Subtypes of Relapsing-Remitting Multiple Sclerosis Identified by Network Analysis
Howlett-Prieto, Q.; Oommen, C.; Carrithers, M. D.; Wunsch, D. C.; Hier, D. B.
Show abstract
The objective of this study was to use network analysis to identify subtypes of relapsing-remitting multiple sclerosis subjects based on their cumulative signs and symptoms. We reviewed the electronic medical records of 120 subjects with relapsing-remitting multiple sclerosis and recorded signs and symptoms. Signs and symptoms were mapped to a neuroontology and then collapsed into 16 superclasses by subsumption and normalized. Bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using Gephi. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps were used to visualize differences in features by the community. Network analysis of the unipartite graph yielded a higher modularity score (0.49) than the bipartite graph (0.247). Network analysis can partition multiple sclerosis subjects into communities based on signs and symptoms. Communities of subjects with predominant motor, sensory, pain, fatigue, cognitive, behavior, and fatigue features were found. Larger datasets and additional partitioning algorithms are needed to confirm these results and elucidate their clinical significance.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Current status and future opportunities in modeling Multiple Sclerosis clinical characteristics 94%
- The Canadian Collaborative Project on Genetic Susceptibility to Multiple Sclerosis cohort population structure and disease etiology 92%
- Optical coherence tomography assessment of axonal and neuronal damage of the retina in patients with familial and sporadic multiple sclerosis 91%
Similar papers in this journal
- Immunomodulatory Therapy with Glatiramer Acetate Reduces Endoplasmic Reticulum Stress and Mitochondrial Dysfunction in Experimental Autoimmune Encephalomyelitis 94%
- System-level analysis of genes mutated in muscular dystrophies reveals a functional pattern associated with muscle weakness distribution 93%
- Correlates of patient-reported cognitive performance with regard to disability 92%
Similar papers in this journal
- Mood symptoms and chronic fatigue syndrome due to relapsing remitting multiple sclerosis are associated with immune activation and aberrations in the erythron 93%
- In-phase bilateral upper limb exercises improve cognitive and motor function in Progressive Multiple Sclerosis: A pilot randomized controlled trial 90%
- Possible association of nucleobindin-1 protein with depressive disorder in patients with HIV infection 90%
Similar papers in this journal
- A self-administered, artificial intelligence (AI) platform for cognitive assessment in multiple sclerosis (MS) 92%
- Smartphone Postural Sway and Pronator Drift tests as Measures of Neurological Disability 92%
- Machine Learning Analysis of Motor Evoked Potential Time Series to Predict Disability Progression in Multiple Sclerosis 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.