About this Research Topic
Recently, much of the field of cancer diagnosis has been focused on developing new computational methods. However, most of these methods suffer from lower accuracy, experimental noise, high dimensionality, and poor interpretability. These methods still require significant improvement, so that can meet the need of real-world clinical diagnosis.
Machine learning algorithms have pushed the boundaries for numerous problems in areas such as computer vision, natural language processing, and audio processing. Recent cancer research has also focused on machine learning, which has attracted attention from both the academic research and commercial application communities. In a different yet often closely related arena, evolutionary algorithms use a population-based approach in which more than one solution participates in an iteration and evolves a new population of solutions in each iteration. Meanwhile, evolutionary algorithms have successfully been employed to increase the performance of machine learning methods significantly.
With this perspective, this Research Topic will collect cutting-edge research in all aspects of evolutionary algorithm and machine learning for cancer diagnoses including experimental and theoretical research and real-world applications to promote research, sharing, and development.
We welcome all types of articles accepted within the Bioinformatics and Computational Biology specialty section (please see here). Potential topics include, but are not limited to the following:
• Deep learning for cancer diagnoses,
• Perspectives on evolutionary machine learning,
• Multiobjective cancer diagnoses,
• Mathematical modelling of cancer diagnoses,
• Conventional machine learning methods for cancer diagnoses
• Unsupervised cancer diagnoses
Machine learning algorithms have pushed the boundaries for numerous problems in areas such as computer vision, natural language processing, and audio processing. Recent cancer research has also focused on machine learning, which has attracted attention from both the academic research and commercial application communities. In a different yet often closely related arena, evolutionary algorithms use a population-based approach in which more than one solution participates in an iteration and evolves a new population of solutions in each iteration. Meanwhile, evolutionary algorithms have successfully been employed to increase the performance of machine learning methods significantly.
With this perspective, this Research Topic will collect cutting-edge research in all aspects of evolutionary algorithm and machine learning for cancer diagnoses including experimental and theoretical research and real-world applications to promote research, sharing, and development.
We welcome all types of articles accepted within the Bioinformatics and Computational Biology specialty section (please see here). Potential topics include, but are not limited to the following:
• Deep learning for cancer diagnoses,
• Perspectives on evolutionary machine learning,
• Multiobjective cancer diagnoses,
• Mathematical modelling of cancer diagnoses,
• Conventional machine learning methods for cancer diagnoses
• Unsupervised cancer diagnoses
Keywords: Cancer Diagnoses, Evolutionary Algorithm, Multiobjective Optimization, Evolutionary Deep Learning, Evolutionary Machine Learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
