PMID- 28368814
OWN - NLM
STAT- MEDLINE
DCOM- 20171010
LR  - 20171217
IS  - 1557-9964 (Electronic)
IS  - 1545-5963 (Linking)
VI  - 14
IP  - 2
DP  - 2017 Mar-Apr
TI  - Predicting Protein Functions by Using Unbalanced Random Walk Algorithm on Three 
      Biological Networks.
PG  - 360-369
LID - 10.1109/TCBB.2015.2394314 [doi]
AB  - With the gap between the sequence data and their functional annotations becomes 
      increasing wider, many computational methods have been proposed to annotate 
      functions for unknown proteins. However, designing effective methods to make good 
      use of various biological resources is still a big challenge for researchers due 
      to function diversity of proteins. In this work, we propose a new method named 
      ThrRW, which takes several steps of random walking on three different biological 
      networks: protein interaction network (PIN), domain co-occurrence network (DCN), 
      and functional interrelationship network (FIN), respectively, so as to infer 
      functional information from neighbors in the corresponding networks. With respect 
      to the topological and structural differences of the three networks, the number 
      of walking steps in the three networks will be different. In the course of 
      working, the functional information will be transferred from one network to 
      another according to the associations between the nodes in different networks. 
      The results of experiment on S. cerevisiae data show that our method achieves 
      better prediction performance not only than the methods that consider both PIN 
      data and GO term similarities, but also than the methods using both PIN data and 
      protein domain information, which verifies the effectiveness of our method on 
      integrating multiple biological data sources.
FAU - Peng, Wei
AU  - Peng W
FAU - Li, Min
AU  - Li M
FAU - Chen, Lu
AU  - Chen L
FAU - Wang, Lusheng
AU  - Wang L
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - IEEE/ACM Trans Comput Biol Bioinform
JT  - IEEE/ACM transactions on computational biology and bioinformatics
JID - 101196755
RN  - 0 (Proteins)
RN  - 0 (Saccharomyces cerevisiae Proteins)
SB  - IM
MH  - *Algorithms
MH  - Computational Biology/*methods
MH  - Protein Interaction Mapping
MH  - Protein Interaction Maps/*physiology
MH  - Proteins/*classification/*physiology
MH  - Reproducibility of Results
MH  - Saccharomyces cerevisiae Proteins
EDAT- 2017/04/04 06:00
MHDA- 2017/10/11 06:00
CRDT- 2017/04/04 06:00
PHST- 2017/04/04 06:00 [entrez]
PHST- 2017/04/04 06:00 [pubmed]
PHST- 2017/10/11 06:00 [medline]
AID - 10.1109/TCBB.2015.2394314 [doi]
PST - ppublish
SO  - IEEE/ACM Trans Comput Biol Bioinform. 2017 Mar-Apr;14(2):360-369. doi: 
      10.1109/TCBB.2015.2394314.