Breast cancer is globally the most diagnosed form of cancer in females, and one of the major causes of death from cancer (~2.3 million cases according to Hyuna et., al (2021) Global cancer statistics, 2020). Breast cancer is a clinical condition with distinct molecular features and genetic profiles composed of various subtypes. Triple Negative Breast Cancers are one of the most aggressive breast cancers and, particularly in comparison to other Breast Cancers, have a higher 5-year death rate after treatment. Breast cancers in young females appear to be detected at more advanced stages and exhibit more aggressive biological features compared to tumors that arise in older patients. Additionally, various factors determine the emergence of drugs resistance in multifactorial diseases like breast cancer.
The ultimate objective is therefore to investigate the factors directly involved in the development of breast cancer drug resistance and to overcome this problem. Alternatively, novel drug targets (biomarkers) may help to overcome the problem of drug resistance in breast cancer. In silico studies. 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, such as artificial intelligence or machine learning approaches, relevant to breast cancer diagnosis, management, treatment, and the development of different biomarkers. Original Research articles, mini-reviews and full length review articles covering breast cancer are welcome. We encourage submissions covering, but not limited to, the following topics:
• Artificial intelligence or machine learning approaches in breast cancer diagnosis
• Discovery of novel biomarkers in breast cancer
• Machine learning based drug discovery
• Molecular dynamics simulation to understand different mechanisms in breast cancer
• Structural implications of drug resistance in Breast cancer
• Breast cancer resistance prediction
Breast cancer is globally the most diagnosed form of cancer in females, and one of the major causes of death from cancer (~2.3 million cases according to Hyuna et., al (2021) Global cancer statistics, 2020). Breast cancer is a clinical condition with distinct molecular features and genetic profiles composed of various subtypes. Triple Negative Breast Cancers are one of the most aggressive breast cancers and, particularly in comparison to other Breast Cancers, have a higher 5-year death rate after treatment. Breast cancers in young females appear to be detected at more advanced stages and exhibit more aggressive biological features compared to tumors that arise in older patients. Additionally, various factors determine the emergence of drugs resistance in multifactorial diseases like breast cancer.
The ultimate objective is therefore to investigate the factors directly involved in the development of breast cancer drug resistance and to overcome this problem. Alternatively, novel drug targets (biomarkers) may help to overcome the problem of drug resistance in breast cancer. In silico studies. 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, such as artificial intelligence or machine learning approaches, relevant to breast cancer diagnosis, management, treatment, and the development of different biomarkers. Original Research articles, mini-reviews and full length review articles covering breast cancer are welcome. We encourage submissions covering, but not limited to, the following topics:
• Artificial intelligence or machine learning approaches in breast cancer diagnosis
• Discovery of novel biomarkers in breast cancer
• Machine learning based drug discovery
• Molecular dynamics simulation to understand different mechanisms in breast cancer
• Structural implications of drug resistance in Breast cancer
• Breast cancer resistance prediction
