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While the L and S methods identified periodic patterns oscillating in different phases, the P, A and C methods also identified a larger variety of patterns (illustrated by higher numbers of clusters).
Our methods also identified an important predictor of TLS that was otherwise omitted if relying on any of the automated model selection procedures alone.
Another group using bioinformatics methods also identified FABP-1 as a candidate molecule potentially associated with PaC, though again, no further validation was performed [ 32].
Although the genes that were identified by the machine learning methods shared some overlap, different methods also identified different genes; for example, DTNBP1 was only identified by J48.
Both methods also identified five other well-supported insect clades of Dscam-like genes, three of which clustered around one of the three D. melanogaster Dscam-like genes, and which we therefore named as Dscam2, Dscam3 and Dscam4 according to which of the three D. melanogaster genes they were orthologous to.
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The river chemistry method also identified spatial variability of flux, whereas the river temperature method did not have sufficient resolution.
The method also identified 39 chromosomal qnr variants in 33 bacterial species.
Our method also identified more known LTR retroelements than LTR_STRUCT in the D. melanogaster genome.
Besides the pheromone signaling pathway, our method also identified an alternative path from Gpa1 to Prp39 (GPrp39st2-MPrp39dc28-Cln2-Dig1-Prp39).
The ALTree method also identified three potential breast cancer susceptibility loci in mtDNA.
The method also identified outlier samples, which is not possible for grouped case-control studies.
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