Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms.

It is widely believed that cooperation between clinicians and machines may address many of the decisional fragilities intrinsic to current medical practice. However, the realization of this potential will require more precise definitions of disease states as well as their dynamics and interactions. A careful probabilistic examination of symptoms and signs, including the molecular profiles of the relevant biochemical networks, will often be required for building an unbiased and efficient diagnostic approach.

Disease evolution in reaction networks: Implications for a diagnostic problem.

We study the time evolution of symptoms (signs) with some defects in the dynamics of a reaction network as a (microscopic) model for the progress of disease phenotypes. To this end, we take a large population of reaction networks and follow the stochastic dynamics of the system to see how the development of defects affects the macroscopic states of the signs probability distribution. We start from some plausible definitions for the healthy and disease states along with a dynamical model for the emergence of diseases by a reverse simulated annealing algorithm.

Efficiency and irreversibility of movements in a city.

We know that maximal efficiency in physical systems is attained by reversible processes. It is then interesting to see how irreversibility affects efficiency in other systems, e.g.

Statistical physics of medical diagnostics: Study of a probabilistic model.

We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases.

Uncovering hidden disease patterns by simulating clinical diagnostic processes.

Choosing a sequence of observations (often with stochastic outcomes) which maximizes the information gain from a system of interacting variables is essential for a wide range of problems in science and technology, such as clinical diagnostic problems. Here, we use a probabilistic model of diseases and signs/symptoms to simulate the effects of medical decisions on the quality of diagnosis by maximizing an appropriate objective function of the medical observations. The study provides a systematic way of proposing new medical tests, considering the significance of diseases and cost of the suggested observations.

Distance measures and evolution of polymer chains in their topological space.

Conformational transitions are ubiquitous in biomolecular systems, have significant functional roles and are subject to evolutionary pressures. Here we provide a first theoretical framework for topological transition, i.e.

Statistical mechanics of maximal independent sets.

The graph theoretic concept of maximal independent set arises in several practical problems in computer science as well as in game theory. A maximal independent set is defined by the set of occupied nodes that satisfy some packing and covering constraints. It is known that finding minimum and maximum-density maximal independent sets are hard optimization problems.