Sequence alignment was first performed by a web-based program (http://bioinfo.genopole-toulouse.prd.fr/multalin/) [50] and the machine alignment was then refined manually.
Since strictly secondary-structure-based alignment for the two rRNA genes was impractical for the large dataset, we employed machine alignment instead, which would minimize erroneous assessment of the positional homology of the rRNA molecules.
The cross-machine alignment of PEDOT PSS and P3HT PCBM layers were shifted closer to the edge of ITO on the left side of the leaf although the deviations were within the specified tolerance range providing an acceptable alignment.
The machine alignments were then refined manually.
MoRFpred predicts sequences that undergo disorder-to-order transitions of all types of MoRFs (α, β, coil, and complex) using a combination of sequence alignment and machine learning predictions based on amino acid properties, predicted disorder, B-factors, and solvent accessibility.
In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM-) LZ complexity pairwise algorithm.
Using support vector machines with multiple alignments and secondary structure predictions from PSIPRED [20], Zhang et al. obtained a Matthews correlation coefficient of 0.45, which was slightly higher than the E-SSpred method.
These filtration steps are necessary to ensure that machine errors and alignment errors do not bias the conclusions drawn from the data and the resulting set of high quality bases was used for all subsequent downstream analysis discussed below.
The main contribution of RAF, however, is its application of discriminative machine learning techniques for parameter estimation to the problem of simultaneous alignment and folding.
The architecture consists of systolic arrays to speed up the data intensive sequence alignment and structure prediction steps, and finite state machines for the control dominated steps.
