Background. Psoriasis is a chronic autoimmune disease impairing significantly the quality of life of the patient. The diagnosis of the disease is done via a visual inspection of the lesional skin by dermatologists. Classification of psoriasis using gene expression is an important issue for the early and effective treatment of the disease. Therefore, gene expression data and selection of suitable gene signatures are effective sources of information. Methods. We aimed to develop a hybrid classifier for the diagnosis of psoriasis based on two machine learning models of the genetic algorithm and support vector machine (SVM). The method also conducts gene signature selection. A publically available gene expression dataset was used to test the model. Results. A number of 181 probe sets were selected among the original 54,675 probes using the hybrid model with a prediction accuracy of 100% over the test set. A number of 10 hub genes were identified using the protein-protein interaction network. Nine out of 10 identified genes were found in significant modules. Conclusions. The results showed that the genetic algorithm improved the SVM classifier performance significantly implying the ability of the proposed model in terms of detecting relevant gene expression signatures as the best features.

Overtreatment of prostate cancer (PCa) remains the pervasive problem in PCa management due to the highly variable outcomes of the disease and the lack of accurate clinical tools for patient stratification. Many gene expression signatures have been developed to improve the prognosis of PCa and some of them have already been used in clinical practice, however, no comprehensive evaluation was performed to compare the performances of the signatures. In this study, we conducted a systematic and unbiased evaluation of 15 machine learning algorithms and 30 published PCa gene expression-based prognostic signatures leveraging 10 transcriptomics datasets with 1,754 primary PCa patients from public data repositories. The results revealed that survival analysis models always outperformed binary classification models for risk assessment, and the performances of the survival analysis methods - Cox model regularized with ridge penalty (Cox-Ridge) and partial least squares regression for Cox model (Cox-PLS) - were generally more robust than the other methods. Based on the Cox-Ridge algorithm, some top prognostic signatures that performed equally well or even better than the commercial panels have been identified. The findings from the study can facilitate the identification of existing prognostic signatures that are promising for further validations in prospective studies before the clinical use and the selection of the optimal approaches for the development of new prognostic models. Moreover, the study provided a valuable resource with 10 transcriptomics datasets from large primary PCa cohorts and a comprehensive collection of 30 published gene expression-based signatures that can be used to develop, validate, and evaluate new signatures for PCa prognosis.