Limits...
Discovering time-lagged rules from microarray data using gene profile classifiers.

Gallo CA, Carballido JA, Ponzoni I - BMC Bioinformatics (2011)

Bottom Line: The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge.In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations.The method was carefully validated with several publicly available data sets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratorio de Investigación y Desarrollo en Computación Científica (LIDeCC), Departamento de Ciencias e Ingeniería de la Computación, Universidad Nacional del Sur, Av, Alem 1253, 8000, Bahía Blanca, Argentina.

ABSTRACT

Background: Gene regulatory networks have an essential role in every process of life. In this regard, the amount of genome-wide time series data is becoming increasingly available, providing the opportunity to discover the time-delayed gene regulatory networks that govern the majority of these molecular processes.

Results: This paper aims at reconstructing gene regulatory networks from multiple genome-wide microarray time series datasets. In this sense, a new model-free algorithm called GRNCOP2 (Gene Regulatory Network inference by Combinatorial OPtimization 2), which is a significant evolution of the GRNCOP algorithm, was developed using combinatorial optimization of gene profile classifiers. The method is capable of inferring potential time-delay relationships with any span of time between genes from various time series datasets given as input. The proposed algorithm was applied to time series data composed of twenty yeast genes that are highly relevant for the cell-cycle study, and the results were compared against several related approaches. The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge. Additionally, a genome-wide study on multiple publicly available time series data was performed. In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations.

Conclusions: A novel method for inferring time-delayed gene regulatory networks from genome-wide time series datasets is proposed in this paper. The method was carefully validated with several publicly available data sets. The results have demonstrated that the algorithm constitutes a usable model-free approach capable of predicting meaningful relationships between genes, revealing the time-trends of gene regulation.

Show MeSH
Precision and score values achieved by GRNCOP2 with different Accuracy and RCA parameters. Values of the precision and score metrics achieved by GRNCOP2 with the Accuracy and RCA parameters varying from 0.70 to 1 and from 0.60 to 1 respectively, with the SCP parameter fixed in 0.95 and with W = 4. The number of associations is also showed. Figure 5a: yeastnet precision. Figure 5b: yeastnet score. Figure 5c: co-citation precision. Figure 5d: co-citation score. Figure 5e: GO precision. Figure 5f: number of associations.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3111372&req=5

Figure 5: Precision and score values achieved by GRNCOP2 with different Accuracy and RCA parameters. Values of the precision and score metrics achieved by GRNCOP2 with the Accuracy and RCA parameters varying from 0.70 to 1 and from 0.60 to 1 respectively, with the SCP parameter fixed in 0.95 and with W = 4. The number of associations is also showed. Figure 5a: yeastnet precision. Figure 5b: yeastnet score. Figure 5c: co-citation precision. Figure 5d: co-citation score. Figure 5e: GO precision. Figure 5f: number of associations.

Mentions: Additionally, only the rules with a span up to 4 time-delay units (W = 4) were inferred since we consider that this value is appropriated (regarding its magnitude) to assess the genome-wide scalability of the algorithm. However, in order to obtain meaningful time-lagged relationship between genes, the researchers are encouraged to follow the recommendation given by (5) considering their hypothesis about the time-delayed regulations that may be present in the experiments. The SCP parameter was fixed in 0.95 following the suggested criterion as the objective is to analyze the behavior of the algorithm varying the proportion of datasets that support the rules. Each run took 30 min of execution on a six core processor with 8 gb of ram. As regards the results, Figure 5 shows the precision and score metrics on the reference sets and the number of associations achieved by GRNCOP2 in each run. The points of the upper-right corner of the figures (where the Accuracy and the RCA parameter get closer to 1) are omitted since the algorithm was unable to obtain any rule with those parameter values. The details of each run are available in the additional file 2.


Discovering time-lagged rules from microarray data using gene profile classifiers.

Gallo CA, Carballido JA, Ponzoni I - BMC Bioinformatics (2011)

Precision and score values achieved by GRNCOP2 with different Accuracy and RCA parameters. Values of the precision and score metrics achieved by GRNCOP2 with the Accuracy and RCA parameters varying from 0.70 to 1 and from 0.60 to 1 respectively, with the SCP parameter fixed in 0.95 and with W = 4. The number of associations is also showed. Figure 5a: yeastnet precision. Figure 5b: yeastnet score. Figure 5c: co-citation precision. Figure 5d: co-citation score. Figure 5e: GO precision. Figure 5f: number of associations.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3111372&req=5

Figure 5: Precision and score values achieved by GRNCOP2 with different Accuracy and RCA parameters. Values of the precision and score metrics achieved by GRNCOP2 with the Accuracy and RCA parameters varying from 0.70 to 1 and from 0.60 to 1 respectively, with the SCP parameter fixed in 0.95 and with W = 4. The number of associations is also showed. Figure 5a: yeastnet precision. Figure 5b: yeastnet score. Figure 5c: co-citation precision. Figure 5d: co-citation score. Figure 5e: GO precision. Figure 5f: number of associations.
Mentions: Additionally, only the rules with a span up to 4 time-delay units (W = 4) were inferred since we consider that this value is appropriated (regarding its magnitude) to assess the genome-wide scalability of the algorithm. However, in order to obtain meaningful time-lagged relationship between genes, the researchers are encouraged to follow the recommendation given by (5) considering their hypothesis about the time-delayed regulations that may be present in the experiments. The SCP parameter was fixed in 0.95 following the suggested criterion as the objective is to analyze the behavior of the algorithm varying the proportion of datasets that support the rules. Each run took 30 min of execution on a six core processor with 8 gb of ram. As regards the results, Figure 5 shows the precision and score metrics on the reference sets and the number of associations achieved by GRNCOP2 in each run. The points of the upper-right corner of the figures (where the Accuracy and the RCA parameter get closer to 1) are omitted since the algorithm was unable to obtain any rule with those parameter values. The details of each run are available in the additional file 2.

Bottom Line: The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge.In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations.The method was carefully validated with several publicly available data sets.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratorio de Investigación y Desarrollo en Computación Científica (LIDeCC), Departamento de Ciencias e Ingeniería de la Computación, Universidad Nacional del Sur, Av, Alem 1253, 8000, Bahía Blanca, Argentina.

ABSTRACT

Background: Gene regulatory networks have an essential role in every process of life. In this regard, the amount of genome-wide time series data is becoming increasingly available, providing the opportunity to discover the time-delayed gene regulatory networks that govern the majority of these molecular processes.

Results: This paper aims at reconstructing gene regulatory networks from multiple genome-wide microarray time series datasets. In this sense, a new model-free algorithm called GRNCOP2 (Gene Regulatory Network inference by Combinatorial OPtimization 2), which is a significant evolution of the GRNCOP algorithm, was developed using combinatorial optimization of gene profile classifiers. The method is capable of inferring potential time-delay relationships with any span of time between genes from various time series datasets given as input. The proposed algorithm was applied to time series data composed of twenty yeast genes that are highly relevant for the cell-cycle study, and the results were compared against several related approaches. The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge. Additionally, a genome-wide study on multiple publicly available time series data was performed. In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations.

Conclusions: A novel method for inferring time-delayed gene regulatory networks from genome-wide time series datasets is proposed in this paper. The method was carefully validated with several publicly available data sets. The results have demonstrated that the algorithm constitutes a usable model-free approach capable of predicting meaningful relationships between genes, revealing the time-trends of gene regulation.

Show MeSH