Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408, USA.
Published: November 2023
AI Article Synopsis
Article Abstract
Estimating the influence that individual nodes have on one another in a Boolean network is essential to predict and control the system's dynamical behaviour, for example, detecting key therapeutic targets to control pathways in models of biological signalling and regulation. Exact estimation is generally not possible due to the fact that the number of configurations that must be considered grows exponentially with the system size. However, approximate, scalable methods exist in the literature. These methods can be divided into two main classes: (i) graph-theoretic methods that rely on representations of Boolean dynamics into static graphs and (ii) mean-field approaches that describe average trajectories of the system but neglect dynamical correlations. Here, we compare systematically the performance of these state-of-the-art methods on a large collection of real-world gene regulatory networks. We find comparable performance across methods. All methods underestimate the ground truth, with mean-field approaches having a better recall but a worse precision than graph-theoretic methods. Computationally speaking, graph-theoretic methods are faster than mean-field ones in sparse networks, but are slower in dense networks. The preference of which method to use, therefore, depends on a network's connectivity and the relative importance of recall versus precision for the specific application at hand.
Introduction: The electronic health record (EHR) has greatly expanded healthcare communication between patients and health workers. However, the volume and complexity of EHR messages have increased health workers' cognitive load, impeding effective care delivery and contributing to burnout.
Methods: To understand these potential detriments resulting from EHR communication, we analyzed EHR messages sent between patients and health workers at Emory Healthcare, a large academic healthcare system in Atlanta, Georgia.
Enhancers are discrete DNA elements that regulate the expression of eukaryotic genes. They are important not only for their regulatory function, but also as loci that are frequently associated with disease traits. Despite their significance, our conceptual understanding of how enhancers work remains limited.
Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disorder marked by transient involuntary movements triggered by sudden actions. Current diagnostic approaches, including genetic screening, face challenges in identifying secondary cases due to symptom overlap with other disorders. This study introduces a novel PKD recognition method utilizing a resting-state electroencephalogram (EEG) functional connectivity matrix and a deep learning architecture (AT-1CBL).
Mounting neuroimaging evidence indicates that patients with thyroid-associated ophthalmopathy (TAO) demonstrate altered brain function and structure. Nonetheless, the alterations in the topological properties of the functional brain connectome in TAO patients are not yet fully understood. This study aimed to investigate the topological organization of the functional brain connectome in TAO patients using graph-theoretic methods.
Objectives: Older adults often experience more fragmented and less restful sleep than younger individuals. However, the mechanisms in the brain that underlie these phenomena have been rarely studied. The aim of this study was to identify the differences in global brain network properties between older adults with good and poor sleep quality and to explore the relationship between these network properties and symptoms of insomnia.
