Sentence examples for propensities based on from inspiring English sources

The PPCpred outperforms the existing solutions in both the binary prediction (based on the MCC and ACC scores) and the real-valued propensities (based on the AUC values).

We calculated the propensities based on the complete sequences that were cloned and tested experimentally in this work, which we believe, is more credible than using the predicted PrD-cores, which are inferred solely based in statistical precepts.

Summarizing, at a given time t we compute, as described above, the time-dependent propensities based on the volume of the cell at that time and, according to those, we determine the time at which the next reaction takes place, t + △ t, following the standard Gillespie algorithm.

Finally we compare our findings with previous assessments of patent propensity based on several sources of data.

Hence, earlier TMH prediction programs, such as TOP-PRED [1], compute sequence hydrophobicity from amino acid hydrophobicity scales assigned by biophysical and chemical measurements [2] [4], and predict TMH propensity based on the average hydrophobicity score of a sliding prediction window of N successive residues along the sequence.

Mostly, MSM are treated as a homogenous group and therefore the varied risk propensity based on contextual factors are often ignored [ 11].

Here, MTMDAT takes all the previously produced time-course plot files and calculates the relative cleavage propensity based on the algorithm in Hennig et al. (2005) (an example of such a plot is shown in Fig. 1).

Previous work from the Widom and Segal labs measured nucleosome occupancy obtained upon packaging yeast genomic DNA into nucleosomes in vitro, and then developed an algorithm based on the results to predict relative nucleosome-forming propensity based on DNA sequence (Kaplan et al. 2009).

These results strongly suggest that the differential response of memory effect and transcriptionally responsive promoters to loss of Abf1 binding is due at least in part to memory effect promoters showing a reduced propensity, based on DNA sequence, to form a closed chromatin structure upon loss of Abf1 binding in abf1 ts yeast.

However, Cα, Cβ, Hα, and Hβ and chemical shifts for the C-termini of Aβ42 and Aβ43 from natural abundance H C HSQC spectra demonstrate no significant shift differences in resolvable nonterminal residues except a 0.2 ppm upfield Cα shift for I41, consistent with a slightly lower helical or higher extended/coil propensity based on refDB statistics for Ile.

For example, Bellucci et al. utilized lncRNA secondary structure propensities inferred based on sequence information to predict their associated proteins [ 20] and hence provide a potential way to predict functions of lncRNAs.