Research Topic

Breast Cancer Resistance, Biomarkers and Therapeutics Development in the Era of Artificial Intelligence

About this Research Topic

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


Keywords: Breast Cancer, Artificial Intelligence, Mutation, Resistance, Biomarkers discovery


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.

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


Keywords: Breast Cancer, Artificial Intelligence, Mutation, Resistance, Biomarkers discovery


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.

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Submission Deadlines

29 May 2021 Abstract
26 September 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

29 May 2021 Abstract
26 September 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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