About this Research Topic
To mitigate the barriers to cancer detection, recent research has concentrated on the development of advanced computational methods for the early and effective detection of cancer. These approaches are aimed at the development of automated diagnostic tools for cancer detection. These automated tools yielded a better understanding of cancer diseases, new hypotheses, and predictive models. Moreover, these tools guided oncologists toward the next rounds of more in-depth and successful experimental works. The automated computational methods have shown good performance in cancer stage classification, cancer subtype identification, and prognostic prediction. Although, these methods provide a deeper exploration of cancer. However, there are some limitations of these methods such as a low rate of generalization during independent testing and lack of large-scale cancer datasets that are required for deep learning-based computational approaches.
This Research Topic will focus on major trends and challenges in theoretical, and applied aspects of computational methods and their novel applications in effective cancer detection. Topics of this interest include but are not limited to:
- Application of advanced statistical methods for cancer detection
- Machine learning-based computational methods for cancer detection
- Synthetic Data Generation for Cancer Detection
- Novel computational methods for feature selection and their application in cancer detection
- Novel computational methods for feature extraction and application in cancer detection
- Big data and cancer detection
- Deep learning-based computational methods for cancer detection
- Knowledge discovery in cancer data
- Computational methods for cancer drug discovery
- Data collection and analysis approaches for cancer diseases
- Prediction of breast, lung, colon and rectum, prostate, skin and stomach cancers
Keywords: computational methods, cancer detection, machine learning, big data, knowledge discovery
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