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Prostate cancer is widely known to be one of the most common cancers among men around the world. Due to its high heterogeneity, many of the studies carried out to identify the molecular level causes for cancer have only been partially successful. Among the techniques used in cancer studies, gene expression profiling is seen to be one of the most po...
Citations
Prostate cancer is a major world health problem for men. This shows how important early detection and accurate diagnosis are for better treatment and patient outcomes. This study compares different ways to find Prostate Cancer (PCa) and label tumors as normal or abnormal, with the goal of speeding up current work in microarray gene data analysis. The study looks at how well several feature extraction methods work with three feature selection strategies: Harmonic Search (HS), Firefly Algorithm (FA), and Elephant Herding Optimization (EHO). The techniques tested are Expectation Maximization (EM), Nonlinear Regression (NLR), K-means, Principal Component Analysis (PCA), and Discrete Cosine Transform (DCT). Eight classifiers are used for the task of classification. These are Random Forest, Decision Tree, Adaboost, XGBoost, and Support Vector Machine (SVM) with linear, polynomial, and radial basis function kernels. This study looks at how well these classifiers work with and without feature selection methods. It finds that the SVM with radial basis function kernel, using DCT for feature extraction and EHO for feature selection, does the best of all of them, with an accuracy of 94.8 % and an error rate of 5.15 %.
Survival analysis is a critical task in glioma patient management due to the inter and intra tumor heterogeneity. In clinical practice, clinicians estimate the survival with their experience, which can be biased and optimistic. Over the past decades, diverse survival analysis approaches were proposed incorporating distinct data such as imaging and genetic information. The remarkable advancements in imaging and high throughput omics and sequencing technologies have enabled the acquisition of this information of glioma patients eficiently, providing novel insights for survival estimation in the present day. Besides, in the past years, machine learning techniques and deep learning have emerged into the field of survival analysis of glioma patients trading off the traditional statistical analysis-based survival analysis approaches. In this survey paper, we explore the prognostic parameters acquired, utilizing diagnostic imaging techniques and genomic platforms for survival or risk estimation of glioma patients. Further, we review the techniques, learning and statistical analysis algorithms, along with their benefits and limitations used for prognosis prediction. Consequently, we highlight the challenges of the existing state-of-the-art survival prediction studies and propose future directions in the field of research.
Gliomas are lethal type of central nervous system tumors with a poor prognosis. Recently, with the advancements in the micro-array technologies thousands of gene expression related data of glioma patients are acquired, leading for salient analysis in many aspects. Thus, genomics are been emerged into the field of prognosis analysis. In this work, we identify survival related 7 gene signature and explore two approaches for survival prediction and risk estimation. For survival prediction, we propose a novel probabilistic programming based approach, which outperforms the existing traditional machine learning algorithms. An average 4 fold accuracy of 74% is obtained with the proposed algorithm. Further, we construct a prognostic risk model for risk estimation of glioma patients. This model reflects the survival of glioma patients, with high risk for low survival patients.