000			04836nab a22003977a 4500
999			_c59647 _d59639
001			59647
003			MX-TxCIM
005			20240919020950.0
008			180731s2018||||xxk|||p|op||||00||0|eng|d
024	8		_ahttps://doi.org/10.1186/s13007-018-0314-7
040			_aMX-TxCIM
041			_aeng
100	1		_92702 _aMontesinos-Lopez, A.
245	1		_aBayesian functional regression as an alternative statistical analysis of high‑throughput phenotyping data of modern agriculture _h[Electronic Resource]
260			_aLondon : _bBioMed Central, _c2018.
500			_aOpen Access
500			_aPeer review
520			_aBackground: Modern agriculture uses hyperspectral cameras with hundreds of reflectance data at discrete narrow bands measured in several environments. Recently, Montesinos-López et al. (Plant Methods 13(4):1–23, 2017a. https ://doi.org/10.1186/s1300 7-016-0154-2; Plant Methods 13(62):1–29, 2017b. https ://doi.org/10.1186/s1300 7-017-0212- 4) proposed using functional regression analysis (as functional data analyses) to help reduce the dimensionality of the bands and thus decrease the computational cost. The purpose of this paper is to discuss the advantages and disadvantages that functional regression analysis offers when analyzing hyperspectral image data. We provide a brief review of functional regression analysis and examples that illustrate the methodology. We highlight critical elements of model specification: (i) type and number of basis functions, (ii) the degree of the polynomial, and (iii) the methods used to estimate regression coefficients. We also show how functional data analyses can be integrated into Bayesian models. Finally, we include an in-depth discussion of the challenges and opportunities presented by functional regression analysis. Results: We used seven model-methods, one with the conventional model (M1), three methods using the B-splines model (M2, M4, and M6) and three methods using the Fourier basis model (M3, M5, and M7). The data set we used comprises 976 wheat lines under irrigated environments with 250 wavelengths. Under a Bayesian Ridge Regression (BRR), we compared the prediction accuracy of the model-methods proposed under different numbers of basis functions, and compared the implementation time (in seconds) of the seven proposed model-methods for different numbers of basis. Our results as well as previously analyzed data (Montesinos-López et al. 2017a, 2017b) support that around 23 basis functions are enough. Concerning the degree of the polynomial in the context of B-splines, degree 3 approximates most of the curves very well. Two satisfactory types of basis are the Fourier basis for period curves and the B-splines model for non-periodic curves. Under nine different basis, the seven method-models showed similar prediction accuracy. Regarding implementation time, results show that the lower the number of basis, the lower the implementation time required. Methods M2, M3, M6 and M7 were around 3.4 times faster than methods M1, M4 and M5. Conclusions: In this study, we promote the use of functional regression modeling for analyzing high-throughput phenotypic data and indicate the advantages and disadvantages of its implementation. In addition, many key elements that are needed to understand and implement this statistical technique appropriately are provided using a real data set. We provide details for implementing Bayesian functional regression using the developed genomic functional regression (GFR) package. In summary, we believe this paper is a good guide for breeders and scientists interested in using functional regression models for implementing prediction models when their data are curves. Keywords: Hyperspectral data, Functional regression analysis, Bayesian functional regression, Functional data, Bayesian Ridge Regression.
546			_aText in English
591			_aCIMMYT Informa : 2019 (September 13, 2018)
650		7	_2AGROVOC _93634 _aPhenotypes
650		7	_2AGROVOC _96437 _aEconomic activities
650		7	_2AGROVOC _92624 _aStatistical methods
650		7	_2AGROVOC _95834 _aRegression analysis
650		7	_2AGROVOC _94013 _aBayesian theory
650		7	_94371 _aData analysis _2AGROVOC
700	1		_aMontesinos-Lopez, O.A. _92700
700	1		_aDe los Campos, G. _92349
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_9907 _aBurgueño, J. _gGenetic Resources Program _8INT3239
700	1		_97662 _aLuna-Vazquez, F.J.
773	0		_dBioMed Central, 2018 _gv. 14, art. 46 _tPlant Methods _w57210 _x1746-4811
787			_tCorrection to : bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture
856	4		_uhttps://hdl.handle.net/10883/19576 _yOpen Access through DSpace
942			_cSUM _2ddc _n0