High-throughput sequencing (NGS) and other molecular screening methods as well as recent single-cell sequencing techniques provide massive information, which is biologically heterogeneous and noisy. The underlying cellular and organismal changes during oncogenesis require sophisticated computational biology methods able to explore the pathways that give rise to cancer.
The applications of artificial intelligence (AI) in translational cancer research have been increasing rapidly in recent years, generating high expectations for dealing with omics data, predicting therapy response, and improving treatment but have also underscored some challenges.
This Research Topic covers a wide spectrum of studies of machine learning-assisted omics studies that contribute to the research filed of diagnosis and treatment in solid tumor. We welcome research from laboratories, pre-clinical studies, and clinical trials. Topics of interest include, but are not limited to, the following:
1. Using multi-omics and machine learning techniques to identify novel biomarkers that are associated with solid tumor susceptibility, diagnosis, and therapeutics.
2. Developing up-to-date R or Python packages for dealing with omics data.
3. Exploring tumor microenvironment biology (e.g., the dual functions of tumor-associated macrophage, the cell-cell networks and cell-microbe interactions) using omics data and cutting-edge machine learning techniques.
4. Developing novel computational biology methods and designing advanced workflow for CAR-T and related therapies in solid tumor.
High-throughput sequencing (NGS) and other molecular screening methods as well as recent single-cell sequencing techniques provide massive information, which is biologically heterogeneous and noisy. The underlying cellular and organismal changes during oncogenesis require sophisticated computational biology methods able to explore the pathways that give rise to cancer.
The applications of artificial intelligence (AI) in translational cancer research have been increasing rapidly in recent years, generating high expectations for dealing with omics data, predicting therapy response, and improving treatment but have also underscored some challenges.
This Research Topic covers a wide spectrum of studies of machine learning-assisted omics studies that contribute to the research filed of diagnosis and treatment in solid tumor. We welcome research from laboratories, pre-clinical studies, and clinical trials. Topics of interest include, but are not limited to, the following:
1. Using multi-omics and machine learning techniques to identify novel biomarkers that are associated with solid tumor susceptibility, diagnosis, and therapeutics.
2. Developing up-to-date R or Python packages for dealing with omics data.
3. Exploring tumor microenvironment biology (e.g., the dual functions of tumor-associated macrophage, the cell-cell networks and cell-microbe interactions) using omics data and cutting-edge machine learning techniques.
4. Developing novel computational biology methods and designing advanced workflow for CAR-T and related therapies in solid tumor.
