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Thus, we can understand why the incorrect bond lengths in these structures cannot be handled by our algorithm because they are beyond the conventional values defined in our algorithm.
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This is decided dynamically by our algorithm.
We suspect inadequate refinement of the ligand geometry because the same functional groups in the other entries can be correctly perceived by our algorithm.
Fig. 3 and 4 are predicted networks by our algorithm.
Our assumption is that, because high-ranking cliques consist of the TFs deemed to function cooperatively in multiple instances by our algorithm, they may truly function as partners in real biological settings.
We compare our results using two criteria: free energy level obtained by our algorithm and that obtained by other algorithms, and the root mean square deviation (RMSD) of the structures obtained by our algorithm and that obtained by other algorithms.
Our analysis also reveals that the important genes (IL1A, IL1B, IL1R, IL6, TNFA, IL17, IL8, IL1R, TLR4 and TNFR) detected by our algorithms are vital for the inflammatory response because they are more connected during inflammation than in normal conditions.
As a by-product, our algorithm also inferred individuals' diplotypes.
The R in the table gives the percentage by which our algorithm is better than others; the value is negative because better performance gave smaller SNRLOSS measure.
They were missed by our CNV calling algorithm because of their complex nature: for example, presence of overlapping or closely neighboring CNVs in both parents in the CNV region (Additional file 10C).
We are interested in the iterative formula of the generalized inverse, because it is used by our new algorithm.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com