By Bin Ma (auth.), Zhipeng Cai, Oliver Eulenstein, Daniel Janies, Daniel Schwartz (eds.)
ISBN-10: 3642380352
ISBN-13: 9783642380358
ISBN-10: 3642380360
ISBN-13: 9783642380365
This booklet constitutes the refereed lawsuits of the ninth overseas Symposium on Bioinformatics examine and purposes, ISBRA 2013, held in Charlotte, NC, united states, in might 2013. The 25 revised complete papers offered including four invited talks have been rigorously reviewed and chosen from forty six submissions. The papers hide quite a lot of biomedical databases and knowledge integration, high-performance bio-computing, biomolecular imaging, high-throughput sequencing info research, bio-ontologies, molecular evolution, comparative genomics and phylogenomics, molecular modeling and simulation, trend discovery and type, computational proteomics, inhabitants genetics, facts mining and visualization, software program instruments and applications.
Read Online or Download Bioinformatics Research and Applications: 9th International Symposium, ISBRA 2013, Charlotte, NC, USA, May 20-22, 2013. Proceedings PDF
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Extra info for Bioinformatics Research and Applications: 9th International Symposium, ISBRA 2013, Charlotte, NC, USA, May 20-22, 2013. Proceedings
Example text
To simulate such reactions, the probability P (R) that a reaction occurs upon a collision between the species A and B has to be determined. e. P (R) = 1, when all the collisions between the reagents lead to the creation of a product. The corresponding maximum rate constant kmax can easily be computed by using our simulator and by fitting the concentrations with the solution of the mass action law equation. It is worth noting that the values we obtain for different simulations are rather consistent with Von Smoluchowski theory [1] which states that the maximum rate constant is kmax = 4π(DA + DB )(rA + rB )NA , where DA and rA are respectively the diffusion coefficient and the radius of the species A (likewise for B), and NA is the Avogadro number.
The least common ancestor (lca) reconciliation λSG is a map from V (G) to V (S) defined as: λSG (u) = v such that L(v) = L(u) lca{λSG (x) if u ∈ LV(G), ◦ : x ∈ LV(G(u))} if u ∈ V (G), (1) where lca{· · · } denotes the most recent common ancestor of the species in the set. Clearly, for g ∈ V (G) with k children gi , λSG (g) = lca{λSG (gi ) : i ≤ k}. We shall write λ when no confusion is likely to arise after G and S are dropped. ◦ Assume that both G and S are binary. A node u ∈ V (G) is a duplication node if λSG (u) = λSG (u ) for some child u of u.
3 Comparing the Performance with Parsimonious Methods Parsimonious methods are in general time-consuming but very accurate. Their performances are sometimes referred as the upper bound of all methods [21], but such methods (GRAPPA for example) that directly optimize for the exact solution of the genome median problem suggested by Blanchette et al. [22] are NP-hard. We compared the performance of PMAG, InferCarsPro and one direct optimization method GRAPPA with Xu’s ASMedian solver [9] (GRAPPA-DCJ).
Bioinformatics Research and Applications: 9th International Symposium, ISBRA 2013, Charlotte, NC, USA, May 20-22, 2013. Proceedings by Bin Ma (auth.), Zhipeng Cai, Oliver Eulenstein, Daniel Janies, Daniel Schwartz (eds.)
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