The solution presented is based on the theory that when two model systems in vitro AT II cultures and an in silico analogue are composed of components for which similarities can be established, and the two systems exhibit multiple attributes that are similar, then there may also be similarities in the generative mechanisms responsible for those attributes.
At the other end is intertextual analysis, in which similarities between words, phrases and sentences are the determining factor.
The decomposition of objects into primitives is used, across which similarities between past observations and new unknown objects can be made.
Research into the construct validity of the STRS showed statistically significant correlations with other scales with which similarities could be expected.
More generally, in connection with the all-important (G7): how do we determine which similarities and differences are relevant to the conclusion?
Words could be said to have, rather than "meaning", a semantic potential, defined as the collection of past uses of a word w on the basis of which similarities can be established between source situations (i.e., the circumstances in which a speaker has used w) and target situations (i.e., candidate occasions of application of w).
It can be seen that in the current dataset there is not enough data to explore the extent to which similarities in transporter sequences determine similarities in compounds transported, since having a more complete data matrix would be required a selection of compounds tested against a selection of transporters in an all-to-all fashion would be ideal.
Nevertheless, defining the nature, extent and mix of which similarities bear most directly on function remains a challenge [1].
CuniNPV was particularly dissimilar, of which similarities were 15.1%51.1%% range and average of 24.3%.
The only other organism to which similarities over 94% nucleotide identity to protein coding genes were found.
Tracing references and isolating a point at which similarities become apparent (as shown above) is all very well.
