EVs, such as exosomes, play an important role in intercellular communications. In the present study, we focused on evaluation of transcriptomics analysis of circulating EVs in ML systems to predict colorectal cancer and to classify cancer stage. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. The best accuracy in cancer stage classification is achieved with RF as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. Our aim is to develop models for prediction and classification of colon cancer stages. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Currently, it is being used widely in clinical research. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research.
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