN AM, MITSOS A, BLANK LM. Insight to gene expression from promoter libraries with the machine learning workflow Exp2Ipynb[J]. Frontiers in Bioinformatics, 2021, 1: 747428.
    [33] JACOBS TM, YUMEREFENDI H, KUHLMAN B, LEAVER-FAY A. SwiftLib: rapid degenerate-codon-library optimization through dynamic programming[J]. Nucleic Acids Research, 2015, 43(5): e34.
    [34] ZHANG GS, DENG YY, LIU QY, YE BX, DAI ZM, CHEN YW, DAI XH. Identifying circular RNA and predicting its regulatory interactions by machine learning[J]. Frontiers in Genetics, 2020, 11: 655.
    [35] de BOER CG, VAISHNAV ED, SADEH R, ABEYTA EL, FRIEDMAN N, REGEV A. Deciphering eukaryotic gene-regulatory logic with 100 million random promoters[J]. Nature Biotechnology, 2020, 38(1): 56-65.
    [36] LIU XY, GUPTA STP, BHIMSARIA D, REED JL, RODRÍGUEZ-MARTÍNEZ JA, ANSARI AZ, RAMAN S. De novo design of programmable inducible promoters[J]. Nucleic Acids Research, 2019, 47(19): 10452-10463.
    [37] HE WY, JIA CZ, DUAN YC, ZOU Q. 70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features[J]. BMC Systems Biology, 2018, 12(Suppl 4): 44.
    [38] PAUL S, OLYMON K, MARTINEZ GS, SARKAR S, YELLA VR, KUMAR A. MLDSPP: bacterial promoter prediction tool using DNA structural properties with machine learning and explainable AI[J]. Journal of Chemical Information and Modeling, 2024, 64(7): 2705-2719.
    [39] UMAROV RK, SOLOVYEV VV. Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks[J]. PLoS One, 2017, 12(2): e0171410.
    [40] WANG Y, WANG HC, WEI L, LI SL, LIU LY, WANG XW. Synthetic promoter design in Escherichia coli based on a deep generative network[J]. Nucleic Acids Research, 2020, 48(12): 6403-6412.
    [41] ZHANG PC, WANG HC, XU HW, WEI L, LIU LY, HU ZR, WANG XW. Deep flanking sequence engineering for efficient promoter design using DeepSEED[J]. Nature Communications, 2023, 14(1): 6309.
    [42] SALIS HM, MIRSKY EA, VOIGT CA. Automated design of synthetic ribosome binding sites to control protein expression[J]. Nature Biotechnology, 2009, 27(10): 946-950.
    [43] ZHANG S, HU HL, JIANG T, ZHANG L, ZENG JY. TITER: predicting translation initiation sites by deep learning[J]. Bioinformatics, 2017, 33(14): i234-i242.
    [44] HÖLLERER S, PAPAXANTHOS L, GUMPINGER AC, FISCHER K, BEISEL C, BORGWARDT K, BENENSON Y, JESCHEK M. Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping[J]. Nature Communications, 2020, 11(1): 3551.
    [45] FRANCIS-LYON P, CRISTIANINI N, HOLBROOK S. Terminator detection by support vector machine utilizing a stochastic context-free grammar[C]//2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology. April 1–5, 2007, Honolulu, HI, USA. IEEE, 2007: 170-177.
    [46] ZHAI WJ, DUAN YT, ZHANG XM, XU GQ, LI H, SHI JS, XU ZH, ZHANG XJ. Sequence and thermodynamic characteristics of terminators revealed by FlowSeq and the discrimination of terminators strength[J]. Synthetic and Systems Biotechnology, 2022, 7(4): 1046-1055.
    [47] KLEFTOGIANNIS D, KALNIS P, BAJIC VB. DEEP: a general computational framework for predicting enhancers[J]. Nucleic Acids Research, 2015, 43(1): e6.
    [48] HAFEZ D, KARABACAK A, KRUEGER S, HWANG YC, WANG LS, ZINZEN RP, OHLER U. McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes[J]. Genome Biology, 2017, 18(1): 199.
    [49] KÄHÄRÄ J, LÄHDESMÄKI H. BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data[J]. Bioinformatics, 2015, 31(17): 2852-2859.
    [50] DINAKARPANDIAN D, RAHEJA V, MEHTA S, SCHUETZ EG, ROGAN PK. Tandem machine learning for the identification of genes regulated by transcription factors[J]. BMC Bioinformatics, 2005, 6: 204.
    [51] ZHANG YQ, WANG ZX, ZENG YQ, ZHOU JL, ZOU Q. High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method[J]. Briefings in Bioinformatics, 2021, 22(6): bbab273.
    [52] PATIYAL S, SINGH N, ALI MZ, PUNDIR DS, RAGHAVA GPS. Sigma70Pred: a highly accurate method for predicting sigma70 promoter in Escherichia coli K-12 strains[J]. Frontiers in Microbiology, 2022, 13: 1042127.
    [53] Di SALVO M, PINATEL E, TALÀ A, FONDI M, PEANO C, ALIFANO P. G4PromFinder: an algorithm for predicting transcription promoters in GC-rich bacterial genomes based on AT-rich elements and G-quadruplex motifs[J]. BMC Bioinformatics, 2018, 19(1): 36.
    [54] RAHMAN MS, AKTAR U, JANI MR, SHATABDA S. iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features[J]. Molecular Genetics and Genomics, 2019, 294(1): 69-84.
    [55] LIU B, LI K. iPromoter-2L2.0: identifying promoters and their types by combining smoothing cutting window algorithm and sequence-based features[J]. Molecular Therapy-Nucleic Acids, 2019, 18: 80-87.
    [56] LIU B, YANG F, HUANG DS, CHOU KC. iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC[J]. Bioinformatics, 2018, 34(1): 33-40.
    [57] CASSIANO MHA, SILVA-ROCHA R. Benchmarking bacterial promoter prediction tools: potentialities and limitations[J]. mSystems, 2020, 5(4): e00439-20.
    [58] ALPER H, FISCHER C, NEVOIGT E, STEPHANOPOULOS G. Tuning genetic control through promoter engineering[J]. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(36): 12678-12683.
    [59] ZHAO M, YUAN ZQ, WU LT, ZHOU SH, DENG Y. Precise prediction of promoter strength based on a de novo synthetic promoter library coupled with machine learning[J]. ACS Synthetic Biology, 2022, 11(1): 92-102.
    [60] NAIR TM. Calliper randomization: an artificial neural network based analysis of E. coli ribosome binding sites[J]. Journal of Biomolecular Structure & Dynamics, 1997, 15(3): 611-617.
    [61] BISANT D, MAIZEL J. Identification of ribosome binding sites in Escherichia coli using neural network models[J]. Nucleic Acids Research, 1995, 23(9): 1632-1639.
    [62] FANG H, HUANG YF, RADHAKRISHNAN A, SIEPEL A, LYON GJ, SCHATZ MC. Scikit-ribo enables accurate estimation and robust modeling of translation dynamics at codon resolution[J]. Cell Systems, 2018, 6(2): 180-191.e4.
    [63] ZHANG MY, HOLOWKO MB, HAYMAN ZUMPE H, ONG CS. Machine learning guided batched design of a bacterial ribosome binding site[J]. ACS Synthetic Biology, 2022, 11(7): 2314-2326.
    [64] SPRENGART ML, FUCHS E, PORTER AG. The downstream box: an efficient and independent translation initiation signal in Escherichia coli[J]. EMBO Journal, 1996, 15(3): 665-674.
    [65] SALIS HM. The ribosome binding site calculator[J]. Methods in Enzymology, 2011, 498: 19-42.
    [66] FARASAT I, KUSHWAHA M, COLLENS J, EASTERBROOK M, GUIDO M, SALIS HM. Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria[J]. Molecular Systems Biology, 2014, 10(6): 731.
    [67] JERVIS AJ, CARBONELL P, VINAIXA M, DUNSTAN MS, HOLLYWOOD KA, ROBINSON CJ, RATTRAY NJW, YAN CY, SWAINSTON N, CURRIN A, SUNG R, TOOGOOD H, TAYLOR S, FAULON JL, BREITLING R, TAKANO E, SCRUTTON NS. Machine learning of designed translational control allows predictive pathway optimization in Escherichia coli[J]. ACS Synthetic Biology, 2019, 8(1): 127-136.
    [68] DING NN, YUAN ZQ, ZHANG XJ, CHEN J, ZHOU SH, DENG Y. Programmable cross-ribosome-binding sites to fine-tune the dynamic range of transcription factor-based biosensor[J]. Nucleic Acids Research, 2020, 48(18): 10602-10613.
    [69] ALLFANO P, RIVELLINI F, LIMAURO D, BRUNI CB, CARLOMAGNO MS. A consensus motif common to all rho-dependent prokaryotic transcription terminators[J]. Cell, 1991, 64(3): 553-563.
    [70] CIAMPI MS. Rho-dependent terminators and transcription termination[J]. Microbiology, 2006, 152(Pt 9): 2515-2528.
    [71] ROBERTS JW. Mechanisms of bacterial transcription termination[J]. Journal of Molecular Biology, 2019, 431(20): 4030-4039.
    [72] PETERS JM, VANGELOFF AD, LANDICK R. Bacterial transcription terminators: the RNA 3′-end Chronicles[J]. Journal of Molecular Biology, 2011, 412(5): 793-813.
    [73] LI J, MASON SW, GREENBLATT J. Elongation factor NusG interacts with termination factor rho to regulate termination and antitermination of transcription[J]. Genes & Development, 1993, 7(1): 161-172.
    [74] Di SALVO M, PUCCIO S, PEANO C, LACOUR S, ALIFANO P. RhoTermPredict: an algorithm for predicting Rho-dependent transcription terminators based on Escherichia coli, Bacillus subtilis and Salmonella enterica databases[J]. BMC Bioinformatics, 2019, 20(1): 117.
    [75] SALVO MD, PINATEL EM, BELLIS GD, TALÀ A, PEANO C, ALIFANO P. P&TIT: a computer tool for predicting prototypical transcription promoter and terminator elements by conserved motifs[C]//2017 International Conference on Bioinformatics and Biomedicine. October 26, 2017.
    [76] NAIR TM, TAMBE SS, KULKARNI BD. Application of artificial neural networks for prokaryotic transcription terminator prediction[J]. FEBS Letters, 1994, 346(2-3): 273-277.
    [77] PENNACCHIO LA, BICKMORE W, DEAN A, NOBREGA MA, BEJERANO G. Enhancers: five essential questions[J]. Nature Reviews Genetics, 2013, 14(4): 288-295.
    [78] RAY-JONES H, SPIVAKOV M. Transcriptional enhancers and their communication with gene promoters[J]. Cellular and Molecular Life Sciences, 2021, 78(19-20): 6453-6485.
    [79] ERNST J, KELLIS M. ChromHMM: automating chromatin-state discovery and characterization[J]. Nature Methods, 2012, 9(3): 215-216.
    [80] HOFFMAN MM, BUSKE OJ, WANG J, WENG ZP, BILMES JA, NOBLE WS. Unsupervised pattern discovery in human chromatin structure through genomic segmentation[J]. Nature Methods, 2012, 9(5): 473-476.
    [81] FIRPI HA, UCAR D, TAN K. Discover regulatory DNA elements using chromatin signatures and artificial neural network[J]. Bioinformatics, 2010, 26(13): 1579-1586.
    [82] FERNÁNDEZ M, MIRANDA-SAAVEDRA D. Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines[J]. Nucleic Acids Research, 2012, 40(10): e77.
    [83] RAJAGOPAL N, XIE W, LI Y, WAGNER U, WANG W, STAMATOYANNOPOULOS J, ERNST J, KELLIS M, REN B. RFECS: a random-forest based algorithm for enhancer identification from chromatin state[J]. PLoS Computational Biology, 2013, 9(3): e1002968.
    [84] ERWIN GD, OKSENBERG N, TRUTY RM, KOSTKA D, MURPHY KK, AHITUV N, POLLARD KS, CAPRA JA. Integrating diverse datasets improves developmental enhancer prediction[J]. PLoS Computational Biology, 2014, 10(6): e1003677.
    [85] MIR BA, REHMAN MU, TAYARA H, CHONG KT. Improving enhancer identification with a multi-classifier stacked ensemble model[J]. Journal of Molecular Biology, 2023, 435(23): 168314.
    [86] LIU YH, WANG ZX, YUAN H, ZHU GQ, ZHANG YQ. HEAP: a task adaptive-based explainable deep learning framework for enhancer activity prediction[J]. Briefings in Bioinformatics, 2023, 24(5): bbad286.
    [87] GARNER MM, REVZIN A. A gel electrophoresis method for quantifying the binding of proteins to specific DNA regions: application to components of the Escherichia coli lactose operon regulatory system[J]. Nucleic Acids Research, 1981, 9(13): 3047-3060.
    [88] VIERSTRA J, STAMATOYANNOPOULOS JA. Genomic footprinting[J]. Nature Methods, 2016, 13(3): 213-221.
    [89] DAS PM, RAMACHANDRAN K, VANWERT J, SINGAL R. Chromatin immunoprecipitation assay[J]. BioTechniques, 2004, 37(6): 961-969.
    [90] LUO KX, HARTEMINK AJ. Using DNase digestion data to accurately identify transcription factor binding sites[J]. Pacific Symposium on Biocomputing, 2013: 80-91.
    [91] SHERWOOD RI, HASHIMOTO T, O’DONNELL CW, LEWIS S, BARKAL AA, van HOFF JP, KARUN V, JAAKKOLA T, GIFFORD DK. Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape[J]. Nature Biotechnology, 2014, 32(2): 171-178.
    [92] PIQUE-REGI R, DEGNER JF, PAI AA, GAFFNEY DJ, GILAD Y, PRITCHARD JK. Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data[J]. Genome Research, 2011, 21(3): 447-455.
    [93] BARISSI S, SALA A, WIECZÓR M, BATTISTINI F, OROZCO M. DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors[J]. Nucleic Acids Research, 2022, 50(16): 9105-9114.
    [94] ZHOU TY, SHEN N, YANG L, ABE N, HORTON J, MANN RS, BUSSEMAKER HJ, GORDÂN R, ROHS R. Quantitative modeling of transcription factor binding specificities using DNA shape[J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(15): 4654-4659.
    [95] AVSEC Ž, WEILERT M, SHRIKUMAR A, KRUEGER S, ALEXANDARI A, DALAL K, FROPF R, McANANY C, GAGNEUR J, KUNDAJE A, ZEITLINGER J. Base-resolution models of transcription-factor binding reveal soft motif syntax[J]. Nature Genetics, 2021, 53(3): 354-366.
    [96] HAN K, SHEN LC, ZHU YH, XU J, SONG JN, YU DJ. MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network[J]. Briefings in Bioinformatics, 2022, 23(1): bbab445.
    [97] DENG L, WU H, LIU XJ, LIU H. DeepD2V: a novel deep learning-based framework for predicting transcription factor binding sites from combined DNA sequence[J]. International Journal of Molecular Sciences, 2021, 22(11): 5521.
    [98] PREMKUMAR KAR, BHARANIKUMAR R, PALANIAPPAN A. Riboflow: using deep learning to classify riboswitches with ~99% accuracy[J]. Frontiers in Bioengineering and Biotechnology, 2020, 8: 808.
    [99] ANGENENT-MARI NM, GARRUSS AS, SOENKSEN LR, CHURCH G, COLLINS JJ. A deep learning approach to programmable RNA switches[J]. Nature Communications, 2020, 11(1): 5057.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

王金盛,孙喆,张学礼. 生物制造中核酸元件的智能设计[J]. 生物工程学报, 2025, 41(3): 968-992

复制
分享
文章指标
  • 点击次数:40
  • 下载次数: 56
  • HTML阅读次数: 76
  • 引用次数: 0
历史
  • 收稿日期:2024-07-23
  • 最后修改日期:2025-02-06
  • 在线发布日期: 2025-03-29
  • 出版日期: 2025-03-25
文章二维码
您是第5996522位访问者
生物工程学报 ® 2025 版权所有

通信地址:中国科学院微生物研究所    邮编:100101

电话:010-64807509   E-mail:cjb@im.ac.cn

技术支持:北京勤云科技发展有限公司