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Key Points Gene fusion analysis is one of the most successful computational methods for the detection of genome-wide protein interactions.
However, the most successful computational methods use some combination of these and other features (Cho et al., 2009; Darnell et al., 2007; Guney et al., 2008; Lise et al., 2009; Tuncbag et al., 2009; Zhu and Mitchell, 2011) and achieve accuracies of 60 80%.
The use of profile hidden Markov models (HMMs) that capture the conserved sequence features of protein domains [ 7, 13, 14] is arguably the most successful computational approach for identifying protein domains, and the Pfam (Protein Families) database is the premier repository, currently containing 13,672 protein domain models in its high-quality, curated Pfam-A part [ 15].
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Arguably the most successful set of computational tools for ncRNA exploration have been developed for the problem of ncRNA validation.
The "efficient coding" hypothesis has been among the most successful of all computational approaches in explaining the function of the nervous system [6], [41].
We analyse the stability, accuracy and dissipativity of the time integrators and demonstrate that the most successful methods yield a substantial gain in computational efficiency as compared to classical explicit Runge Kutta methods.
In the genomic era machine learning algorithms that improve automatically through experience have proven to be among the most successful methods for addressing relevant problems of Computational Molecular Biology, including protein structure prediction.
The most successful attempt to design a standard format in computational chemistry is based on introduction of Chemical Markup Language (XML/CML) [13,14].
The two-part MDL, despite its very low computational complexity, is among the most successful methods for source enumeration in array processing.
People that are most successful at this primarily started out studying biology but also had an interest in acquiring some sophistication in computational methods.
Most successful companies do this.
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