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In this paper, we present a new evaluation system for PPI datasets that can distinguish true interacting protein pairs from noisy datasets.
In this system, we use an alternating decision tree algorithm [27] that dynamically selects discriminating features among various attributes related with PPIs and trains an interpretable classifier that can distinguish true interacting protein pairs with confidence scores from noisy datasets.
As the expression profiling technologies mature, the identification of significant cancer-related signals from noisy datasets (characterized by a high CV) remains a major challenge.
The distribution of calculated mRNA specific translation frequencies that would be expected if all frequencies were identical in reality, but were calculated from noisy datasets, is shown in the histogram in figure 4 in open circles.
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Bayesian probabilistic models such as LDA are well suited for the inference of robust associations from potentially noisy datasets [ 82, 83].
Contradictory data undermines the soundness of the information from a noisy dataset.
Also, database management systems such as Oracle and MySQL provide platforms for retrieving contradictory data from a noisy dataset.
However, the removal of contradictory data from a noisy dataset will increase the incompleteness in the dataset thereby reducing the soundness of any information from such set of data.
ConTra graphically represents contradictory data from a noisy dataset by mining the contradictory data in the investigated dataset and enabling the visualisation of such data in a pie chart.
The difficulties in bridging the gap between packages for CNV prediction, association analysis and visualization for distinguishing genuine signals from false positive/negative predictions in generally noisy datasets have hindered many scientists from conducting genome-wide CNV analysis of their existing large GWAS datasets.
Finally, we performed experiments using datasets with and without noise to demonstrate the effectiveness of the proposed method in several applications, including dynamic-response noisy datasets obtained from a magnetorheological damper.
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