About this Research Topic
Bioinformatics has revolutionized cancer research by providing a comprehensive understanding of the intricate molecular landscapes underlying cancer development and progression. Clinical prediction models, integrating diverse omics data, have emerged as crucial tools for improving prognostication, making treatment selection, and predicting patient outcomes. In the realm of cancer, the integration of genomics, proteomics, metabolomics, and other omics data into clinical prediction models holds immense potential for enhancing precision medicine.
The overarching goal of this Research Topic is to advance the field of cancer research by focusing on the application of bioinformatics analyses, spanning genomics, proteomics, metabolomics, etc., to construct innovative clinical prediction models. The primary challenge addressed is the need for more accurate, personalized prediction models that can guide clinical decision-making for cancer patients. By leveraging the wealth of biological information provided by various omics technologies, we aim to enhance the predictive power of models, ultimately contributing to more effective and tailored cancer care strategies.
Contributors are invited to explore the following themes within the context of applying bioinformatics for clinical prediction models in cancer:
1. Genomics-driven predictive modeling:
• Exploration of genomic alterations as predictive biomarkers.
• Integrating genomics data into comprehensive prediction models.
2. Proteomics-based predictions:
• Identification of protein signatures influencing cancer prognosis.
• Development of proteomics-centered models for clinical predictions.
3. Metabolomics for treatment response predictions:
• Profiling metabolomic alterations to predict treatment responses.
• Incorporating metabolomics data into predictive modeling.
4. Multi-Omics Integration for enhanced predictions:
• Strategies for integrating diverse omics data in predictive models.
• Development of multi-omics prediction models for comprehensive patient stratification.
This Research Topic seeks to facilitate interdisciplinary collaboration and provide a platform for disseminating cutting-edge research that propels the development and application of bioinformatics-driven clinical prediction models in the field of cancer research.
Authors are encouraged to submit original research articles, reviews, and methodological papers that contribute to the advancement of clinical prediction models in cancer through bioinformatics. Manuscripts should reflect innovative approaches and methodologies, ensuring the translation of data-driven insights into clinically relevant predictions.
The overarching goal of this Research Topic is to advance the field of cancer research by focusing on the application of bioinformatics analyses, spanning genomics, proteomics, metabolomics, etc., to construct innovative clinical prediction models. The primary challenge addressed is the need for more accurate, personalized prediction models that can guide clinical decision-making for cancer patients. By leveraging the wealth of biological information provided by various omics technologies, we aim to enhance the predictive power of models, ultimately contributing to more effective and tailored cancer care strategies.
Contributors are invited to explore the following themes within the context of applying bioinformatics for clinical prediction models in cancer:
1. Genomics-driven predictive modeling:
• Exploration of genomic alterations as predictive biomarkers.
• Integrating genomics data into comprehensive prediction models.
2. Proteomics-based predictions:
• Identification of protein signatures influencing cancer prognosis.
• Development of proteomics-centered models for clinical predictions.
3. Metabolomics for treatment response predictions:
• Profiling metabolomic alterations to predict treatment responses.
• Incorporating metabolomics data into predictive modeling.
4. Multi-Omics Integration for enhanced predictions:
• Strategies for integrating diverse omics data in predictive models.
• Development of multi-omics prediction models for comprehensive patient stratification.
This Research Topic seeks to facilitate interdisciplinary collaboration and provide a platform for disseminating cutting-edge research that propels the development and application of bioinformatics-driven clinical prediction models in the field of cancer research.
Authors are encouraged to submit original research articles, reviews, and methodological papers that contribute to the advancement of clinical prediction models in cancer through bioinformatics. Manuscripts should reflect innovative approaches and methodologies, ensuring the translation of data-driven insights into clinically relevant predictions.
Keywords: clinical prediction models, genomics, proteomics, metabolomics, multi-omics integration, bioinformatics
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.
