About this Research Topic
Cancer continues to be one of the major causes of illness and death worldwide. Cancer is growing at a shocking speed and touches every geographic region of the world. It is predicted that by 2030 there will be 21.7 million new cases and 13 million deaths. To overcome this problem efficiently and to make significant progress in cancer research and therapy, both the scientific and healthcare sectors must work together. Recent advancements in the development of AI-based methods i.e. ensemble or stacking algorithms to discover novel biomarkers using gene expression and other data provide greater opportunity for complete data analysis to decipher the mechanism of colon cancer initiation, progression, and metastasis. In cancer therapy and precision medicine, drug discovery is critical. The surge of omics data over the previous decade has allowed for experimental and computational prediction of anti-cancer therapies and enhanced drug discovery.
The goal is therefore to both experimentally and computationally investigate the novel biomarkers in the development of colon cancer and drug resistance. Then, novel drug targets (biomarkers) may help to overcome the problem of drug resistance in cancer. The most advanced experimental and computational techniques, particularly using artificial intelligence and machine learning methods, can be implemented to predict the structural implications of mutations. This will be beneficial in understanding mechanisms of drug resistance and the discovery of novel biomarkers and drugs.
In this Research Topic, we aim to provide an overview of recent technologies in experimental and computational areas, such as artificial intelligence or machine learning approaches, relevant to the identification of novel biomarkers and drug testing in cancer diagnosis, management, and treatment. Original research articles, mini-reviews, and full-length review articles covering colon cancer are welcome. We encourage submissions covering, but not limited to, the following topics:
• Drug testing against colon cancer biomarkers using cutting-edge experimental technologies.
• Experimental and computational methods of discovery of novel biomarkers in colon cancer
• Artificial intelligence or machine learning approaches in colon cancer diagnosis
• Machine learning-based drug screening and discovery against cancer biomarkers
• Molecular dynamics simulation to understand different mechanisms in colon cancer
• Cancer resistance prediction and development of novel cancer therapy strategies
Disclaimer: All computational studies must be supported by experimental findings to be considered for this Research Topic collection.
The goal is therefore to both experimentally and computationally investigate the novel biomarkers in the development of colon cancer and drug resistance. Then, novel drug targets (biomarkers) may help to overcome the problem of drug resistance in cancer. The most advanced experimental and computational techniques, particularly using artificial intelligence and machine learning methods, can be implemented to predict the structural implications of mutations. This will be beneficial in understanding mechanisms of drug resistance and the discovery of novel biomarkers and drugs.
In this Research Topic, we aim to provide an overview of recent technologies in experimental and computational areas, such as artificial intelligence or machine learning approaches, relevant to the identification of novel biomarkers and drug testing in cancer diagnosis, management, and treatment. Original research articles, mini-reviews, and full-length review articles covering colon cancer are welcome. We encourage submissions covering, but not limited to, the following topics:
• Drug testing against colon cancer biomarkers using cutting-edge experimental technologies.
• Experimental and computational methods of discovery of novel biomarkers in colon cancer
• Artificial intelligence or machine learning approaches in colon cancer diagnosis
• Machine learning-based drug screening and discovery against cancer biomarkers
• Molecular dynamics simulation to understand different mechanisms in colon cancer
• Cancer resistance prediction and development of novel cancer therapy strategies
Disclaimer: All computational studies must be supported by experimental findings to be considered for this Research Topic collection.
Keywords: Colon Cancer, Diagnostics, Therapeutics, Machine-Learning, Computational, Experimental
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.
