Multi-layer Trajectory Clustering: a Network Algorithm for Disease Subtyping Article Swipe
Sanjukta Krishnagopal
·
YOU?
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· 2020
· Open Access
·
· DOI: https://doi.org/10.1088/2057-1976/abad8f
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1088/2057-1976/abad8f
Many diseases display heterogeneity in clinical features and their progression, indicative of the existence of disease subtypes. Extracting patterns of disease variable progression for subtypes has tremendous application in medicine, for example, in early prognosis and personalized medical therapy. This work presents a novel, data-driven, network-based Trajectory Clustering (TC) algorithm for identifying Parkinson’s subtypes based on disease trajectory. Modeling patient-variable interactions as a bipartite network, TC first extracts communities of co-expressing disease variables at different stages of progression. Then, it identifies Parkinson’s subtypes by clustering similar patient trajectories that are characterized by severity of disease variables through a multi-layer network. Determination of trajectory similarity accounts for direct overlaps between trajectories as well as second-order similarities, i.e., common overlap with a third set of trajectories. This work clusters trajectories across two types of layers: (a) temporal, and (b) ranges of independent outcome variable (representative of disease severity), both of which yield four distinct subtypes. The former subtypes exhibit differences in progression of disease domains (Cognitive, Mental Health etc.), whereas the latter subtypes exhibit different degrees of progression, i.e., some remain mild, whereas others show significant deterioration after 5 years. The TC approach is validated through statistical analyses and consistency of the identified subtypes with medical literature. This generalizable and robust method can easily be extended to other progressive multi-variate disease datasets, and can effectively assist in targeted subtype-specific treatment in the field of personalized medicine.
Related Topics
Concepts
Subtyping
Cluster analysis
Disease
Variable (mathematics)
Trajectory
Set (abstract data type)
Personalized medicine
Consistency (knowledge bases)
Similarity (geometry)
Computer science
Cluster (spacecraft)
Artificial intelligence
Machine learning
Medicine
Bioinformatics
Mathematics
Biology
Internal medicine
Image (mathematics)
Physics
Mathematical analysis
Astronomy
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1088/2057-1976/abad8f
- OA Status
- green
- References
- 34
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3046347131
All OpenAlex metadata
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https://openalex.org/W3046347131Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/2057-1976/abad8fDigital Object Identifier
- Title
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Multi-layer Trajectory Clustering: a Network Algorithm for Disease SubtypingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-08-10Full publication date if available
- Authors
-
Sanjukta KrishnagopalList of authors in order
- Landing page
-
https://doi.org/10.1088/2057-1976/abad8fPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2005.14472Direct OA link when available
- Concepts
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Subtyping, Cluster analysis, Disease, Variable (mathematics), Trajectory, Set (abstract data type), Personalized medicine, Consistency (knowledge bases), Similarity (geometry), Computer science, Cluster (spacecraft), Artificial intelligence, Machine learning, Medicine, Bioinformatics, Mathematics, Biology, Internal medicine, Image (mathematics), Physics, Mathematical analysis, Astronomy, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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34Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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| publication_date | 2020-08-10 |
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