Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation systems, and gaming. Similarly, graph neural networks (GNNs) have also demonstrated their superior performance in supervised learning for graph-structured data. In recent times, the fusion of GNN with DRL for graph-structured environments has attracted a lot of attention. This article provides a comprehensive review of these hybrid works. These works can be classified into two categories: 1) algorithmic contributions, where DRL and GNN complement each other with an objective of addressing each other’s shortcomings and 2) application-specific contributions that leverage a combined GNN-DRL formulation to address problems specific to different applications. This fusion effectively addresses various complex problems in engineering and life sciences. Based on the review, we further analyze the applicability and benefits of fusing these two domains, especially in terms of increasing generalizability and reducing computational complexity. Finally, the key challenges in integrating DRL and GNN, and potential future research directions are highlighted, which will be of interest to the broader machine learning community.

Objective: Pancreatic cancer (PC) is a silent killer, because its detection is difficult and to date no effective treatment has been developed. In the US, the current 5-year survival rate of 11%. Therefore, PC has to be detected as early as possible. Methods and procedures: In this work, we have combined the use of ultrasensitive nanobiosensors for protease/arginase detection with information fusion based hierarchical decision structure to detect PC at the localized stage by means of a simple Liquid Biopsy. The problem of early-stage detection of pancreatic cancer is modelled as a multi-class classification problem. We propose a Hard Hierarchical Decision Structure (HDS) along with appropriate feature engineering steps to improve the performance of conventional multi-class classification approaches. Further, a Soft Hierarchical Decision Structure (SDS) is developed to additionally provide confidences of predicted labels in the form of class probability values. These frameworks overcome the limitations of existing research studies that employ simple biostatistical tools and do not effectively exploit the information provided by ultrasensitive protease/arginase analyses. Results: The experimental results demonstrate that an overall mean classification accuracy of around 92% is obtained using the proposed approach, as opposed to 75% with conventional multi-class classification approaches. This illustrates that the proposed HDS framework outperforms traditional classification techniques for early-stage PC detection. Conclusion: Although this study is only based on 31 pancreatic cancer patients and a healthy control group of 48 human subjects, it has enabled combining Liquid Biopsies and Machine Learning methodologies to reach the goal of earliest PC detection. The provision of both decision labels (via HDS) as well as class probabilities (via SDS) helps clinicians identify instances where statistical model-based predictions lack confidence. This further aids in determining if more tests are required for better diagnosis. Such a strategy makes the output of our decision model more interpretable and can assist with the diagnostic procedure. Clinical impact: With further validation, the proposed framework can be employed as a decision support tool for the clinicians to help in detection of pancreatic cancer at early stages.

Ovarian cancer survival depends strongly on the time of diagnosis. Detection at stage 1 must be the goal of liquid biopsies for ovarian cancer detection. We report the development and validation of graphene-based optical nanobiosensors (G-NBSs) that quantify the activities of a panel of proteases, which were selected to provide a crowd response that is specific for ovarian cancer. These G-NBSs consist of few-layer explosion graphene featuring a hydrophilic coating, which is linked to fluorescently labeled highly selective consensus sequences for the proteases of interest, as well as a fluorescent dye. The panel of G-NBSs showed statistically significant differences in protease activities when comparing localized (early-stage) ovarian cancer with both metastatic (late-stage) and healthy control groups. A hierarchical framework integrated with active learning (AL) as a prediction and analysis tool for early-stage detection of ovarian cancer was implemented, which obtained an overall accuracy score of 94.5%, with both a sensitivity and specificity of 0.94.

Dehydration in the human body arises due to inadequate replenishment of fluids. An appropriate level of hydration is essential for optimal functioning of the human body, and complications ranging from mild discomfort to, in severe cases, death, could result from a neglected imbalance in fluid levels. Regular and accurate monitoring of hydration status can provide meaningful information for people operating in stressful environmental conditions, such as athletes, military professionals and the elderly. In this study, we propose a non-invasive hydration monitoring technique employing non-ionizing electromagnetic power in the microwave band to estimate the changes in the water content of the whole body. Specifically, we investigate changes in the attenuation coefficient in the frequency range 2–3.5 GHz between a pair of planar antennas positioned across a participant’s arm during various states of hydration. Twenty healthy young adults (10M, 10F) underwent controlled hypohydration and euhydration control bouts. The attenuation coefficient was compared among trials and used to predict changes in body mass. Volunteers lost $1.50\pm 0.44\%$ and $0.49\pm 0.54\%$ body mass during hypohydration and euhydration, respectively. The microwave transmission-based attenuation coefficient (2–3.5 GHz) was accurate in predicting changes in hydration status. The corresponding regression analysis demonstrates that building separate estimation models for dehydration and rehydration phases offer better predictive performance (88%) relative to a common model for both the phases (76%).

Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming. Similarly, graph neural networks (GNN) have also demonstrated their superior performance in supervised learning for graph-structured data. In recent times, the fusion of GNN with DRL for graph-structured environments has attracted a lot of attention. This paper provides a comprehensive review of these hybrid works. These works can be classified into two categories: (1) algorithmic enhancement, where DRL and GNN complement each other for better utility; (2) application-specific enhancement, where DRL and GNN support each other. This fusion effectively addresses various complex problems in engineering and life sciences. Based on the review, we further analyze the applicability and benefits of fusing these two domains, especially in terms of increasing generalizability and reducing computational complexity. Finally, the key challenges in integrating DRL and GNN, and potential future research directions are highlighted, which will be of interest to the broader machine learning community.

In recent years, neural networks (NNs) have been embraced by several scientific and engineering disciplines for diverse modeling and inferencing applications. The importance of quantifying the confidence in NN predictions has escalated due to the increasing adoption of these decision models. Nevertheless, conventional NN do not furnish uncertainty estimates associated with their predictions and are therefore ill-calibrated. Uncertainty quantification techniques offer probability distributions or CIs to represent the uncertainty associated with NN predictions, instead of solely presenting the point predictions/estimates. Once the uncertainty in NN is quantified, it is crucial to leverage this information to modify training objectives and improve the accuracy and reliability of the corresponding decision models. This work presents a novel framework to utilize the knowledge of input and output uncertainties in NN to guide querying process in the context of Active Learning. We also derive the lower and upper bounds for label complexity. The efficacy of the proposed framework is established by conducting experiments across classification and regression tasks.