By Faisal Rezwan, Yi Sun, Neil Davey, Rod Adams, Alistair G. Rust, Mark Robinson (auth.), Clara Pizzuti, Marylyn D. Ritchie, Mario Giacobini (eds.)
This ebook constitutes the refereed complaints of the ninth eu convention on Evolutionary Computation, laptop studying and information Mining in Bioinformatics, EvoBIO 2011, held in Torino, Italy, in April 2011 co-located with the Evo* 2011 occasions. The 12 revised complete papers awarded including 7 poster papers have been conscientiously reviewed and chosen from various submissions. All papers incorporated themes of curiosity comparable to biomarker discovery, mobilephone simulation and modeling, ecological modeling, fluxomics, gene networks, biotechnology, metabolomics, microarray research, phylogenetics, protein interactions, proteomics, series research and alignment, and structures biology.
Read or Download Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics: 9th European Conference, EvoBIO 2011, Torino, Italy, April 27-29, 2011. Proceedings PDF
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Additional info for Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics: 9th European Conference, EvoBIO 2011, Torino, Italy, April 27-29, 2011. Proceedings
Each increase in scope beyond 6 signiﬁcantly decreases performance. 34 S. Quader et al. 40% Multiple-ML-Consensus-IC-PS-2 Multiple-ML-Consensus 75% ML-Consensus-IC-PS-2 Multiple-ML-Consensus True Positive Rate True Positive Rate Multiple-ML-Consensus-IC-PS-2 ML-Consensus-IC-PS-2 ML-Consensus ML-Consensus 20% 55% 0% 35% 0% 1% 2% False Positive Rate 0% 3% Fig. 4. Multiple ML-Consensus on S. cerevisiae 1% 2% False Positive Rate 3% Fig. 5. Multiple ML-Consensus on H. sapiens 40% ML-Consensus ML-Consensus Multiple-ML-Consensus Sorted-Multiple-ML-Consensus 60% Sorted-ML-Consensus True Positive Rate True Positive Rate Multiple-ML-Consensus Sorted-ML-Consensus Sorted-Multiple-ML-Consensus 20% 0% 40% 0% 1% 2% 3% False Positive Rate Fig.
PPV and Se values returned by the considered methods on the switch on data. Results of BANJO, NIR and TSNI calculated on the networks found by these methods as reported in . 084 we can see that the best PPV and Se values found by GRNGen outperform BANJO, NIR and TSNI. Median and average PPV values are outperformed by NIR and TSNI, while they outperform BANJO. Finally, median and average Se values outperform all frameworks studied in . The best networks found by GRNGen are shown in Figure 4, where Figure 4(a) shows the network with the best PPV obtained using the switch off data, Figure 4(b) shows the network with the best Se on the switch off data, Figure 4(c) shows the network with the best PPV on the switch on data and Figure 4(d) shows the network with the best Se on the switch on data.
Moreover the full power of GP as a regression method can be better exploited by considering non-linear activation functions that could describe threshold and saturation effects in gene regulation. The application of GRNGen to large genetic networks will require some preprocessing of the data since the regression problem is underdetermined in the likely scenario in which the number of genes is much larger than the number of available time-points. This problem could be solved either by limiting the genes involved in the activation function to known transcription factors or by preprocessing the expression data to identify clusters of coexpressed genes and then running GRNGen on the clusters rather than on the genes, based on the reasonable assumption that coexpressed genes should have similar activation functions.