Quantitative Structure Activity Relationship study of the Anti-Hepatitis Peptides employing Random Forest and Extra Tree regressors

Mishra, Gunjan; Sehgal, Deepak; Valadi, Jayaraman

doi:10.6026/97320630013060

Cited by 29 publications

(17 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the strength of the selection of attributes and the average output noise strength are determined by k and n min respectively. ese two parameters improve the precision and reduce overfitting in the ETR model [40,41]. Figure 4 shows the structure of the ETR.…”

Section: Extra Tree Regressionmentioning

confidence: 99%

“…Twelve statistical parameters have been used to assess the accuracy performance of different AI models used in this study for predicting the C d . e statistical parameters include different matrices to assess the prediction error and other parameters used for evaluating the agreement between actual and predicted values of C d (i) Mean absolute error (MAE) is used as an indicator of how similar the estimated values to the observed ones, and it is given by[41,42]…”

mentioning

confidence: 99%

See 1 more Smart Citation

An Extra Tree Regression Model for Discharge Coefficient Prediction: Novel, Practical Applications in the Hydraulic Sector and Future Research Directions

Hameed

AlOmar

Khaleel

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Despite modern advances used to estimate the discharge coefficient ( C d ), it is still a major challenge for hydraulic engineers to accurately determine C d for side weirs. In this study, extra tree regression (ETR) was used to predict the C d of rectangular sharp-crested side weirs depending on hydraulic and geometrical parameters. The prediction capacity of the ETR model was validated with two predictive models, namely, extreme learning machine (ELM) and random forest (RF). The quantitative assessment revealed that the ETR model achieved the highest accuracy in the predictions compared to other applied models, and also, it exhibited excellent agreement between measured and predicted C d (correlation coefficient is 0.9603). Moreover, the ETR achieved 6.73% and 22.96% higher prediction accuracy in terms of root mean square error in comparison to ELM and RF, respectively. Furthermore, the performed sensitivity analysis shows that the geometrical parameter such as b/B has the most influence on C d . Overall, the proposed model (ETR) is found to be a suitable, practical, and qualified computer-aid technology for C d modeling that may contribute to enhance the basic knowledge of hydraulic considerations.

show abstract

Section: Extra Tree Regressionmentioning

confidence: 99%

mentioning

confidence: 99%

An Extra Tree Regression Model for Discharge Coefficient Prediction: Novel, Practical Applications in the Hydraulic Sector and Future Research Directions

Hameed

AlOmar

Khaleel

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Models with a score higher than RMSE = 2 were discarded. Models were selected on the basis of [34,40] and considering [43][44][45]. The novel prediction model is the extra-tree regressor, an efficient technique over random forest models, as demonstrated in [43].…”

Section: Feature Engineeringmentioning

confidence: 99%

“…Models were selected on the basis of [34,40] and considering [43][44][45]. The novel prediction model is the extra-tree regressor, an efficient technique over random forest models, as demonstrated in [43]. This model considers a metaestimator, fitting several randomized decision trees (called extra trees) on several subsamples of the dataset.…”

Section: Feature Engineeringmentioning

confidence: 99%

Comparison of Climate Reanalysis and Remote-Sensing Data for Predicting Olive Phenology through Machine-Learning Methods

et al. 2021

View full text Add to dashboard Cite

Machine-learning algorithms used for modelling olive-tree phenology generally and largely rely on temperature data. In this study, we developed a prediction model on the basis of climate data and geophysical information. Remote measurements of weather conditions, terrain slope, and surface spectral reflectance were considered for this purpose. The accuracy of the temperature data worsened when replacing weather-station measurements with remote-sensing records, though the addition of more complete environmental data resulted in an efficient prediction model of olive-tree phenology. Filtering and embedded feature-selection techniques were employed to analyze the impact of variables on olive-tree phenology prediction, facilitating the inclusion of measurable information in decision support frameworks for the sustainable management of olive-tree systems.

show abstract

“…(IV) RandomForestRegressor (36): in the integrated model of the random forest, the samples of each tree are constructed from the training set after the put-back sampling. In addition, the selected segmentation point is not the best segmentation point for all features, but the best segmentation point in a random subset of features (37).…”

Section: Feature Score Calculationmentioning

confidence: 99%

Early prediction of acute xerostomia during radiation therapy for nasopharyngeal cancer based on delta radiomics from CT images

Liu¹,

Shi²,

Huang³

et al. 2019

Quant. Imaging Med. Surg.

View full text Add to dashboard Cite

Background: Acute xerostomia is the most common side effect of radiation therapy (RT) for head and neck (H&N) malignancies. Investigating radiation-induced changes of computed tomography (CT) radiomics in parotid glands (PGs) and saliva amount (SA) can predict acute xerostomia during the RT for nasopharyngeal cancer (NPC).Methods: CT and SA data from 35 patients with stages I-IVB were randomly collected from an NPC clinical trial registered on the clinicaltrials.gov (ID: NCT01762514). All patients received radical treatment based on intensity-modulated RT (IMRT) with a prescription dose of 68.1 Gy in 30 fractions. The patients' ages ranged 24-72 years, and each patient had five CT sets acquired at treatment position: at the 0 th , 10 th , 20 th , 30 th fractions during the RT, and at 3-month later after the RT. The PGs for each CT set were delineated by a radiation oncologist and verified independently by another. Patients' saliva was collected every other 10 days during the RT. Acute xerostomia was evaluated based on the RTOG acute toxicity scoring and the SA. In total, 1,703 radiomics features were calculated for PGs from each CT set, including feature value at 0 th fraction (FV 0F ), FV 10F , and delta FV (ΔFV 10F-0F ), respectively. Extensive experiments were conducted to achieve the optimal results. RidgeCV and Recursive Feature Elimination (RFE) were used for feature selection, while linear regression was used for predicting SA 30F . Four more patients were added for independent testing.Results: Substantial changes in various radiomics metrics of PGs were observed during the RT. Eight normalized feature value (NFV), selected from NFV 0F , predicted SA 10F with a mean square error (MSE) of 0.9042 and a R 2 score of 0.7406. Fourteen NFV, selected from ΔNFV 10F-0F , NFV 0F , and NFV 10F to predict SA 30F , showed the best predictive ability with an MSE of 0.0569. The model predicted the level of acute xerostomia with a precision of 0.9220 and a sensitivity of 100%, compared to the clinical observed SA. For the independent test, the MSE of PSA 30F was 0.0233. Conclusions:This study demonstrated that radiation-induced acute xerostomia level could be early predicted based on the SA and radiomics changes of the PGs during IMRT delivery. SA, NFV 0F , NFV 10F , and especially ΔNFV 10F-0F provided the best performance on acute xerostomia prediction for individual patient based on RidgeCV_RFE_LinearRegression method of delta radiomics.

show abstract

Quantitative Structure Activity Relationship study of the Anti-Hepatitis Peptides employing Random Forest and Extra Tree regressors

Cited by 29 publications

References 11 publications

An Extra Tree Regression Model for Discharge Coefficient Prediction: Novel, Practical Applications in the Hydraulic Sector and Future Research Directions

An Extra Tree Regression Model for Discharge Coefficient Prediction: Novel, Practical Applications in the Hydraulic Sector and Future Research Directions

Comparison of Climate Reanalysis and Remote-Sensing Data for Predicting Olive Phenology through Machine-Learning Methods

Early prediction of acute xerostomia during radiation therapy for nasopharyngeal cancer based on delta radiomics from CT images

Contact Info

Product

Resources

About