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The results shown in Fig. 5 for real datasets highlight the difference from the results conducted in [21] for 9 heuristics with (n = 10000) using uniform distributed datasets.
Empirical results obtained on several real-world text datasets highlight the advantages of the proposed model and the corresponding co-clustering algorithms.
The discrepancies identified in these datasets highlight two main observations regarding the LCM process and resulting expression data.
The limitations inherent in different datasets highlight the need to continue rely on multiple information sources for complete injury surveillance information.
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When properly calibrated, the three sensor sets capture trends in the data quite well, with slight variations between different datasets highlighting the importance of synergistic use of the instruments to determine the most accurate measurement when disagreements arise.
Ensemble refinement of 20 protein datasets highlighted global dynamics features of protein molecules.
All three datasets highlighted that our approach could lead to groups with similar expected behavioural changes.
A number of immune/inflammation-associated genes were altered in the two datasets (highlighted in Additional file 2: Table S2).
A comparison of published datasets highlights the surprisingly high variability in the APs measured during experiments in hiPSC-CMs.
Our comparison of ChIP-chip datasets highlights the importance of data quality for correct identification of protein-DNA interactions.
The likelihood that a frequent co-splicing cluster is biologically meaningful increases with its recurrence across multiple datasets, highlighting the importance of the integrative approach.
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