Researchers from Queen Mary University of London have developed a machine learning algorithm that ranks drugs based on their efficacy in reducing cancer cell growth. The approach may have the potential to advance personalised therapies in the future by allowing oncologists to select the best drugs to treat individual cancer patients. The method, named Drug Ranking Using Machine Learning (DRUML), was published today in Nature Communications and is based on machine learning analysis of data derived from the study of proteins expressed in cancer cells. Having been trained on the responses of these cells to over 400 drugs, DRUML predicts the best drug to treat a given cancer model. Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, said: "DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset. These are exciting results because previous machine learning methods have failed to accurately predict drug responses in verification datasets, and they demonstrate the robustness and wide applicability of our method."

The method, named Drug Ranking Using Machine Learning (DRUML), was published today in Nature Communications and is based on machine learning analysis of data derived from the study of proteins expressed in cancer cells. Having been trained on the responses of these cells to over 400 drugs, DRUML predicts the best drug to treat a given cancer model. Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, said: "DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset. By training the models using the responses of these cells to 412 cancer drugs listed in drug response repositories, DRUML was able to produce ordered lists based on the effectiveness of the drugs to reduce cancer cell growth. This study represents a significant advancement in artificial intelligence in biomedical research, and demonstrates that machine learning using proteomics and phosphoproteomics data may be an effective way of selecting the best drug to treat different cancer models. The method, named Drug Ranking Using Machine Learning (DRUML), was published today in Nature Communications and is based on machine learning analysis of data derived from the study of proteins expressed in cancer cells. Having been trained on the responses of these cells to over 400 drugs, DRUML predicts the best drug to treat a given cancer model. Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, said: "DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset.

Processed tissue slide viewing and assessment is crucial in determining the diagnosis and cancer staging. This protocol is thus instrumental in deciding the treatment therapy for a patient. Applications of deep learning and AI in the medical fields such as dermatology, radiology, ophthalmology, and pathology have shown great potential in providing great accuracy in diagnosis. Although AI promises to provide quality healthcare, the cost of slide digitization and lack of infrastructure for AI deployments remain as barriers for widespread adoption of digital pathology in clinical settings. Google recently published a paper in Nature demonstrating the prediction of metastatic breast cancer in lymph nodes using convolutional neural networks at an accuracy comparable to pathologists.

Right: A picture of the prototype which has been retrofitted into a typical clinical-grade light microscope. Applications of deep learning in medical disciplines including ophthalmology, dermatology, radiology and pathology have recently shown great promise to increase both the accuracy and availability of high-quality healthcare. To further this technology, Google researchers have developed a tool that combines augmented reality with a deep learning neural network to provide pathologists with help in spotting cancerous cells on slides under a microscope. The prototype Augmented Reality Microscope (ARM) platform consists of a modified light microscope that enables real-time image analysis and presentation of the results of machine learning algorithms directly into the field of view. The ARM can be retrofitted into existing light microscopes found in hospitals and clinics by using low-cost, readily available components, and without the need for whole slide digital versions of the tissue being analyzed.

My thesis investigates fault tolerance for cooperative agent systems that have some equivalent of self-replication and self-death. Utilizing biologically-inspired mechanisms, I increase multi-agent system robustness for faulty agents when it is unknown exactly which agent is malfunctioning. It is important to determine new ways to increase robustness of a system, as otherwise it cannot be guaranteed to function in all situations and thus cannot be relied upon. Robustness of a system allows agents to recover from errors and thus function continuously, an increasingly important trait as agent systems are deployed in real world scenarios such as sensor networks or surveillance systems where faulty or malicious nodes could disrupt application performance. To achieve robustness, there must either be prevention of all errors, or a technique for recovering from errors after they have occurred. My thesis creates a new fault tolerance mechanism inspired by cancer biology to remove faulty agents, and then re-applies the developed technique to study the removal of biological cancer cells in simulation.