XGBFEMF: An XGBoost-Based Framework for Essential Protein Prediction

Zhong, Jiancheng; Sun, Yusui; Peng, Wei; Xie, Minzhu; Yang, Jiahong; Tang, Xiwei

doi:10.1109/tnb.2018.2842219

Cited by 114 publications

(57 citation statements)

References 30 publications

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“…This would allow us to customize multispectral sensing and accelerate the sampling process to make the pipeline more efficient [26]. We show that the XGBoost algorithm is a better tool for modeling in our case ( Figure Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 11 July 2019 doi:10.20944/preprints201907.0158.v1 5), but the arguments need to be optimized due to the environmental variables to further improve the pipeline [27].…”

Section: Discussionmentioning

confidence: 91%

UAV-Based High-Throughput Approach for Fast Growing <em>Cunninghamia lanceolata (Lamb.)</em> Cultivar Screening by Machine Learning

Zou¹,

Liang²,

Wu³

et al. 2019

Preprint

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Accurate measurements of tree height and diameter at breast height (DBH) in forests to evaluate the growth rate of cultivars is still a significant challenge, even when using LiDAR and 3-D modeling. We propose an integrated pipeline methodology to measure the biomass of different tree cultivars in plantation forests with high crown density which that combines unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Using a planation of Cunninghamia lanceolate, commonly known as Chinese fir, in Fujian, China, images were collected using a hyperspectral camera and orthorectified in HiSpectral Stitcher. Vegetation indices and modeling were processed in Python using decision trees, random forests, support vector machine, and eXtreme Gradient Boosting (XGBoost) third-party libraries. Tree height and DBH of 2880 samples were measured manually and clustering into three groups: “fast growth,” “median,” growth and “normal” growth group, and 19 vegetation indices from 12,000 pixels were abstracted as the input of features for the modeling. After modeling and cross-validation, the classifier generated by random forests had the best prediction accuracy compare to other algorisms (75%). This framework can be applied to other tree species to make management and business decisions.

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Section: Discussionmentioning

confidence: 91%

UAV-Based High-Throughput Approach for Fast Growing <em>Cunninghamia lanceolata (Lamb.)</em> Cultivar Screening by Machine Learning

Zou¹,

Liang²,

Wu³

et al. 2019

Preprint

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“…It works well even on small datasets (where it outperforms deep learning approaches), it is robust to outliers and it is able to model complex interdependencies. For these reasons, it has been used by many researchers in various biomedical fields, e.g., [49][50][51], etc.…”

Section: Discussionmentioning

confidence: 99%

Changes in Phonation and Their Relations with Progress of Parkinson’s Disease

et al. 2018

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Hypokinetic dysarthria, which is associated with Parkinson's disease (PD), affects several speech dimensions, including phonation. Although the scientific community has dealt with a quantitative analysis of phonation in PD patients, a complex research revealing probable relations between phonatory features and progress of PD is missing. Therefore, the aim of this study is to explore these relations and model them mathematically to be able to estimate progress of PD during a two-year follow-up. We enrolled 51 PD patients who were assessed by three commonly used clinical scales. In addition, we quantified eight possible phonatory disorders in five vowels. To identify the relationship between baseline phonatory features and changes in clinical scores, we performed a partial correlation analysis. Finally, we trained XGBoost models to predict the changes in clinical scores during a two-year follow-up. For two years, the patients' voices became more aperiodic with increased microperturbations of frequency and amplitude. Next, the XGBoost models were able to predict changes in clinical scores with an error in range 11-26%. Although we identified some significant correlations between changes in phonatory features and clinical scores, they are less interpretable. This study suggests that it is possible to predict the progress of PD based on the acoustic analysis of phonation. Moreover, it recommends utilizing the sustained vowel /i/ instead of /a/.

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“…e use of apoptosis proteins is a key by which organisms maintain homeostasis. Normally, cells maintain a balance between increasing proliferation and apoptosis, but too much or too little apoptosis can lead to many diseases [3,4]. Cancer and AIDS are currently the most serious diseases threatening human health, and are linked with insufficient apoptosis and excessive apoptosis, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…e evolutionary information of protein sequence is obtained by analyzing sequence homology, and some published articles revealed that this could achieve better performance in protein prediction. Methods include the Position Specific Scoring Matrix (PSSM) [17], Psepssm [2], and Gene ontology (Go) [4]. (v) Fusion of multiple feature expression.…”

Section: Introductionmentioning

confidence: 99%

Predictions of Apoptosis Proteins by Integrating Different Features Based on Improving Pseudo-Position-Specific Scoring Matrix

Ruan

Zhou

Nie

et al. 2020

BioMed Research International

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Apoptosis proteins are strongly related to many diseases and play an indispensable role in maintaining the dynamic balance between cell death and division in vivo. Obtaining localization information on apoptosis proteins is necessary in understanding their function. To date, few researchers have focused on the problem of apoptosis data imbalance before classification, while this data imbalance is prone to misclassification. Therefore, in this work, we introduce a method to resolve this problem and to enhance prediction accuracy. Firstly, the features of the protein sequence are captured by combining Improving Pseudo-Position-Specific Scoring Matrix (IM-Psepssm) with the Bidirectional Correlation Coefficient (Bid-CC) algorithm from position-specific scoring matrix. Secondly, different features of fusion and resampling strategies are used to reduce the impact of imbalance on apoptosis protein datasets. Finally, the eigenvector adopts the Support Vector Machine (SVM) to the training classification model, and the prediction accuracy is evaluated by jackknife cross-validation tests. The experimental results indicate that, under the same feature vector, adopting resampling methods remarkably boosts many significant indicators in the unsampling method for predicting the localization of apoptosis proteins in the ZD98, ZW225, and CL317 databases. Additionally, we also present new user-friendly local software for readers to apply; the codes and software can be freely accessed at https://github.com/ruanxiaoli/Im-Psepssm.

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XGBFEMF: An XGBoost-Based Framework for Essential Protein Prediction

Cited by 114 publications

References 30 publications

UAV-Based High-Throughput Approach for Fast Growing <em>Cunninghamia lanceolata (Lamb.)</em> Cultivar Screening by Machine Learning

UAV-Based High-Throughput Approach for Fast Growing <em>Cunninghamia lanceolata (Lamb.)</em> Cultivar Screening by Machine Learning

Changes in Phonation and Their Relations with Progress of Parkinson’s Disease

Predictions of Apoptosis Proteins by Integrating Different Features Based on Improving Pseudo-Position-Specific Scoring Matrix

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