Precision medicine, which aims to optimize medical care at the individual level, remains a significant challenge and aspiration in oncology. The pathway to a successful implementation requires methods that can work with a vast heterogeneity of cancer and consider the interplay of environmental, societal, biological, and clinical factors. To support decision-making in this context, computational frameworks must integrate large-scale, diverse, and noisy data, discover fine-grained patient subgroups with shared underlying characteristics, and characterize the imperfect preclinical spaces where novel therapies are tested. We propose an integrated digital twin framework in which machine learning and semantic models collaboratively represent and reason with diverse patient data and medical domain knowledge to generate treatment recommendations. Clinical and molecular characteristics are used to discover subtypes of brain cancers, which are represented as ontologies with associated rules to determine a patient’s membership in a given subtype. Similarly, preclinical models used for therapeutic testing are characterized and assessed for their similarity to patient cancer models. By semantically discovering links between these preclinical models and patient cancer subtypes, novel therapeutics tested on preclinical models can be prioritized and hypothesized for individual patients. This approach, which requires empirical testing, demonstrates how cross-domain reasoning can be used to propose individualized treatment plans.

SUMMARY: Network analysis (NA) has recently emerged as a new paradigm by which to model the symptom patterns of patients with complex illnesses such as cancer. NA uses graph theory-based methods to capture the interplay between symptoms and identify which symptoms may be most impactful to patient quality of life and are therefore most critical to treat/prevent. Despite NA's increasing popularity in research settings, its clinical applicability is hindered by the lack of a unified platform that consolidates all the software tools needed to perform NA, and by the lack of methods for capturing heterogeneity across patient cohorts. Addressing these limitations, we present PRONA, an R-package for Patient Reported Outcomes Network Analysis. PRONA not only consolidates previous NA tools into a unified, easy-to-use analysis pipeline, but also augments the traditional approach with functionality for performing unsupervised discovery of patient subgroups with distinct symptom patterns. AVAILABILITY AND IMPLEMENTATION: PRONA is implemented in R. Source code, installation, and use instructions are available on GitHub at https://github.com/bbergsneider/PRONA.

We extend correctness and refinement reasoning methods in order to show how angelic nondeterminism can be systematically transformed into demonic nondeterminism or determinism. This kind of transformation is important because angelic nondeterminism assumes that an agent interested in establishing the postcondition will resolve the choices intelligently. When angelic nondeterminism is reduced into demonic nondeterminism or determinism, such intelligent choices are no longer necessary. Reducing angelic nondeterminism is generally not a refinement; however, when context is taken into consideration, it can be a correctness-preserving transformation.