Abstract
Many proteins are known to perform their own functions when they form particular groups of proteins, called protein complexes. With the advent of large-scale protein-protein interaction (PPI) studies, it has been a challenging problem in systems biology to predict protein complexes from PPIs. In this paper, we propose a novel method, called Repeated Simulated Annealing of Partitions of Proteins (ReSAPP), which predicts protein complexes from weighted PPIs. ReSAPP, in the first stage, generates multiple (possibly different) partitions of all proteins of given PPIs by repeatedly applying a simulated annealing based optimization algorithm to the PPIs. In the second stage, all different clusters of size two or more in those multiple partitions are merged into a collection of those clusters, which are outputted as predicted protein complexes. In performance comparison of ReSAPP with our previous algorithm, PPSampler2, as well as other various tools, MCL, MCODE, DPClus, CMC, COACH, RRW, NWE, and PPSampler1, ReSAPP is shown to outperform the other methods. Furthermore, the value of F-measure of ReSAPP is higher than that of the variant of ReSAPP without merging partitions. Thus, we empirically conclude that the combination of sampling multiple partitions and merging them is effective to predict protein complexes.
| Original language | English |
|---|---|
| Article number | 1442004 |
| Journal | Journal of bioinformatics and computational biology |
| Volume | 12 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - Dec 29 2014 |
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All Science Journal Classification (ASJC) codes
- Biochemistry
- Molecular Biology
- Computer Science Applications
Cite this
ReSAPP : Predicting overlapping protein complexes by merging multiple-sampled partitions of proteins. / Kobiki, So; Maruyama, Osamu.
In: Journal of bioinformatics and computational biology, Vol. 12, No. 6, 1442004, 29.12.2014.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - ReSAPP
T2 - Predicting overlapping protein complexes by merging multiple-sampled partitions of proteins
AU - Kobiki, So
AU - Maruyama, Osamu
PY - 2014/12/29
Y1 - 2014/12/29
N2 - Many proteins are known to perform their own functions when they form particular groups of proteins, called protein complexes. With the advent of large-scale protein-protein interaction (PPI) studies, it has been a challenging problem in systems biology to predict protein complexes from PPIs. In this paper, we propose a novel method, called Repeated Simulated Annealing of Partitions of Proteins (ReSAPP), which predicts protein complexes from weighted PPIs. ReSAPP, in the first stage, generates multiple (possibly different) partitions of all proteins of given PPIs by repeatedly applying a simulated annealing based optimization algorithm to the PPIs. In the second stage, all different clusters of size two or more in those multiple partitions are merged into a collection of those clusters, which are outputted as predicted protein complexes. In performance comparison of ReSAPP with our previous algorithm, PPSampler2, as well as other various tools, MCL, MCODE, DPClus, CMC, COACH, RRW, NWE, and PPSampler1, ReSAPP is shown to outperform the other methods. Furthermore, the value of F-measure of ReSAPP is higher than that of the variant of ReSAPP without merging partitions. Thus, we empirically conclude that the combination of sampling multiple partitions and merging them is effective to predict protein complexes.
AB - Many proteins are known to perform their own functions when they form particular groups of proteins, called protein complexes. With the advent of large-scale protein-protein interaction (PPI) studies, it has been a challenging problem in systems biology to predict protein complexes from PPIs. In this paper, we propose a novel method, called Repeated Simulated Annealing of Partitions of Proteins (ReSAPP), which predicts protein complexes from weighted PPIs. ReSAPP, in the first stage, generates multiple (possibly different) partitions of all proteins of given PPIs by repeatedly applying a simulated annealing based optimization algorithm to the PPIs. In the second stage, all different clusters of size two or more in those multiple partitions are merged into a collection of those clusters, which are outputted as predicted protein complexes. In performance comparison of ReSAPP with our previous algorithm, PPSampler2, as well as other various tools, MCL, MCODE, DPClus, CMC, COACH, RRW, NWE, and PPSampler1, ReSAPP is shown to outperform the other methods. Furthermore, the value of F-measure of ReSAPP is higher than that of the variant of ReSAPP without merging partitions. Thus, we empirically conclude that the combination of sampling multiple partitions and merging them is effective to predict protein complexes.
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U2 - 10.1142/S0219720014420049
DO - 10.1142/S0219720014420049
M3 - Article
C2 - 25385080
AN - SCOPUS:84930033464
VL - 12
JO - Journal of Bioinformatics and Computational Biology
JF - Journal of Bioinformatics and Computational Biology
SN - 0219-7200
IS - 6
M1 - 1442004
ER -