A new study from the University of Surrey has demonstrated a novel approach to training artificial intelligence (AI) models using a “punishment and reward” method. The researchers believe that this approach could provide hope for finding new therapeutic methods for cancer, particularly aggressive cancers with little information on their behavior.
The study shows that an open-ended deep reinforcement learning method can stabilize large datasets used in AI models, which holds the prospect of uncovering ways to arrest the development of cancer by predicting the response of cancerous cells to perturbations including drug treatment. The traditional approach to training AI models involves supervised learning, where the model is trained on labeled data. However, this approach requires a large amount of labeled data, which may not be available in some cases.
Reinforcement learning is another approach that uses a reward-penalty method to teach an AI model. In reinforcement learning, an agent learns by interacting with its environment and receiving rewards or penalties based on its actions. This type of machine learning has been used successfully in various applications such as game playing and robotics.The punishment and reward method used in this study is a variant of reinforcement learning that involves punishing the agent for making incorrect decisions and rewarding it for making correct decisions. The researchers applied this method to predict the response of cancerous cells to perturbations including drug treatment.
They used a dataset consisting of gene expression profiles from 200 nodes representing different genes involved in cancer pathways. The results showed that the punishment and reward method was able to stabilize the dataset and improve prediction accuracy compared to other methods such as supervised learning and unsupervised learning. The researchers believe that this approach could be used to develop new therapeutic methods for cancer by predicting how cancerous cells will respond to different treatments.
Dr Sotiris Moschoyiannis, corresponding author of the study from the University of Surrey, said: “There are a heart-breaking number of aggressive cancers out there with little to no information on where they come from, let alone how to categorize their behavior. This is where machine learning can provide real hope for us all. “The potential applications of this research are significant. Cancer is one of the leading causes of death worldwide, with over 9 million deaths in 2020 alone . Aggressive cancers are particularly difficult to treat due to their rapid growth and tendency to spread quickly throughout the body . If successful, this new approach could lead to more effective treatments for aggressive cancers by predicting how cancerous cells will respond to different treatments.
In conclusion, The new approach to the punishment and reward method of training AI for cancer treatment involves a more complex system. Instead of providing rewards or punishments based on the outcome of an action, this approach takes into account the complexity of cancer progression and evolution.
The researchers used a technique called “multi-agent reinforcement learning” to train AI models to work together to find the best treatment options for aggressive cancers.This new approach has the potential to unlock new treatments for aggressive cancers by taking into account the complexity of cancer progression and evolution. It also has implications beyond cancer treatment, as it could be applied to other complex problems in healthcare and beyond.Self-efficacy beliefs are cognitions that determine whether health behavior change will be initiated, how much effort will be expended, and how successful the change will be .
Patient education, coaching, and self-management have been shown to be effective in reducing cancer pain . These approaches could potentially be combined with the new AI training method to develop personalized treatment plans for cancer patients. While this new approach shows promise, there is still much work to be done before it can be implemented in clinical settings. More research is needed to validate its effectiveness and safety. Additionally, ethical considerations must be taken into account when using AI The potential for artificial intelligence (AI) in healthcare is vast, and AI is increasingly being applied within the field.
In oncology, AI has the potential to provide clinical decision support for cancer diagnosis and screening, process medical images, and develop personalized treatment plans .However, there are also risks and challenges associated with the use of AI in healthcare. One major concern is bias against demographic groups that can make its way into doctor’s notes and other data sources used to train AI models To address these concerns, researchers must work to develop unbiased AI models that take into account the diversity of patient populations.Additionally, there must be transparency in how AI models are developed and validated to ensure their safety and effectiveness .
Overall, the new approach to the punishment and reward method of training AI for cancer treatment offers a promising avenue for developing personalized treatment plans for aggressive cancers. However, it is important to continue researching and developing unbiased AI models that take into account the complexity of cancer progression and evolution while also ensuring their safety and effectiveness in clinical settings.
