Keloid nomogram prediction model based on weighted gene co-expression network analysis and machine learning / 生物医学工程学杂志

Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.

Major depressive disorder prediction using data science

Background@#Major depressive disorder is a mood disorder that has affected many people worldwide. It is characterized by persistently low or depressed mood, anhedonia or decreased interest in pleasurable activities, feelings of guilt or worthlessness, lack of energy, poor concentration, appetite changes, psychomotor retardation or agitation, sleep disturbances, or suicidal thoughts. @*Objective@#The objective of the study was to predict the presence of major depressive disorder using a variety of machine learning classification algorithms (logistic regression, Naive Bayes, support vector machine, random forest, adaptive boosting, and extreme gradient boosting) on a publicly available depression dataset. @*Methodology@#After data pre-processing, several experiments were performed to assess the recursive feature elimination with cross validation as a feature selection method and synthetic minority over-sampling technique to address dataset imbalance. Several machine learning algorithms were applied on an anonymized publicly available depression dataset. Feature importance of the top performing models were also generated. All simulation experiments were implemented via Python 3.8 and its machine learning libraries (Scikit-learn, Keras, Tensorflow, Pandas, Matplotlib, Seaborn, NumPy). @*Results@#The top performing model was obtained by logistic regression with excellent performance metrics (91% accuracy, 93% sensitivity, 85% specificity, 93% recall, 93% F1-score, and 0.78 Matthews correlation coefficient). Feature importance scores of the most relevant attribute were also generated for the best model. @*Conclusion@#The findings suggest the utility of data science techniques powered by machine learning models to make a diagnosis of major depressive disorders with acceptable results. The potential deployment of these machine learning models in clinical practice can further enhance the diagnostic acumen of health professionals. Using data analytics and machine learning, data scientists can have a better understanding of mental health illness contributing to prompt and improved diagnosis thereby leading to the institution of early intervention and medical treatments ensuring the best quality of care for our patients.

Application of multiple support vector machine recursive feature elimination model in cancer feature gene selection / 国际生物医学工程杂志

Objective To analyze the cancergene expression profile data using multi-support vector machine recursive feature elimination algorithm (MSVM-RFE) and calculate the genetic ranking score to obtain the optimal feature gene subset. Methods Gene expression profiles of bladder cancer, breast cancer, colon cancer and lung cancer were downloaded from GEO (Gene Expression Omnibus) database.The differentially expressed genes were obtained by differential expression analysis. The differential gene expressions were sequenced by MSVM-RFE algorithm and the average test errors of each gene subset were calculated. Then the optimal gene subsetsof four kinds of cancer were obtained according to the minimum average test errors. Based on the datasets of four kinds of cancer characteristic genes before and after screening, linear SVM classifiers were constructed and the classification efficiencies of the optimal feature gene subsets were verified. Results Using the optimal feature gene subsetobtained by MSVM-RFE algorithm, the classification accuracy was improved from (96.77±1.28)％to (99.85±0.46)％for the bladder cancer data, improved from (83.77±4.93)％to (88.30±3.85)％for the breast cancer data, and improved from (72.69±2.41)％to (90.21±3.31)％for the lung cancer data.Besides, theoptimal feature gene subsetkept the classification accuracy of colon cancer classifierat a high level (>99.5％). Conclusions The feature gene extraction based on MSVM-RFE algorithm can improve the classification efficiency of cancer.