Integrated analysis of multiple data sources reveals modular structure of biological networks.
It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks.
Lu
Hongchao
H
Bioinformatics Laboratory and National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, PR China.
Shi
Baochen
B
Wu
Gaowei
G
Zhang
Yong
Y
Zhu
Xiaopeng
X
Zhang
Zhihua
Z
Liu
Changning
C
Zhao
Yi
Y
Wu
Tao
T
Wang
Jie
J
Chen
Runsheng
R
D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D003628:Database Management Systems; D030562:Databases, Protein; D008954:Models, Biological; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D018511:Systems Integration; D014584:User-Computer Interface
D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins