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Existing computational alternatives often require replicate genotyping as well.
Computational alternatives can be broadly divided into two subgroups: supervised and unsupervised.
In general, the computational alternatives currently available can be broadly divided into two: supervised and unsupervised methods.
However, both of these computational alternatives have no built-in method to set a threshold for signal assignments.
However these approaches are either time-consuming or resource-intensive, and computational alternatives are highly valuable and may aid in characterization of whole genome sequence interpretation [ 9].
Based on the analysis of AS signals we report and the comparison of our method to standard computational alternatives, we believe this work will facilitate the study of future high-throughput AS datasets, extending our understanding of the transcriptome complexity.
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Some progress has been made to find a computational alternative to replace these physical tests.
Recently, efforts have been made to realise a reliable computational alternative to these physical tests.
We also briefly discuss the spectral norm as a computational alternative to the Frobenius norm in estimating approximation errors of tensor ID.
Independently of the shape of the triangles, we show that the three-coarsening strategy is a good computational alternative to standard coarsening.
In this paper, we present a purely computational alternative by using inverse imaging [ 18].
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