Molecular Subtyping and Outlier Detection in Human Disease Using the Paraclique Algorithm
| Autor: | Ronald D. Hagan, Michael A. Langston |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
lcsh:T55.4-60.8
Computer science paraclique algorithm Gene regulatory network Disease outlier detection lcsh:QA75.5-76.95 Theoretical Computer Science 03 medical and health sciences molecular subtyping 0302 clinical medicine Human disease lcsh:Industrial engineering. Management engineering 030304 developmental biology Clustering coefficient Clique 0303 health sciences Numerical Analysis transcriptomic data Subtyping Computational Mathematics Computational Theory and Mathematics 030220 oncology & carcinogenesis Graph (abstract data type) Anomaly detection lcsh:Electronic computers. Computer science Algorithm |
| Zdroj: | Algorithms, Vol 14, Iss 63, p 63 (2021) Algorithms Volume 14 Issue 2 |
| ISSN: | 1999-4893 |
| Popis: | Recent discoveries of distinct molecular subtypes have led to remarkable advances in treatment for a variety of diseases. While subtyping via unsupervised clustering has received a great deal of interest, most methods rely on basic statistical or machine learning methods. At the same time, techniques based on graph clustering, particularly clique-based strategies, have been successfully used to identify disease biomarkers and gene networks. A graph theoretical approach based on the paraclique algorithm is described that can easily be employed to identify putative disease subtypes and serve as an aid in outlier detection as well. The feasibility and potential effectiveness of this method is demonstrated on publicly available gene co-expression data derived from patient samples covering twelve different disease families. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|