About this Research Topic
The technological advances associated with the development of "omic" technologies, clinical big data and the fast development of targeted therapies & immunotherapies comes along with a need for personalised approaches. Such optimisations can be achieved through the application of comprehensive models based on intelligent data analytics, such as machine learning. A growing number of examples exist on how these tools can be used to improve patient prognostication, predict response to different treatments and accelerate the identification of new drugs or drug targets.
Machine learning has been applied to predict survival in different clinical scenarios with encouraging results. The implementation of machine learning-based survival models is increasingly popular in order to provide patient-centered risk information that can assist both the clinician and the patient. A growing number of research lines are focused on the application of machine learning to genomic data and electronic health records in order to derive new predictors to anticipate important clinical end-points. Additionally, new machine learning algorithms are becoming increasingly popular, such as reinforcement learning, and their application will be useful to optimise treatment strategies in the long term. Finally, the emergence and broader implementation of new technologies (e.g., single-cell sequencing) makes it necessary to develop new tools in order to comprehensively inform doctors about the clinical implications of their findings.
Hematological cancer research is a promising area for artificial intelligence applications, as the global burden of clinical and genomic research has provided vast amounts of available data. Machine learning tools can integrate the complex clinical, imaging (histology, radiology etc.) and molecular landscape of cancer patients in order to provide better risk stratification, optimal treatments and risk of adverse events. Overall, the application of machine learning to hematological cancer is enabling more personalized approaches that will facilitate the development of novel therapeutic strategies for different groups of patients.
Basic, translational and clinical research manuscripts are welcome. Original research papers as well as reviews that add value to our previous knowledge or evaluate new horizons can be submitted.
The following are specific themes to be addressed:
1. Big data and machine learning models for improved risk stratification in hematological cancer, using both simple clinical data, NGS results and integrative strategies.
2. Big data and machine learning models for single drug or drug combination prioritization in hematological cancer.
3. Big data and machine learning models to predict the risk of clinical complications in hematological cancer.
4. Big data and machine learning models to uncover new biological disease subgroups. A special interest will be paid to applications based on NGS and single-cell genomics data.
5. Application of novel artificial intelligence tools (e.g., reinforcement learning) to optimise treatment routes in the long term.
6. Computer vision tools for improved diagnosis and prognostication in hematological cancer (both in the field of radiology and pathology).
7. Big data and machine learning tools for application in clinical trials and real world data (RWD).
8. Artificial intelligence tools to design new drugs and to find new drug targets/neoantigens.
Machine learning has been applied to predict survival in different clinical scenarios with encouraging results. The implementation of machine learning-based survival models is increasingly popular in order to provide patient-centered risk information that can assist both the clinician and the patient. A growing number of research lines are focused on the application of machine learning to genomic data and electronic health records in order to derive new predictors to anticipate important clinical end-points. Additionally, new machine learning algorithms are becoming increasingly popular, such as reinforcement learning, and their application will be useful to optimise treatment strategies in the long term. Finally, the emergence and broader implementation of new technologies (e.g., single-cell sequencing) makes it necessary to develop new tools in order to comprehensively inform doctors about the clinical implications of their findings.
Hematological cancer research is a promising area for artificial intelligence applications, as the global burden of clinical and genomic research has provided vast amounts of available data. Machine learning tools can integrate the complex clinical, imaging (histology, radiology etc.) and molecular landscape of cancer patients in order to provide better risk stratification, optimal treatments and risk of adverse events. Overall, the application of machine learning to hematological cancer is enabling more personalized approaches that will facilitate the development of novel therapeutic strategies for different groups of patients.
Basic, translational and clinical research manuscripts are welcome. Original research papers as well as reviews that add value to our previous knowledge or evaluate new horizons can be submitted.
The following are specific themes to be addressed:
1. Big data and machine learning models for improved risk stratification in hematological cancer, using both simple clinical data, NGS results and integrative strategies.
2. Big data and machine learning models for single drug or drug combination prioritization in hematological cancer.
3. Big data and machine learning models to predict the risk of clinical complications in hematological cancer.
4. Big data and machine learning models to uncover new biological disease subgroups. A special interest will be paid to applications based on NGS and single-cell genomics data.
5. Application of novel artificial intelligence tools (e.g., reinforcement learning) to optimise treatment routes in the long term.
6. Computer vision tools for improved diagnosis and prognostication in hematological cancer (both in the field of radiology and pathology).
7. Big data and machine learning tools for application in clinical trials and real world data (RWD).
8. Artificial intelligence tools to design new drugs and to find new drug targets/neoantigens.
Keywords: Machine Learning, Artificial Intelligence, Computer Vision, Genomics, Proteomics, Single cell Genomics, Drug synthesis
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