Protein generation has a wide range of applications in the design of therapeutic antibodies and the creation of new drugs. Nevertheless, it is a challenging endeavour, largely due to the complexities intrinsic to protein structures and the constraints of current generative models. The complex three-dimensional structure of proteins and the vast number of potential conformations that they can adopt present significant challenges for sampling. This paper introduces a novel variational autoencoder based on Lévy noise and a quantum graph transformer attention mechanism, which enables a more effective exploration of the conformational space. The method was applied to two protein datasets, resulting in enhanced outcomes in terms of Fréchet distance by a factor of up to 168 in comparison to a variational autoencoder using Gaussian noise and a bilinear attention mechanism.

Increasingly complex learning methods such as boosting, bagging and deep learning have made ML models more accurate, but harder to interpret and explain, culminating in black-box machine learning models. Model developers and users alike are often presented with a trade-off between performance and intelligibility, especially in high-stakes applications like medicine. In the present article we propose a novel methodological approach for generating explanations for the predictions of a generic machine learning model, given a specific instance for which the prediction has been made. The method, named AraucanaXAI, is based on surrogate, locally-fitted classification and regression trees that are used to provide post-hoc explanations of the prediction of a generic machine learning model. Advantages of the proposed XAI approach include superior fidelity to the original model, ability to deal with non-linear decision boundaries, and native support to both classification and regression problems. We provide a packaged, open-source implementation of the AraucanaXAI method and evaluate its behaviour in a number of different settings that are commonly encountered in medical applications of AI. These include potential disagreement between the model prediction and physician's expert opinion and low reliability of the prediction due to data scarcity.

Protein-protein interactions play a fundamental role in drug design, gene therapy and vaccine development. The study of protein-protein interactions relies heavily on complex and time-consuming experiments, which has a severe impact on research throughputs. Thus, it is important to provide the experimentalist with the most promising cases by screening rapidly through a very large number of potential candidates. We propose a new deep neural network architecture that allows the binding probability for two proteins to be predicted instantly based solely on their amino acid sequences. Subsequently, screenings are performed based on the binding probabilities. The novelty of our approach lies in the fact that we consider self-binding and folding amino acid sequences, rather than just looking at these sequences per se. Our novel Siamese Pyramid Network (SPNet) architecture is inspired by Feature Pyramid Networks and consists of a multi-level Siamese neural network with an attention mechanism and a multilevel, trainable binding probability prediction network. Our experimental evaluation is performed on a strict dataset and shows that SPNet outperforms the state-of-the-art architectures. In addition, we employ SPNet to find the proteins that are most likely to bind with the Covid-2019 spike, thus providing a small and potentially valuable set of candidates for a future therapeutic vaccine.

Operationalizing care delivery through an interdisciplinary healthcare team (IHT) requires knowledge about the overall structure of an IHT and the behavioral rules that "control" the dynamics of this structure interpreted as team and clinical leadership maintenance and task allocation. While progress has been made in understanding IHT structure, there is less work on the behavioral aspects of an IHT associated with its dynamics. In this paper we fill this void by extending our Team and Workflow Management Framework (TWMF) with a set of rules to operationalize IHT behavior in terms of clinical leadership, coordination of workflow execution over multiple days as part of continuity of care, and management of tasks, including urgent ones that prevent planned workflow execution. We briefly describe a proof-of-concept implementation of extended TWMF in the form of a computer system for supporting cooperative execution of clinical workflows by an IHT. The system is built on top of an existing business workflow execution engine and employs behavioral rules to control the IHT behavior. We also illustrate the operations of TWMF in a case study where an IHT is executing a workflow for the management of post-operative inpatient recovery after radical prostatectomy.

The development and application of an interactive multiple-objective linear programming algorithm are considered. The displaced worst compromise is used as a reference point, to select the most preferred decision alternative. This point represents the undesired target values, which a decision-maker would prefer to avoid. To reflect the dynamics of a decisionmaker's preferences, use is made of the concept of a displaced point which captures changes of reference during the decision process. An illustrative example is presented to show the process of reaching a final compromise decision.>