Name
Title | ||
|---|---|---|
Integrative clustering by nonnegative matrix factorization can reveal coherent functional groups from gene profile data. |
Abstract | ||
|---|---|---|
Recent developments in molecular biology and techniques for genome-wide data acquisition have resulted in abundance of data to profile genes and predict their function. These datasets may come from diverse sources and it is an open question how to commonly address them and fuse them into a joint prediction model. A prevailing technique to identify groups of related genes that exhibit similar profiles is profile-based clustering. Cluster inference may benefit from consensus across different clustering models. In this paper, we propose a technique that develops separate gene clusters from each of available data sources and then fuses them by means of nonnegative matrix factorization. We use gene profile data on the budding yeast S. cerevisiae to demonstrate that this approach can successfully integrate heterogeneous datasets and yield high-quality clusters that could otherwise not be inferred by simply merging the gene profiles prior to clustering. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/JBHI.2014.2316508 | IEEE journal of biomedical and health informatics |
Keywords | Field | DocType |
cellular biophysics,pattern clustering,data source,gene profiling,joint prediction model,gene function prediction,genetics,gene profile merging,microorganisms,profile-based clustering,gene cluster development,genome-wide data acquisition,high cluster quality,gene profile data,gene set enrichment,s. cerevisiae,data fusion,molecular biophysics,similar gene profile,molecular biology,nonnegative matrix factorization,gene cluster inference,nonnegative matrix factorization (nmf),matrix decomposition,budding yeast,gene dataset fusion,heterogeneous dataset integration,clustering model consensus,bioinformatics,data integration,integrative clustering,related gene group identification,coherent functional group,gene cluster fusion,clustering,informatics,clustering algorithms,gene expression | Data integration,Data mining,Cluster (physics),Gene,Pattern recognition,Inference,Computer science,Data acquisition,Matrix decomposition,Artificial intelligence,Non-negative matrix factorization,Cluster analysis | Journal |
Volume | Issue | ISSN |
19 | 2 | 2168-2208 |
Citations | PageRank | References |
0 | 0.34 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanja Brdar | 1 | 1 | 2.71 |
| Vladimir S. Crnojevic | 2 | 3 | 0.78 |
| Blaz Zupan | 3 | 0 | 0.34 |