Name
Title | ||
|---|---|---|
Integrate multi-omic data using affinity network fusion (ANF) for cancer patient clustering |
Abstract | ||
|---|---|---|
Clustering cancer patients into subgroups and identifying cancer subtypes is an important task in cancer genomics. Clustering based on comprehensive multi-omic molecular profiling can often achieve better results than those using a single data type, since each omic data type may contain complementary information. However, it is challenging to integrate heterogeneous omic data directly. Based on one popular method - Similarity Network Fusion (SNF), we presented Affinity Network Fusion (ANF), an “upgrade” of SNF with several advantages. Similar to SNF, ANF treats each omic data type as one view of patients and learns a fused affinity matrix for clustering. We applied ANF to a harmonized TCGA dataset consisting of 2193 patients, and generated promising results on clustering patients into correct disease types. Our experimental results also demonstrated the power of feature selection and transformation combined with using ANF in patient clustering. Moreover, eigengap analysis suggests that the learned affinity matrices of four cancer types using our proposed framework may have successfully captured patient group structure and can be used for discovering unknown cancer subtypes. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BIBM.2017.8217682 | 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
Keywords | Field | DocType |
ANF,SNF,omic data type,fused affinity matrix,learned affinity matrices,cancer types,affinity network fusion,cancer genomics,heterogeneous omic data,Similarity Network Fusion,cancer subtypes,cancer patient clustering,multiomic molecular profiling,multiomic data | Kernel (linear algebra),Feature selection,Profiling (computer programming),Computer science,Eigengap,Genomics,Data type,Artificial intelligence,Probabilistic logic,Computational biology,Cluster analysis,Machine learning | Conference |
ISSN | ISBN | Citations |
2156-1125 | 978-1-5090-3051-4 | 0 |
PageRank | References | Authors |
0.34 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianle Ma | 1 | 1 | 1.70 |
| Aidong Zhang | 2 | 2970 | 405.63 |