Abstract
Background: Detecting protein complexes is one of essential and fundamental tasks in understanding various biological functions or processes. Therefore accurate identification of protein complexes is indispensable. Methods. For more accurate detection of protein complexes, we propose an algorithm which detects dense protein sub-networks of which proteins share closely located bottleneck proteins. The proposed algorithm is capable of finding protein complexes which allow overlapping with each other. Results: We applied our algorithm to several PPI (Protein-Protein Interaction) networks of Saccharomyces cerevisiae and Homo sapiens, and validated our results using public databases of protein complexes. The prediction accuracy was even more improved over our previous work which used also bottleneck information of the PPI network, but showed limitation when predicting small-sized protein complex detection. Conclusions: Our algorithm resulted in overlapping protein complexes with significantly improved F1 score over existing algorithms. This result comes from high recall due to effective network search, as well as high precision due to proper use of bottleneck information during the network search.
| Original language | English |
|---|---|
| Article number | S5 |
| Journal | BMC Medical Informatics and Decision Making |
| Volume | 13 |
| Issue number | SUPPL1 |
| DOIs | |
| Publication status | Published - 2013 Apr 12 |
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All Science Journal Classification (ASJC) codes
- Health Policy
- Health Informatics
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Improved method for protein complex detection using bottleneck proteins. / Ahn, Jaegyoon; Lee, Dae Hyun; Yoon, Youngmi; Yeu, Yunku; Park, Sanghyun.
In: BMC Medical Informatics and Decision Making, Vol. 13, No. SUPPL1, S5, 12.04.2013.Research output: Contribution to journal › Article
TY - JOUR
T1 - Improved method for protein complex detection using bottleneck proteins
AU - Ahn, Jaegyoon
AU - Lee, Dae Hyun
AU - Yoon, Youngmi
AU - Yeu, Yunku
AU - Park, Sanghyun
PY - 2013/4/12
Y1 - 2013/4/12
N2 - Background: Detecting protein complexes is one of essential and fundamental tasks in understanding various biological functions or processes. Therefore accurate identification of protein complexes is indispensable. Methods. For more accurate detection of protein complexes, we propose an algorithm which detects dense protein sub-networks of which proteins share closely located bottleneck proteins. The proposed algorithm is capable of finding protein complexes which allow overlapping with each other. Results: We applied our algorithm to several PPI (Protein-Protein Interaction) networks of Saccharomyces cerevisiae and Homo sapiens, and validated our results using public databases of protein complexes. The prediction accuracy was even more improved over our previous work which used also bottleneck information of the PPI network, but showed limitation when predicting small-sized protein complex detection. Conclusions: Our algorithm resulted in overlapping protein complexes with significantly improved F1 score over existing algorithms. This result comes from high recall due to effective network search, as well as high precision due to proper use of bottleneck information during the network search.
AB - Background: Detecting protein complexes is one of essential and fundamental tasks in understanding various biological functions or processes. Therefore accurate identification of protein complexes is indispensable. Methods. For more accurate detection of protein complexes, we propose an algorithm which detects dense protein sub-networks of which proteins share closely located bottleneck proteins. The proposed algorithm is capable of finding protein complexes which allow overlapping with each other. Results: We applied our algorithm to several PPI (Protein-Protein Interaction) networks of Saccharomyces cerevisiae and Homo sapiens, and validated our results using public databases of protein complexes. The prediction accuracy was even more improved over our previous work which used also bottleneck information of the PPI network, but showed limitation when predicting small-sized protein complex detection. Conclusions: Our algorithm resulted in overlapping protein complexes with significantly improved F1 score over existing algorithms. This result comes from high recall due to effective network search, as well as high precision due to proper use of bottleneck information during the network search.
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U2 - 10.1186/1472-6947-13-S1-S5
DO - 10.1186/1472-6947-13-S1-S5
M3 - Article
VL - 13
JO - BMC Medical Informatics and Decision Making
JF - BMC Medical Informatics and Decision Making
SN - 1472-6947
IS - SUPPL1
M1 - S5
ER -