Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach.

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients. We used multivariate Cox regression model with three hybrid penalties approach including least absolute shrinkage and selection operator (Lasso), adaptive lasso and elastic net algorithms for informative prognostic-related genes selection. Then, the best subset regression was used to identify the best prognostic gene signature. The prognostic gene-based risk score was constructed using the Cox coefficient of the prognostic gene signature. The model was evaluated by Kaplan-Meier (KM) and receiver operating characteristic curve (ROC) analyses. A novel four-gene signature associated with prognosis was identified and the risk score was constructed based on the four-gene signature. The risk score efficiently distinguished the patients into a high-risk group with poor prognosis. The time-dependent ROC analysis revealed that the risk model had a good performance with an area under the curve (AUC) of 0.780, 0.732, 0.733 in 1-, 2- and 3-year prognosis prediction in The Cancer Genome Atlas (TCGA) dataset. Moreover, the risk score revealed a high diagnostic performance to classify HCC from normal samples. The prognosis and diagnosis prediction performances of risk scores were verified in external validation datasets. Functional enrichment analysis of the four-gene signature and its co-expressed genes involved in the metabolic and cell cycle pathways was constructed. Overall, we developed a novel-gene-based prognostic model to predict high-risk HCC patients and we hope that our findings can provide promising insight to explore the role of the four-gene signature in HCC patients and aid risk classification.

Predictors of micronutrient deficiency among children aged 6-23 months in Ethiopia: a machine learning approach.

Introduction: Micronutrient (MN) deficiencies are a major public health problem in developing countries including Ethiopia, leading to childhood morbidity and mortality. Effective implementation of programs aimed at reducing MN deficiencies requires an understanding of the important drivers of suboptimal MN intake. Therefore, this study aimed to identify important predictors of MN deficiency among children aged 6-23 months in Ethiopia using machine learning algorithms. Methods: This study employed data from the 2019 Ethiopia Mini Demographic and Health Survey (2019 EMDHS) and included a sample of 1,455 children aged 6-23 months for analysis. Machine Learning (ML) methods including, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Neural Network (NN), and Naïve Bayes (NB) were used to prioritize risk factors for MN deficiency prediction. Performance metrics including accuracy, sensitivity, specificity, and Area Under the Receiver Operating Characteristic (AUROC) curves were used to evaluate model prediction performance. Results: The prediction performance of the RF model was the best performing ML model in predicting child MN deficiency, with an AUROC of 80.01% and accuracy of 72.41% in the test data. The RF algorithm identified the eastern region of Ethiopia, poorest wealth index, no maternal education, lack of media exposure, home delivery, and younger child age as the top prioritized risk factors in their order of importance for MN deficiency prediction. Conclusion: The RF algorithm outperformed other ML algorithms in predicting child MN deficiency in Ethiopia. Based on the findings of this study, improving women's education, increasing exposure to mass media, introducing MN-rich foods in early childhood, enhancing access to health services, and targeted intervention in the eastern region are strongly recommended to significantly reduce child MN deficiency.

A nine-gene signature identification and prognostic risk prediction for patients with lung adenocarcinoma using novel machine learning approach.

BACKGROUND: Lung adenocarcinoma (LUAD) is one the most prevalent cancer with high mortality and its risk stratification is limited due lack of reliable molecular biomarkers. Although several studies have been conducted to identify gene signature involved in LUAD progression, most currently used methods to select gene features did not fully consider the problem of the existence of strong pairwise gene correlations as it resulted inconsistency in gene election. Therefore, it is crucial to develop new strategy to identify reliable gene signatures that improve risk prediction. METHODS AND RESULTS: In this study, novel feature selection strategy (1) univariate Cox regression model to select survival associated genes (2) integrating rigid Cox regression with Adaptive Lasso model to identify informative survival associated genes (3) stepwise Cox regression model to identify optimal gene signature and (4) prognostic risk predictive model for LUAD (PRPML) was constructed. The PRPML was developed-based on four machine learning (ML) methods including logistic regression (LR), K-nearest neighbors (KNN), support vector machine with the radial kernel (SVMR), and average neural network (Avnet). The PRPML model successfully stratified high-risk and low-risk groups of patients with LUAD in three datasets. The PRPML achieved an area under the curve (AUC) of 0.812 and 0.863 in the validation datasets. Finally, a nine-potential gene signature was found and showed great potential for risk prediction. CONCLUSIONS: Our study demonstrates that the developed strategy identified a nine potential gene signature for accurate risk prediction performance and this signature could provide valuable clue into the understanding of the molecular mechanism of LUAD disease.