For the three selected methodologies we show the resampling distributions for the RMSE and R2 statistics.
To demonstrate the feasibility of our methodology, we showed that is it possible to obtain accurate and reliable P- and S-wave ultrasonic velocities measurements and the Thomsen parameters (ε and γ) related to them, even if the sample presents a high fracture density, due to the high quality of the acquired waveform, expressed in a high signal-to-noise ratio.
While developing the promoter knock-in methodology, we showed the importance of conserving the natural leader region containing the ribosome binding site (RBS) of the gene of interest and of eliminating upstream regulatory elements (transcription factor binding sites).
By this methodology, we show that the moments provide a concise summary of the distribution, showing statistically-significant differences across much of the feasible parameter range.
Moreover, using our methodology, we show that indel parameters substantially vary between three genomic data sets: Mammals, bacteria, and retroviruses.
In terms of methodology, we show that high-quality de novo assembly of transcriptomes can be achieved using Illumina sequencing technology with the Trinity assembly pipeline.
By combining the reporter system with virus-based tracing methodology, we show that the new neurons integrate into local circuitry in an efficient manner.
Using the same methodology, we recently showed a complete lack of T-cell recognition of oncogenic proteins from the Merkel cell polyoma virus in a healthy donor cohort, while these were frequently recognized in a Merkel cell carcinoma cohort.
Indeed, we showed our methodology to be, in general, independent of the classifiers utilized, giving even results for different classification heuristics.
