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In order to obtain these probabilities we collected, for each internal exon, the set of transcripts in the dataset that could have contained the exon, i.e. those transcripts that contain exons both upstream and downstream of the genomic location of the cassette exon in question.
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Prior to evaluating the heterozygosity of the samples, we pruned the SNP dataset to avoid bias that could have been introduced by large quantities of SNPs in strong LD.
Using a data-pruning strategy, we found good evidence that, as already remarked by Ray et al. [1], the dataset indeed had "suspicious" training samples, that could have the wrong diagnosis label or did not characterise their classes well due to other clinical factors.
Firstly, there are several genes as well as behavioural, clinical and cognitive phenotypes for BD that could have been examined in this dataset.
The Approximation Bayesian Computation (ABC) is another method to simulate the parameter values from demographic models that could have given rise to the observed dataset.
What that could have meant?
That could have worked elsewhere.
That could have two consequences.
"That could have been funny".
"That could have been us.
That could have gone anywhere.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com