CBRL and CBRC: Novel Algorithms for Improving Missing Value Imputation Accuracy Based on Bayesian Ridge Regression

Mostafa, Samih M.; Eladimy, Abdelrahman S.; Hamad, Safwat; Amano, Hirofumi

doi:10.3390/sym12101594

Cited by 22 publications

(12 citation statements)

References 26 publications

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“…This method is only used for static data imputation at a single time point. • Bayesian Bridge Regression (Bayesian) [32]: is a regression model with an additional regularization parameter for the coefficients, where the prior for the coefficient is given by a spherical Gaussian. This method adopts the same training scheme as our method for temporal imputation and forecasting shown in Fig.…”

Section: B Comparative Methodsmentioning

confidence: 99%

Dynamic Graph Neural Network Learning for Temporal Omics Data Prediction

2022

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High-throughput studies of biological systems are rapidly generating a wealth of 'omics'-scale data. Many of these studies are temporal collecting proteomics and genomics data capturing dynamic observations. While temporal omics data are essential to unravel the mechanisms of various diseases, they often include missing (or incomplete) values due to technical and experimental reasons. Data prediction methods, i.e., imputation and forecasting, have been widely used to mitigate these issues. However, existing imputation and forecasting techniques typically address static omics data representing a single time point and perform forecasting on data with complete values. In this paper, we propose a graph-based method for temporal omics data imputation and forecasting that handle omics data containing missing values at multiple time points. The method takes advantage of topological relationships (e.g., protein-protein and gene-gene interactions) among omics data samples and incorporates a graph convolutional network to first infer the missing values at different time points. Then, we combine these inferred values with the original omics data to perform temporal imputation and forecasting using a long short-term memory network. Evaluating the proposed method on two real-world datasets demonstrated a distinct advantage over existing data imputation and forecasting methods. On the omics dataset, the average mean square error of our method improved 11.3% for imputation and 6.4% for forecasting compared to the baseline methods.INDEX TERMS Temporal biological data, missing values, data prediction, graph neural networks

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Section: B Comparative Methodsmentioning

confidence: 99%

Dynamic Graph Neural Network Learning for Temporal Omics Data Prediction

2022

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“…In each generated dataset, MVs are imputed using single imputation techniques. The final imputed dataset is the average analysis of the m imputed datasets [13,14]. ML algorithms can also be used to predict MVs depending on using the available information within the given dataset.…”

Section: Handling Missing Datamentioning

confidence: 99%

“…( 10), gives an indication of the prediction's goodness of fit to the true values. From a statistical perspective, the R 2 score has been dubbed as the coefficient of determination [14].…”

Section: Mae and Rmsementioning

confidence: 99%

Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

Algarni¹,

Ragab²,

Alamri³

et al. 2022

Computer Systems Science and Engineering

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The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset's values. In most research studies, the existence of missing values (MVs) is a vital problem. In addition, any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high. In this paper, the authors propose a novel algorithm for dealing with MVs depending on the feature selection (FS) of similarity classifier with fuzzy entropy measure. The proposed algorithm imputes MVs in cumulative order. The candidate feature to be manipulated is selected using similarity classifier with Parkash's fuzzy entropy measure. The predictive model to predict MVs within the candidate feature is the Bayesian Ridge Regression (BRR) technique. Furthermore, any imputed features will be incorporated within the BRR equation to impute the MVs in the next chosen incomplete feature. The proposed algorithm was compared against some practical state-of-the-art imputation methods by conducting an experiment on four medical datasets which were gathered from several databases repository with MVs generated from the three missingness mechanisms. The evaluation metrics of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R 2 score) were used to measure the performance. The results exhibited that performance vary depending on the size of the dataset, amount of MVs and the missingness mechanism type. Moreover, compared to other methods, the results showed that the proposed method gives better accuracy and less error in most cases.

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“…González-Vidal et al [108] proposed a missing data imputation framework with Bayesian maximum entropy (BME) to estimate the missing data from the internet of things applications. Mostafa et al [109] introduced two algorithms the cumulative bayesian ridge with less NaN (CBRL) and cumulative bayesian ridge with high correlation (CBRC) for improving the accuracy of missing value imputation.…”

Section: Background and Related Workmentioning

confidence: 99%

A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis

et al. 2021

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Real medical datasets usually consist of missing data with different patterns which decrease the performance of classifiers used in intelligent healthcare and disease diagnosis systems. Many methods have been proposed to impute missing data, however, they do not fulfill the need for data quality especially in real datasets with different missing data patterns. In this paper, a four-layer model is introduced, and then a hybrid imputation (HIMP) method using this model is proposed to impute multi-pattern missing data including non-random, random, and completely random patterns. In HIMP, first, non-random missing data patterns are imputed, and then the obtained dataset is decomposed into two datasets containing random and completely random missing data patterns. Then, concerning the missing data patterns in each dataset, different single or multiple imputation methods are used. Finally, the best-imputed datasets gained from random and completely random patterns are merged to form the final dataset. The experimental evaluation was conducted by a real dataset named IRDia including all three missing data patterns. The proposed method and comparative methods were compared using different classifiers in terms of accuracy, precision, recall, and F1-score. The classifiers’ performances show that the HIMP can impute multi-pattern missing values more effectively than other comparative methods.

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CBRL and CBRC: Novel Algorithms for Improving Missing Value Imputation Accuracy Based on Bayesian Ridge Regression

Cited by 22 publications

References 26 publications

Dynamic Graph Neural Network Learning for Temporal Omics Data Prediction

Dynamic Graph Neural Network Learning for Temporal Omics Data Prediction

Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis

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