Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow P-M, Zietz M, Hoffman MM, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15:20170387.
Article
PubMed
PubMed Central
Google Scholar
Xu C, Jackson SA. Machine learning and complex biological data. Genome Biol. 2019;20:76.
Article
PubMed
PubMed Central
Google Scholar
Zitnik M, Nguyen F, Wang B, Leskovec J, Goldenberg A, Hoffman MM. Machine learning for integrating data in biology and medicine: principles, practice, and opportunities. Information Fusion. 2019;50:71–91.
Article
PubMed
Google Scholar
Altman N, Krzywinski M. The curse(s) of dimensionality. Nat Methods. 2018;15:399–400.
Article
CAS
PubMed
Google Scholar
Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15:233–4.
Article
CAS
PubMed
PubMed Central
Google Scholar
Webb S. Deep learning for biology. Nature. 2018;554:555–8.
Article
CAS
PubMed
Google Scholar
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44.
Article
CAS
Google Scholar
Singh A, Ganapathysubramanian B, Singh AK, Sarkar S. Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci. 2016;21:110–24.
Article
CAS
PubMed
Google Scholar
Tarca AL, Carey VJ, Chen X, Romero R, Drăghici S. Machine learning and Its applications to biology. PLoS Comput Biol. 2007;3:e116.
Article
PubMed
PubMed Central
Google Scholar
Prasad V, Gupta SD. Applications and potentials of artificial neural networks in plant tissue culture. In: Plant tissue culture engineering. Springer; 2008. p. 47–67
Weston J, Leslie C, Ie E, Zhou D, Elisseeff A, Noble WS. Semi-supervised protein classification using cluster kernels. Bioinformatics. 2005;21:3241–7.
Article
CAS
PubMed
Google Scholar
Tang B, Pan Z, Yin K, Khateeb A. Recent advances of deep learning in bioinformatics and computational biology. Front Genet. 2019;10:214.
Article
PubMed
PubMed Central
Google Scholar
Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, et al. Human-level control through deep reinforcement learning. Nature. 2015;518:529–33.
Article
CAS
PubMed
Google Scholar
Krenker A, Bešter J, Kos A. Introduction to the artificial neural networks. Artificial neural networks: methodological advances and biomedical applications. InTech 2011. pp. 1–18.
Osama K, Mishra BN, Somvanshi P: Machine learning techniques in plant biology. In PlantOmics: The omics of plant science. Springer; 2015. Pp. 731–54.
Yao X. Evolving artificial neural networks. Proc IEEE. 1999;87:1423–47.
Article
Google Scholar
Yang ZR. A novel radial basis function neural network for discriminant analysis. IEEE Trans Neural Netw. 2006;17:604–12.
Article
PubMed
Google Scholar
Taner A, Öztekin YB, Duran H. Performance analysis of deep learning CNN models for variety classification in hazelnut. Sustainability. 2021;13:6527.
Article
Google Scholar
Hassan SM, Maji AK, Jasiński M, Leonowicz Z, Jasińska E. Identification of plant-leaf diseases using CNN and transfer-learning approach. Electronics. 2021;10:1388.
Article
Google Scholar
Smith LN. A disciplined approach to neural network hyper-parameters: Part 1--learning rate, batch size, momentum, and weight decay. arXiv preprint arXiv:1803.09820. 2018.
Sarle WS. Stopped training and other remedies for overfitting. Comput Sci Stat. 1996;66:352–60.
Google Scholar
Van der Aalst WM, Rubin V, Verbeek H, van Dongen BF, Kindler E, Günther CW. Process mining: a two-step approach to balance between underfitting and overfitting. Softw Syst Model. 2010;9:87–111.
Article
Google Scholar
Murakoshi K. Avoiding overfitting in multilayer perceptrons with feeling-of-knowing using self-organizing maps. BioSystems. 2005;80:37–40.
Article
PubMed
Google Scholar
Rubio L, Galipienso L, Ferriol I. Detection of plant viruses and disease management: relevance of genetic diversity and evolution. Front Plant Sci. 2020;11:1092.
Article
PubMed
PubMed Central
Google Scholar
Varma A, Singh MK. Chapter 6—Diagnosis of plant virus diseases. In: Awasthi LP, editor. Applied plant virology. Academic Press; 2020. p. 79–92.
Chapter
Google Scholar
Bhattacharyya D, Gnanasekaran P, Kumar RK, Kushwaha NK, Sharma VK, Yusuf MA, Chakraborty S. A geminivirus betasatellite damages the structural and functional integrity of chloroplasts leading to symptom formation and inhibition of photosynthesis. J Exp Bot. 2015;66:5881–95.
Article
CAS
PubMed
PubMed Central
Google Scholar
Pallas V, García JA. How do plant viruses induce disease? Interactions and interference with host components. J Gen Virol. 2011;92:2691–705.
Article
CAS
PubMed
Google Scholar
Landgrebe DA. Signal theory methods in multispectral remote sensing. Wiley; 2003.
Book
Google Scholar
Lowe A, Harrison N, French AP. Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods. 2017;13:80.
Article
PubMed
PubMed Central
Google Scholar
Kawasaki Y, Uga H, Kagiwada S, Iyatomi H: Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. In: International symposium on visual computing. Springer; 2015. pp. 638–45.
Ramcharan A, Baranowski K, McCloskey P, Ahmed B, Legg J, Hughes DP. Deep learning for image-based cassava disease detection. Front Plant Sci. 1852;2017:8.
Google Scholar
Wang D, Vinson R, Holmes M, Seibel G, Bechar A, Nof S, Tao Y. Early detection of tomato spotted wilt virus by hyperspectral imaging and outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). Sci Rep. 2019;9:1–14.
Google Scholar
Kadam KU. Identification of groundnut bud necrosis virus on tomato fruits using machine learning based segmentation algorithm. Int J Fut Gener Commun Netw. 2020;13:259–64.
Google Scholar
Zhu H, Chu B, Zhang C, Liu F, Jiang L, He Y. Hyperspectral imaging for presymptomatic detection of tobacco disease with successive projections algorithm and machine-learning classifiers. Sci Rep. 2017;7:1–12.
Google Scholar
Gu Q, Sheng L, Zhang T, Lu Y, Zhang Z, Zheng K, Hu H, Zhou H. Early detection of tomato spotted wilt virus infection in tobacco using the hyperspectral imaging technique and machine learning algorithms. Comput Electron Agric. 2019;167:105066.
Article
Google Scholar
Griffel L, Delparte D, Edwards J. Using support vector machines classification to differentiate spectral signatures of potato plants infected with Potato Virus Y. Comput Electron Agric. 2018;153:318–24.
Article
Google Scholar
Joshi RC, Kaushik M, Dutta MK, Srivastava A, Choudhary N. VirLeafNet: Automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant. Ecol Inf. 2021;61:101197.
Article
Google Scholar
Chakraborty S, Kodamana H, Chakraborty S. Deep learning aided automatic and reliable detection of tomato begomovirus infections in plants. J Plant Biochem Biotechnol. 2021;66:1–8.
Google Scholar
Gao Z, Luo Z, Zhang W, Lv Z, Xu YJA. Deep learning application in plant stress imaging: a review. AgriEngineering. 2020;2:430–46.
Article
Google Scholar
Singh AK, Ganapathysubramanian B, Sarkar S, Singh A. Deep Learning for plant stress phenotyping: trends and future perspectives. Trends Plant Sci. 2018;23:883–98.
Article
CAS
PubMed
Google Scholar
Chen Y-M, Zu X-P, Li D. Identification of proteins of Tobacco mosaic virus by using a method of feature extraction. Front Genet. 2020;11:1186.
Article
Google Scholar
Mahlein A-K. Plant disease detection by imaging sensors—parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 2016;100:241–51.
Article
PubMed
Google Scholar
Ferentinos KP. Deep learning models for plant disease detection and diagnosis. Comput Electron Agric. 2018;145:311–8.
Article
Google Scholar
Roossinck MJ. Plant virus metagenomics: biodiversity and ecology. Annu Rev Genet. 2012;46:359–69.
Article
CAS
PubMed
Google Scholar
Stobbe AH, Roossinck MJ. Plant virus metagenomics: what we know and why we need to know more. Front Plant Sci. 2014;5:150.
Article
PubMed
PubMed Central
Google Scholar
Li J, Zhang S, Li B, Hu Y, Kang X-P, Wu X-Y, Huang M-T, Li Y-C, Zhao Z-P, Qin C-F. Machine learning methods for predicting human-adaptive influenza A viruses based on viral nucleotide compositions. Mol Biol Evol. 2020;37:1224–36.
Article
CAS
PubMed
Google Scholar
Randhawa GS, Soltysiak MP, El Roz H, de Souza CP, Hill KA, Kari L. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PLoS ONE. 2020;15:e0232391.
Article
CAS
PubMed
PubMed Central
Google Scholar
Posada-Céspedes S, Seifert D, Topolsky I, Jablonski KP, Metzner KJ, Beerenwinkel N. V-pipe: a computational pipeline for assessing viral genetic diversity from high-throughput data. Bioinformatics. 2021;6:66.
Google Scholar
Elena SF, Agudelo-Romero P, Carrasco P, Codoñer FM, Martín S, Torres-Barceló C, Sanjuán R. Experimental evolution of plant RNA viruses. Heredity. 2008;100:478–83.
Article
CAS
PubMed
Google Scholar
Salama MA, Hassanien AE, Mostafa A. The prediction of virus mutation using neural networks and rough set techniques. EURASIP J Bioinf Syst Biol. 2016;2016:1–11.
Article
Google Scholar
Kumar RV, Singh AK, Singh AK, Yadav T, Basu S, Kushwaha N, Chattopadhyay B, Chakraborty S. Complexity of begomovirus and betasatellite populations associated with chilli leaf curl disease in India. J Gen Virol. 2015;96:3143–58.
Article
CAS
PubMed
Google Scholar
Devendran R, Kumar M, Ghosh D, Yogindran S, Karim MJ, Chakraborty S. Capsicum-infecting begomoviruses as global pathogens: host-virus interplay, pathogenesis, and management. Trends Microbiol. 2021;6:66.
Google Scholar
Silva JCF, Carvalho TFM, Fontes EPB, Cerqueira FR. Fangorn Forest (F2): a machine learning approach to classify genes and genera in the family Geminiviridae. BMC Bioinformatics. 2017;18:431.
Article
PubMed
PubMed Central
Google Scholar
Gorzynski JE, Goenka SD, Shafin K, Jensen TD, Fisk DG, Grove ME, Spiteri E, Pesout T, Monlong J, Baid G, et al. Ultrarapid nanopore genome sequencing in a critical care setting. New Engl J Med. 2022;6:66.
Google Scholar
Mandadi KK. Scholthof K-BG: Plant immune responses against viruses: How does a virus cause disease? Plant Cell. 2013;25:1489–505.
Article
CAS
PubMed
PubMed Central
Google Scholar
Calil IP, Fontes EPB. Plant immunity against viruses: antiviral immune receptors in focus. Ann Bot. 2017;119:711–23.
CAS
PubMed
Google Scholar
Wu X, Valli A, García JA, Zhou X, Cheng X. The tug-of-war between plants and viruses: great progress and many remaining questions. Viruses. 2019;66:11.
Google Scholar
Kourelis J, van der Hoorn RAL. Defended to the Nines: 25 years of resistance gene cloning identifies nine mechanisms for R protein function. Plant Cell. 2018;30:285–99.
Article
CAS
PubMed
PubMed Central
Google Scholar
Kushwaha SK, Chauhan P, Hedlund K, Ahrén D. NBSPred: a support vector machine-based high-throughput pipeline for plant resistance protein NBSLRR prediction. Bioinformatics. 2015;32:1223–5.
Article
PubMed
Google Scholar
Tang D, Wang G, Zhou JM. Receptor kinases in plant-pathogen interactions: more than pattern recognition. Plant Cell. 2017;29:618–37.
Article
CAS
PubMed
PubMed Central
Google Scholar
Macho AP, Lozano-Duran R. Molecular dialogues between viruses and receptor-like kinases in plants. Mol Plant Pathol. 2019;20:1191–5.
Article
PubMed
PubMed Central
Google Scholar
Brustolini OJ, Silva JC, Sakamoto T, Fontes EP. Bioinformatics analysis of the receptor-like kinase (RLK) superfamily. Methods Mol Biol. 2017;1578:123–32.
Article
CAS
PubMed
Google Scholar
Liu D, Zhao Q, Cheng Y, Li D, Jiang C, Cheng L, Wang Y, Yang A. Transcriptome analysis of two cultivars of tobacco in response to Cucumber mosaic virus infection. Sci Rep. 2019;9:3124.
Article
PubMed
PubMed Central
Google Scholar
Liu Y, Liu Y, Spetz C, Li L, Wang X. Comparative transcriptome analysis in Triticum aestivum infecting wheat dwarf virus reveals the effects of viral infection on phytohormone and photosynthesis metabolism pathways. Phytopathol Res. 2020;2:3.
Article
Google Scholar
Rajamäki M-L, Sikorskaite-Gudziuniene S, Sarmah N, Varjosalo M, Valkonen JPT. Nuclear proteome of virus-infected and healthy potato leaves. BMC Plant Biol. 2020;20:355.
Article
PubMed
PubMed Central
Google Scholar
Sade D, Shriki O, Cuadros-Inostroza A, Tohge T, Semel Y, Haviv Y, Willmitzer L, Fernie AR, Czosnek H, Brotman Y. Comparative metabolomics and transcriptomics of plant response to Tomato yellow leaf curl virus infection in resistant and susceptible tomato cultivars. Metabolomics. 2015;11:81–97.
Article
CAS
Google Scholar
Mochida K, Koda S, Inoue K, Nishii R. Statistical and machine learning approaches to predict gene regulatory networks from transcriptome datasets. Front Plant Sci. 2018;9:1770.
Article
PubMed
PubMed Central
Google Scholar
Rodriguez-Peña R, Mounadi KE, Garcia-Ruiz H. Changes in subcellular localization of host proteins induced by plant viruses. Viruses. 2021;13:677.
Article
PubMed
PubMed Central
Google Scholar
Zhang N, Rao RSP, Salvato F, Havelund JF, Møller IM, Thelen JJ, Xu D. MU-LOC: a machine-learning method for predicting mitochondrially localized proteins in plants. Front Plant Sci. 2018;9:634.
Article
PubMed
PubMed Central
Google Scholar
Sperschneider J, Catanzariti A-M, DeBoer K, Petre B, Gardiner DM, Singh KB, Dodds PN, Taylor JM. LOCALIZER: subcellular localization prediction of both plant and effector proteins in the plant cell. Sci Rep. 2017;7:44598.
Article
PubMed
PubMed Central
Google Scholar
Sperschneider J, Gardiner DM, Dodds PN, Tini F, Covarelli L, Singh KB, Manners JM, Taylor JM. EffectorP: predicting fungal effector proteins from secretomes using machine learning. New Phytol. 2016;210:743–61.
Article
CAS
PubMed
Google Scholar
Ghosh D. M M, Chakraborty S: Impact of viral silencing suppressors on plant viral synergism: a global agro-economic concern. Appl Microbiol Biotechnol. 2021;105:6301–13.
Article
CAS
PubMed
Google Scholar
Jagga Z, Gupta D. Supervised learning classification models for prediction of plant virus encoded RNA silencing suppressors. PLoS ONE. 2014;9:e97446.
Article
PubMed
PubMed Central
Google Scholar
Nath A, Subbiah K. Probing an optimal class distribution for enhancing prediction and feature characterization of plant virus-encoded RNA-silencing suppressors. 3 Biotech. 2016;6:93.
Article
PubMed
PubMed Central
Google Scholar
Zhang B, Li W, Zhang J, Wang L, Wu J. Roles of small RNAs in virus-plant interactions. Viruses. 2019;11:827.
Article
PubMed Central
Google Scholar
Zhang B-T, Nam J-W. Supervised learning methods for microRNA studies. In: Machine learning in bioinformatics. 2008. pp 339–365.
Zhang X-M, Liang L, Liu L, Tang M-J. Graph neural networks and their current applications in bioinformatics. Front Genet. 2021;12:690049.
Article
CAS
PubMed
PubMed Central
Google Scholar
Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583–9.
Article
CAS
PubMed
PubMed Central
Google Scholar
Batra R, Chan H, Kamath G, Ramprasad R, Cherukara MJ, Sankaranarayanan SK. Screening of therapeutic agents for COVID-19 using machine learning and ensemble docking studies. J Phys Chem Lett. 2020;11:7058–65.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ma C, Zhang HH, Wang X. Machine learning for big data analytics in plants. Trends Plant Sci. 2014;19:798–808.
Article
CAS
PubMed
Google Scholar
Ren J, Ahlgren NA, Lu YY, Fuhrman JA, Sun F. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome. 2017;5:69.
Article
PubMed
PubMed Central
Google Scholar
Poland J, Rutkoski J. Advances and challenges in genomic selection for disease resistance. Annu Rev Phytopathol. 2016;54:79–98.
Article
CAS
PubMed
Google Scholar
Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de los Campos G, Burgueño J, González-Camacho JM, Pérez-Elizalde S, Beyene Y, et al: Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci. 2017;22:961–75.