Abstract
Background: Cereal hull color is an important quality specification characteristic. Many studies were conducted to identify genetic changes underlying cereal hull color diversity. However, these studies mainly focused on the gene level. Recent studies have suggested that metabolomics can accurately reflect the integrated and real-time cell processes that contribute to the formation of different cereal colors.
Methods: In this study, we exploited published metabolomics databases and applied several advanced computational methods, such as minimum redundancy maximum relevance (mRMR), incremental forward search (IFS), random forest (RF) to investigate cereal hull color at the metabolic level. First, the mRMR was applied to analyze cereal hull samples represented by metabolite features, yielding a feature list. Then, the IFS and RF were used to test several feature sets, constructed according to the aforementioned feature list. Finally, the optimal feature sets and RF classifier were accessed based on the testing results.
Results and Conclusion: A total of 158 key metabolites were found to be useful in distinguishing white cereal hulls from colorful cereal hulls. A prediction model constructed with these metabolites and a random forest algorithm generated a high Matthews coefficient correlation value of 0.701. Furthermore, 24 of these metabolites were previously found to be relevant to cereal color. Our study can provide new insights into the molecular basis of cereal hull color formation.
Keywords: Cereal hull color, molecular metabolites, minimum redundancy maximum relevance, random forest, incremental forward search.
[1]
Abbo, S.; Lev-Yadun, S.; Heun, M.; Gopher, A. On the ‘lost’ crops of the neolithic Near East. J. Exp. Bot., 2013, 64(4), 815-822.
[2]
Wang, J.; Liu, L.; Ball, T.; Yu, L.; Li, Y.; Xing, F. Revealing a 5,000-y-old beer recipe in China. Proc. Natl. Acad. Sci. USA, 2016, 113(23), 6444-6448.
[3]
Worthington, M.; Reberg-Horton, C. Breeding cereal crops for enhanced weed suppression: Optimizing allelopathy and competitive ability. J. Chem. Ecol., 2013, 39(2), 213-231.
[4]
Julia, C.; Kesse-Guyot, E.; Ducrot, P.; Péneau, S.; Touvier, M.; Méjean, C.; Hercberg, S. Performance of a five category front-of-pack labelling system - the 5-colour nutrition label - to differentiate nutritional quality of breakfast cereals in France. BMC Public Health, 2015, 15, 179.
[5]
Gu, X.Y.; Foley, M.E.; Horvath, D.P.; Anderson, J.V.; Feng, J.; Zhang, L.; Mowry, C.R.; Ye, H.; Suttle, J.C.; Kadowaki, K.; Chen, Z. Association between seed dormancy and pericarp color is controlled by a pleiotropic gene that regulates abscisic acid and flavonoid synthesis in weedy red rice. Genetics, 2011, 189(4), 1515-1524.
[6]
Tan, B.C.; Guan, J.C.; Ding, S.; Wu, S.; Saunders, J.W.; Koch, K.E.; McCarty, D.R. Structure and origin of the White cap locus and its role in evolution of grain color in maize. Genetics, 2017, 206(1), 135-150.
[7]
Yagishita, Y.; Mihara, M.; Kohno, Y.; Shibata, M. Photochromic properties of 3-deoxyanthocyanidin pigments in nontoxic solvents. J. Food Sci., 2016, 81(12), E2950-E2955.
[8]
Min, B.; McClung, A.M.; Chen, M.H. Phytochemicals and antioxidant capacities in rice brans of different color. J. Food Sci., 2011, 76(1), C117-C126.
[9]
Maiti, D.; Das, D.K.; Karak, T.; Banerjee, M. Management of nitrogen through the use of leaf color chart (LCC) and soil plant analysis development (SPAD) or chlorophyll meter in rice under irrigated ecosystem. Sci. World J., 2004, 4, 838-846.
[10]
Kim, G.R.; Jung, E.S.; Lee, S.; Lim, S.H.; Ha, S.H.; Lee, C.H. Combined mass spectrometry-based metabolite profiling of different pigmented rice (Oryza sativa L.) seeds and correlation with antioxidant activities. Molecules, 2014, 19(10), 15673-15686.
[11]
Chen, W.; Wang, W.; Peng, M.; Gong, L.; Gao, Y.; Wan, J.; Wang, S.; Shi, L.; Zhou, B.; Li, Z.; Peng, X.; Yang, C.; Qu, L.; Liu, X.; Luo, J. Comparative and parallel genome-wide association studies for metabolic and agronomic traits in cereals. Nat. Commun., 2016, 7, 12767.
[12]
Gomez-Casati, D.F.; Zanor, M.I.; Busi, M.V. Metabolomics in plants and humans: Applications in the prevention and diagnosis of diseases. BioMed Res. Int., 2013, 2013, 792527.
[13]
Lodge, C.J.; Allen, K.J.; Lowe, A.J.; Dharmage, S.C. Overview of evidence in prevention and aetiology of food allergy: A review of systematic reviews. Int. J. Environ. Res. Public Health, 2013, 10(11), 5781-5806.
[14]
Windle, P.E. The systematic review process: An overview. J. Perianesth. Nurs., 2010, 25(1), 40-42.
[15]
Peng, H.; Long, F.; Ding, C. Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27(8), 1226-1238.
[16]
Chen, L.; Zhang, Y.H.; Lu, G.; Huang, T.; Cai, Y.D. Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways. Artif. Intell. Med., 2017, 76, 27-36.
[17]
Liu, L.; Chen, L.; Zhang, Y.H.; Wei, L.; Cheng, S.; Kong, X.; Zheng, M.; Huang, T.; Cai, Y.D. Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection. J. Biomol. Struct. Dyn., 2017, 35(2), 312-329.
[18]
Mohabatkar, H.; Beigi, M.M.; Abdolahi, K.; Mohsenzadeh, S. Prediction of allergenic proteins by means of the concept of Chou’s pseudo amino acid composition and a machine learning approach. Med. Chem., 2013, 9(1), 133-137.
[19]
Chen, L.; Wang, S.; Zhang, Y.H.; Wei, L.; Xu, X.; Huang, T.; Cai, Y.D. Prediction of nitrated tyrosine residues in protein sequences by extreme learning machine and feature selection methods. Comb. Chem. High Throughput Screen., 2018, 21(6), 393-402.
[20]
Ni, Q.; Chen, L. A feature and algorithm selection method for improving the prediction of protein structural classes. Comb. Chem. High Throughput Screen., 2017, 20(7), 612-621.
[21]
Li, Z.; Zhou, X.; Dai, Z.; Zou, X. Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm. BMC Bioinformatics, 2010, 11(1), 325.
[22]
Chen, L.; Zhang, Y.H.; Zheng, M.; Huang, T.; Cai, Y.D. Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds. Mol. Genet. Genomics, 2016, 291(6), 2065-2079.
[23]
Zhang, Y.; Ding, C.; Li, T. Gene selection algorithm by combining reliefF and mRMR. BMC Genomics, 2008, 9(Suppl. 2), S27.
[24]
Chen, L.; Zhang, Y.H.; Huang, T.; Cai, Y.D. Gene expression profiling gut microbiota in different races of humans. Sci. Rep., 2016, 6, 23075.
[25]
Cai, Z.; Xu, D.; Zhang, Q.; Zhang, J.; Ngai, S.M.; Shao, J. Classification of lung cancer using ensemble-based feature selection and machine learning methods. Mol. Biosyst., 2015, 11(3), 791-800.
[26]
He, L. An ensemble feature selection method Based on mRMR for paired microarray data. J. Comput. Inf. Syst., 2014, 10(11), 4875-4882.
[27]
Chen, L.; Pan, X.; Hu, X.; Zhang, Y.H.; Wang, S.; Huang, T.; Cai, Y.D. Gene expression differences among different MSI statuses in colorectal cancer. Int. J. Cancer, 2018, 143(7), 1731-1740.
[28]
Chen, L.; Zhang, Y.H.; Huang, G.; Pan, X.; Wang, S.; Huang, T.; Cai, Y.D. Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection. Mol. Genet. Genomics, 2018, 293(1), 137-149.
[30]
Chen, L. Identify key sequence features to improve CRISPR sgRNA efficacy. IEEE Access, 2017, 5, 26582-26590.
[31]
Wang, S. Analysis and prediction of nitrated tyrosine sites with mRMR method and support vector machine algorithm. Curr. Bioinform., 2017.
[32]
Li, B.Q.; Cai, Y.D.; Feng, K.Y.; Zhao, G.J. Prediction of protein cleavage site with feature selection by random forest. PLoS One, 2012, 7(9), e45854.
[33]
Chen, L.; Li, J.; Zhang, Y.H.; Feng, K.; Wang, S.; Zhang, Y.; Huang, T.; Kong, X.; Cai, Y.D. Identification of gene expression signatures across different types of neural stem cells with the Monte-Carlo feature selection method. J. Cell. Biochem., 2018, 119(4), 3394-3403.
[34]
Li, J.; Huang, T. Predicting and analyzing early wake-up associated gene expressions by integrating GWAS and eQTL studies. Biochim. Biophys. Acta Mol. Basis Dis., 2018, 1864(6 Pt B), 2241-2246.
[35]
Kohavi, R. A study of cross-validation and bootstrap for accuracy
estimation and model selection. in International joint Conference
on artificial intelligence,1995.Lawrence Erlbaum Associates Ltd
[36]
Chen, L.; Zeng, W.M.; Cai, Y.D.; Feng, K.Y.; Chou, K.C. Predicting anatomical therapeutic chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities. PLoS One, 2012, 7(4), e35254.
[38]
Ho, T.K. Random Decision Forests. Proceeding of the 3rd International Conference on Document Analysis and Recognition, 1995.Montreal, QC
[39]
Ting, K.M.; Witten, I.H. Stacking bagged and dagged models in Proceedings of the Fourteenth International Conference on Machine Learning, 1997, pp. 367-375.
[40]
Ho, T.K. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20(8), 832-844.
[41]
Witten, I.H.; Frank, E., Eds.; Data Mining:Practical Machine Learning Tools and Techniques. (2nd ed.); , 2005.
[42]
Cai, Y.; He, J.; Lu, L. Predicting sumoylation site by feature selection method. J. Biomol. Struct. Dyn., 2011, 28(5), 797-804.
[43]
Chen, L.; Feng, K.Y.; Cai, Y.D.; Chou, K.C.; Li, H.P. Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition. BMC Bioinformatics, 2010, 11, 293.
[44]
Chen, L. Identification of drug-drug interactions using chemical interactions. Curr. Bioinform., 2017, 12(6), 526-534.
[45]
Matthews, B.W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta, 1975, 405(2), 442-451.
[47]
Cover, T.; Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory, 1967, 13(1), 21-27.
[48]
Shoeva, O.Y.; Mock, H.P.; Kukoeva, T.V.; Börner, A.; Khlestkina, E.K. Regulation of the flavonoid biosynthesis pathway genes in purple and black grains of Hordeum vulgare. PLoS One, 2016, 11(10), e0163782.
[49]
Gerats, A.G.; Wallroth, M.; Donker-Koopman, W.; Groot, S.P.; Schram, A.W. The genetic control of the enzyme UDP-glucose: 3-0-flavonoïd-glucosyltransferase in flowers of Petunia hybrida. Theor. Appl. Genet., 1983, 65(4), 349-352.
[50]
Chen, W.; Gao, Y.; Xie, W.; Gong, L.; Lu, K.; Wang, W.; Li, Y.; Liu, X.; Zhang, H.; Dong, H.; Zhang, W.; Zhang, L.; Yu, S.; Wang, G.; Lian, X.; Luo, J. Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat. Genet., 2014, 46(7), 714-721.
[51]
Yang, Z.; Nakabayashi, R.; Okazaki, Y.; Mori, T.; Takamatsu, S.; Kitanaka, S.; Kikuchi, J.; Saito, K. Toward better annotation in plant metabolomics: Isolation and structure elucidation of 36 specialized metabolites from Oryza sativa (rice) by using MS/MS and NMR analyses. Metabolomics, 2014, 10(4), 543-555.
[52]
Kim, S.; Thiessen, P.A.; Cheng, T.; Yu, B.; Shoemaker, B.A.; Wang, J.; Bolton, E.E.; Wang, Y.; Bryant, S.H. Literature information in PubChem: Associations between PubChem records and scientific articles. J. Cheminform., 2016, 8, 32.
[53]
Kim, M.J.; Hyun, J.N.; Kim, J.A.; Park, J.C.; Kim, M.Y.; Kim, J.G.; Lee, S.J.; Chun, S.C.; Chung, I.M. Relationship between phenolic compounds, anthocyanins content and antioxidant activity in colored barley germplasm. J. Agric. Food Chem., 2007, 55(12), 4802-4809.
[54]
Xu, B.J.; Yuan, S.H.; Chang, S.K. Comparative analyses of phenolic composition, antioxidant capacity, and color of cool season legumes and other selected food legumes. J. Food Sci., 2007, 72(2), S167-S177.
[55]
Ojwang, L.O.; Awika, J.M. Stability of apigeninidin and its methoxylated derivatives in the presence of sulfites. J. Agric. Food Chem., 2010, 58(16), 9077-9082.
[56]
Panzella, L.; Eidenberger, T.; Napolitano, A.; d’Ischia, M. Black sesame pigment: DPPH assay-guided purification, antioxidant/antinitrosating properties, and identification of a degradative structural marker. J. Agric. Food Chem., 2012, 60(36), 8895-8901.
[57]
Pomar, F.; Merino, F.; Barceló, A.R. O-4-Linked coniferyl and sinapyl aldehydes in lignifying cell walls are the main targets of the Wiesner (phloroglucinol-HCl) reaction. Protoplasma, 2002, 220(1-2), 17-28.
[58]
Mantzouris, D.; Karapanagiotis, I.; Valianou, L.; Panayiotou, C. HPLC-DAD-MS analysis of dyes identified in textiles from Mount Athos. Anal. Bioanal. Chem., 2011, 399(9), 3065-3079.
[59]
Wang, L.; Albert, N.W.; Zhang, H.; Arathoon, S.; Boase, M.R.; Ngo, H.; Schwinn, K.E.; Davies, K.M.; Lewis, D.H. Temporal and spatial regulation of anthocyanin biosynthesis provide diverse flower colour intensities and patterning in Cymbidium orchid. Planta, 2014, 240(5), 983-1002.
[60]
Fu, X.; Cheng, S.; Liao, Y.; Huang, B.; Du, B.; Zeng, W.; Jiang, Y.; Duan, X.; Yang, Z. Comparative analysis of pigments in red and yellow banana fruit. Food Chem., 2018, 239, 1009-1018.
[61]
Pereira-Caro, G.; Watanabe, S.; Crozier, A.; Fujimura, T.; Yokota, T.; Ashihara, H. Phytochemical profile of a Japanese black-purple rice. Food Chem., 2013, 141(3), 2821-2827.
[62]
Abad-García, B.; Garmón-Lobato, S.; Berrueta, L.A.; Gallo, B.; Vicente, F. On line characterization of 58 phenolic compounds in Citrus fruit juices from Spanish cultivars by high-performance liquid chromatography with photodiode-array detection coupled to electrospray ionization triple quadrupole mass spectrometry. Talanta, 2012, 99, 213-224.
[63]
Jeong, Y.J.; An, C.H.; Woo, S.G.; Park, J.H.; Lee, K.W.; Lee, S.H.; Rim, Y.; Jeong, H.J.; Ryu, Y.B.; Kim, C.Y. Enhanced production of resveratrol derivatives in tobacco plants by improving the metabolic flux of intermediates in the phenylpropanoid pathway. Plant Mol. Biol., 2016, 92(1-2), 117-129.
[64]
Kim, C.K.; Cho, M.A.; Choi, Y.H.; Kim, J.A.; Kim, Y.H.; Kim, Y.K.; Park, S.H. Identification and characterization of seed-specific transcription factors regulating anthocyanin biosynthesis in black rice. J. Appl. Genet., 2011, 52(2), 161-169.
[65]
Kim, M.K.; Kim, H.A.; Koh, K.; Kim, H.S.; Lee, Y.S.; Kim, Y.H. Identification and quantification of anthocyanin pigments in colored rice. Nutr. Res. Pract., 2008, 2(1), 46-49.
[66]
Loypimai, P.; Moongngarm, A.; Chottanom, P. Thermal and pH degradation kinetics of anthocyanins in natural food colorant prepared from black rice bran. J. Food Sci. Technol., 2016, 53(1), 461-470.
[67]
Abdel-Aal, S.M.; Young, J.C.; Rabalski, I. Anthocyanin composition in black, blue, pink, purple, and red cereal grains. J. Agric. Food Chem., 2006, 54(13), 4696-4704.
[68]
Ruiz, A.; Mardones, C.; Vergara, C.; von Baer, D.; Gómez-Alonso, S.; Gómez, M.V.; Hermosín-Gutiérrez, I. Isolation and structural elucidation of anthocyanidin 3,7-β-O-diglucosides and caffeoyl-glucaric acids from calafate berries. J. Agric. Food Chem., 2014, 62(29), 6918-6925.
[69]
Turi, C.E.; Axwik, K.E.; Smith, A.; Jones, A.M.; Saxena, P.K.; Murch, S.J. Galanthamine, an anti-cholinesterase drug, effects plant growth and development in Artemisia tridentata Nutt. via modulation of auxin and neurotransmitter signaling. Plant Signal. Behav., 2014, 9(4), e28645.
[70]
Li, W.; Xu, L.; Wu, J.; Ma, L.; Liu, M.; Jiao, J.; Li, H.; Hu, F. Effects of Indole-3-Acetic Acid (IAA), a Plant Hormone, on the Ryegrass Yield and the Removal of Fluoranthene from Soil. Int. J. Phytoremediation, 2015, 17(1-6), 422-428.
[71]
Hu, F.; Wang, D.; Zhao, X.; Zhang, T.; Sun, H.; Zhu, L.; Zhang, F.; Li, L.; Li, Q.; Tao, D.; Fu, B.; Li, Z. Identification of rhizome-specific genes by genome-wide differential expression analysis in Oryza longistaminata. BMC Plant Biol., 2011, 11, 18.
[72]
Symons, G.M.; Chua, Y.J.; Ross, J.J.; Quittenden, L.J.; Davies, N.W.; Reid, J.B. Hormonal changes during non-climacteric ripening in strawberry. J. Exp. Bot., 2012, 63(13), 4741-4750.
[73]
Reig, C.; Mesejo, C.; Martínez-Fuentes, A.; Martínez-Alcántara, B.; Agustí, M. Loquat fruit ripening is associated with root depletion. Nutritional and hormonal control. J. Plant Physiol., 2015, 177, 51-59.
[74]
Su, L.; Diretto, G.; Purgatto, E.; Danoun, S.; Zouine, M.; Li, Z.; Roustan, J.P.; Bouzayen, M.; Giuliano, G.; Chervin, C. Carotenoid accumulation during tomato fruit ripening is modulated by the auxin-ethylene balance. BMC Plant Biol., 2015, 15, 114.
[75]
Frelin, O.; Huang, L.; Hasnain, G.; Jeffryes, J.G.; Ziemak, M.J.; Rocca, J.R.; Wang, B.; Rice, J.; Roje, S.; Yurgel, S.N.; Gregory, J.F., III; Edison, A.S.; Henry, C.S.; de Crécy-Lagard, V.; Hanson, A.D. A directed-overflow and damage-control N-glycosidase in riboflavin biosynthesis. Biochem. J., 2015, 466(1), 137-145.
[76]
Dansby, M.Y.; Bovell-Benjamin, A.C. Physical properties and sixth graders’ acceptance of an extruded ready-to-eat sweetpotato breakfast cereal. J. Food Sci., 2003, 68(8), 2607-2612.
[77]
Lorenz, K. Wild rice: the Indian’s staple and the white man’s delicacy. Crit. Rev. Food Sci. Nutr., 1981, 15(3), 281-319.
[78]
Burini, G.; Damiani, P.; Avellini, P. [Riboflavin content of
commercial samples of alimentary pastes (author’s transl)]. S TA
NU,1975, 5(5-6), 313-314. [Riboflavin content of commercial
samples of alimentary pastes (author's transl)].
[79]
Cupo, M.A.; Donaldson, W.E. Effects of lead and niacin on tryptophan and serotonin metabolism. Drug Nutr. Interact., 1988, 5(4), 297-308.
[80]
Kanjanaphachoat, P.; Wei, B.Y.; Lo, S.F.; Wang, I.W.; Wang, C.S.; Yu, S.M.; Yen, M.L.; Chiu, S.H.; Lai, C.C.; Chen, L.J. Serotonin accumulation in transgenic rice by over-expressing tryptophan decarboxylase results in a dark brown phenotype and stunted growth. Plant Mol. Biol., 2012, 78(6), 525-543.
[81]
Li, C.Y.; Leopold, A.L.; Sander, G.W.; Shanks, J.V.; Zhao, L.; Gibson, S.I. The ORCA2 transcription factor plays a key role in regulation of the terpenoid indole alkaloid pathway. BMC Plant Biol., 2013, 13, 155.
[82]
Menke, F.L.; Champion, A.; Kijne, J.W.; Memelink, J. A novel jasmonate- and elicitor-responsive element in the periwinkle secondary metabolite biosynthetic gene Str interacts with a jasmonate- and elicitor-inducible AP2-domain transcription factor, ORCA2. EMBO J., 1999, 18(16), 4455-4463.
[83]
Kage, U.; Yogendra, K.N.; Kushalappa, A.C. TaWRKY70 transcription factor in wheat QTL-2DL regulates downstream metabolite biosynthetic genes to resist Fusarium graminearum infection spread within spike. Sci. Rep., 2017, 7, 42596.
[84]
Tao, Z.; Kou, Y.; Liu, H.; Li, X.; Xiao, J.; Wang, S. OsWRKY45 alleles play different roles in abscisic acid signalling and salt stress tolerance but similar roles in drought and cold tolerance in rice. J. Exp. Bot., 2011, 62(14), 4863-4874.
[85]
Xiang, Y.; Liu, C.S.; Liu, Y.; Song, X.N.; Gu, X. [Effects of abscisic acid on chemical components content and color of Glycyrrhiza uralensis]. [Effects of abscisic acid on chemical components content and color of Glycyrrhiza uralensis]. Zhongguo Zhongyao Zazhi, 2015, 40(9), 1688-1692.
[86]
Xing, P.; Su, R.; Guo, F.; Wei, L. Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine. Sci. Rep., 2017, 7, 46757.
[87]
Wei, L.; Xing, P.; Su, R.; Shi, G.; Ma, Z.S.; Zou, Q. CPPred-RF: A Sequence-based Predictor for Identifying Cell-Penetrating Peptides and Their Uptake Efficiency. J. Proteome Res., 2017, 16(5), 2044-2053.
[88]
Su, R.; Zhang, C.; Pham, T.D.; Davey, R.; Bischof, L.; Vallotton, P.; Lovell, D.; Hope, S.; Schmoelzl, S.; Sun, C. Detection of tubule boundaries based on circular shortest path and polar-transformation of arbitrary shapes. J. Microsc., 2016, 264(2), 127-142.
[89]
Wei, L. Improved and promising identification of human MicroRNAs by incorporating a high-quality negative set. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2014, 11(1), 192-201.
[90]
Wei, L.; Zou, Q. Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition. Int. J. Mol. Sci., 2016, 17(12), 2118.
[91]
Wei, L.; Liao, M.; Gao, X.; Zou, Q. Enhanced Protein Fold Prediction Method Through a Novel Feature Extraction Technique. IEEE Trans. Nanobioscience, 2015, 14(6), 649-659.
[92]
Leyi, Wei. Minghong Liao; Xing Gao; Quan Zou, An improved protein structural classes prediction method by incorporating both sequence and structure information. IEEE Trans. Nanobioscience, 2015, 14(4), 339-349.
[93]
Wei, L.; Tang, J.; Zou, Q. Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information. Inf. Sci., 2017, 384, 135-144.
[94]
Wei, L. Fast prediction of protein methylation sites using a
sequence-based feature selection technique IEEE/ACM Trans
Comput Biol Bioinform,2017.
[95]
Wei, L.; Xing, P.; Tang, J.; Zou, Q. PhosPred-RF: a novel sequence-based predictor for phosphorylation sites using sequential information only. IEEE Trans. Nanobioscience, 2017, 16(4), 240-247.
Related Journals
Current Computer-Aided Drug Design
Article Metrics
33
6
1