Sentence examples for estimate the alignment from inspiring English sources

Despite the lack of a manually curated correct alignment, we can estimate the alignment accuracy by modeling evolution and aligning simulated data sets.

The experimental results show that this method is able to estimate the alignment parameters reliably.

In addition, we used BAli-Phy 2.0.2 [51] to simultaneously estimate the alignment and phylogeny of the each species' OBPs in a Bayesian framework [52].

Because mis-alignments of similar length have similar alignment scores (within noise), we use seven alignments of similar length of the same mate-pair to calculate a baseline, and divide by the range of the scores to estimate the alignment's significance.

CNFpred integrates as much information as possible in order to estimate the alignment probability of two residues.

We estimate the alignment confidence score by iteratively passing messages between neighboring symbol pairs, where each symbol pair (x i, y j ) corresponds to a potential symbol alignment in the true (unknown) sequence alignment between x and y.

Second, we estimated the alignment between the (distorted) reference EPI frame (see Preprocessing section) and the undistorted, partial FOV, high-resolution T2*-weighted volume.

The pair-HMM provides a simple, yet very effective, mathematical framework for estimating the alignment probability between symbols in different biological sequences.

For each reference set, we estimated the average SN, PPV, and CPU time (for estimating the alignment scores/probabilities) of different alignment schemes based on all possible pairwise sequence alignments: 943 alignments for the reference set RV11, 2,335 alignments for RV12, 50,062 alignments for RV20, 76,370 alignments for RV30, 23,445 alignments for RV40, and 7,538 alignments for RV50.

The weight parameter λ ∈ [0, 1] is used to balance the contribution from the neighbors and that from the joint probability of (x i, y j ) in estimating the alignment confidence score.

BAli-Phy [ 85] avoids this problem by not conditioning on a single guide tree but instead finding the multiple alignment with the highest posterior probability by estimating the alignment and tree topology simultaneously using a Markov Chain Monte Carlo (MCMC) sampler.