Sentence examples for bias the datasets from inspiring English sources

To avoid any additional bias, the datasets were previously modelled [28] and the predictions were used as provided at the OChem QSAR modelling repository (https://ochem.eu/home/show.do).eu/home/show.do

In an effort to minimize bias, the datasets were analysed using three different statistical approaches to identify the most stably expressed genes during grape berry development.

Just like the outliers can skew the entire data set, a little tweaking of the raw data can bias the dataset into a wrong prediction and misrepresentation.

However the presence of outliers (i.e., noise) may bias the dataset to the point of compromising the ability of machine learning algorithms to build predictive models.

To avoid potential biases, the datasets were filtered according to the status code provided by the Cufflinks and the FPKM value; all expression levels for a specific gene among samples were reliable (status code is OK), and the average of the gene's FPKM among samples was greater than 2. In total, 11,969 genes were detected as expressed in at least one of the samples (see Table S1).

IBM researchers have been busy coming up with ways to reduce bias in the datasets used to train AI machine learning systems.

We also recognised a potential anatomical bias in the datasets derived from brain regions with high neuronal loss.

Methodological variations in generation of the underlying cDNA sequences, e. g. 5'-, 3'- or full length cDNA enrichment, may have led to additional bias in the datasets.

Then, XPN was used in serial increments to bring the five cohorts into uniform agreement by removing persistent systematic bias between the datasets.

To determine whether enrichment introduced any bias into the datasets, we analyzed non-enriched and enriched samples of two independent biological infection replicates.

Although those studies performed a profound analysis on a few datasets, we think a superficial comparison on a large number of experimental datasets is more important than a profound one on a few experimental datasets when estimating the methods' practical ability to detect DEGs, as the superficial comparison on a large number of datasets can also prevent selection bias regarding the datasets.