CNVABNN: An AdaBoost algorithm and neural networks-based detection of copy number variations from NGS data

Wang, Xuan; Junqing, L; Huang, Tihao

doi:10.1016/j.compbiolchem.2022.107720

Cited by 2 publications

(1 citation statement)

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“…GBDT was developed by Friedman (2001) , and builds on each tree, learning the residual (negative gradient) of the sum of all previous tree conclusions ( Kriegler and Berk, 2010 ). AdaBoost is an algorithm for constructing strong classifiers as a linear combination of simple weak classifiers ( Freund and Schapire, 1997 ; Wang et al., 2022 ). ET is directly divided using random features and random thresholds on random features ( Geurts et al., 2006 ; Ahmad et al., 2018 ).…”

Section: Methodsmentioning

confidence: 99%

Predicting Fv/Fm and evaluating cotton drought tolerance using hyperspectral and 1D-CNN

et al. 2022

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The chlorophyll fluorescence parameter Fv/Fm is significant in abiotic plant stress. Current acquisition methods must deal with the dark adaptation of plants, which cannot achieve rapid, real-time, and high-throughput measurements. However, increased inputs on different genotypes based on hyperspectral model recognition verified its capabilities of handling large and variable samples. Fv/Fm is a drought tolerance index reflecting the best drought tolerant cotton genotype. Therefore, Fv/Fm hyperspectral prediction of different cotton varieties, and drought tolerance evaluation, are worth exploring. In this study, 80 cotton varieties were studied. The hyperspectral cotton data were obtained during the flowering, boll setting, and boll opening stages under normal and drought stress conditions. Next, One-dimensional convolutional neural networks (1D-CNN), Categorical Boosting (CatBoost), Light Gradient Boosting Machines (LightBGM), eXtreme Gradient Boosting (XGBoost), Decision Trees (DT), Random Forests (RF), Gradient elevation decision trees (GBDT), Adaptive Boosting (AdaBoost), Extra Trees (ET), and K-Nearest Neighbors (KNN) were modeled with Fv/Fm. The Savitzky-Golay + 1D-CNN model had the best robustness and accuracy (RMSE = 0.016, MAE = 0.009, MAPE = 0.011). In addition, the Fv/Fm prediction drought tolerance coefficient and the manually measured drought tolerance coefficient were similar. Therefore, cotton varieties with different drought tolerance degrees can be monitored using hyperspectral full band technology to establish a 1D-CNN model. This technique is non-destructive, fast and accurate in assessing the drought status of cotton, which promotes smart-scale agriculture.

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