PMID- 23368995
OWN - NLM
STAT- MEDLINE
DCOM- 20130802
LR  - 20211021
IS  - 1471-2105 (Electronic)
IS  - 1471-2105 (Linking)
VI  - 14 Suppl 1
IP  - Suppl 1
DP  - 2013
TI  - A knowledge-based decision support system in bioinformatics: an application to 
      protein complex extraction.
PG  - S5
LID - 10.1186/1471-2105-14-S1-S5 [doi]
AB  - BACKGROUND: We introduce a Knowledge-based Decision Support System (KDSS) in 
      order to face the Protein Complex Extraction issue. Using a Knowledge Base (KB) 
      coding the expertise about the proposed scenario, our KDSS is able to suggest 
      both strategies and tools, according to the features of input dataset. Our system 
      provides a navigable workflow for the current experiment and furthermore it 
      offers support in the configuration and running of every processing component of 
      that workflow. This last feature makes our system a crossover between classical 
      DSS and Workflow Management Systems. RESULTS: We briefly present the KDSS' 
      architecture and basic concepts used in the design of the knowledge base and the 
      reasoning component. The system is then tested using a subset of Saccharomyces 
      cerevisiae Protein-Protein interaction dataset. We used this subset because it 
      has been well studied in literature by several research groups in the field of 
      complex extraction: in this way we could easily compare the results obtained 
      through our KDSS with theirs. Our system suggests both a preprocessing and a 
      clustering strategy, and for each of them it proposes and eventually runs suited 
      algorithms. Our system's final results are then composed of a workflow of tasks, 
      that can be reused for other experiments, and the specific numerical results for 
      that particular trial. CONCLUSIONS: The proposed approach, using the KDSS' 
      knowledge base, provides a novel workflow that gives the best results with regard 
      to the other workflows produced by the system. This workflow and its numeric 
      results have been compared with other approaches about PPI network analysis found 
      in literature, offering similar results.
FAU - Fiannaca, Antonino
AU  - Fiannaca A
AD  - ICAR-CNR, National Research Council of Italy, Viale delle Scienze Ed, 11, 
      Palermo, 90128, Italy.
FAU - La Rosa, Massimo
AU  - La Rosa M
FAU - Urso, Alfonso
AU  - Urso A
FAU - Rizzo, Riccardo
AU  - Rizzo R
FAU - Gaglio, Salvatore
AU  - Gaglio S
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20130114
PL  - England
TA  - BMC Bioinformatics
JT  - BMC bioinformatics
JID - 100965194
RN  - 0 (Multiprotein Complexes)
RN  - 0 (Saccharomyces cerevisiae Proteins)
SB  - IM
MH  - Algorithms
MH  - Computational Biology/methods
MH  - Decision Support Techniques
MH  - *Knowledge Bases
MH  - Multiprotein Complexes/metabolism
MH  - *Protein Interaction Mapping
MH  - Saccharomyces cerevisiae Proteins/metabolism
MH  - Software
MH  - Workflow
PMC - PMC3548703
EDAT- 2013/02/13 06:00
MHDA- 2013/08/03 06:00
PMCR- 2013/01/14
CRDT- 2013/02/02 06:00
PHST- 2013/02/02 06:00 [entrez]
PHST- 2013/02/13 06:00 [pubmed]
PHST- 2013/08/03 06:00 [medline]
PHST- 2013/01/14 00:00 [pmc-release]
AID - 1471-2105-14-S1-S5 [pii]
AID - 10.1186/1471-2105-14-S1-S5 [doi]
PST - ppublish
SO  - BMC Bioinformatics. 2013;14 Suppl 1(Suppl 1):S5. doi: 10.1186/1471-2105-14-S1-S5. 
      Epub 2013 Jan 14.