The majority of extracted strings are likely to be bona fide DNA sequences, since out of 3443 articles published before 1960 [i.e. prior to the advent of nucleic acid sequencing (Holley et al., 1965)], only one article contains a sequence-like string (which was caused by an OCR error).
The straightforward way to extract binary strings is quantization and coding of the real-valued features.
In the Yale Image Finder, Xu et al. [24] developed algorithms to extract text strings from figures and provided a figure searching tool that queries over the extracted figure text as well as caption text.
In order to test these ideas, we used APPIA to extract those STRING entries with higher scores from a set of 240,885 protein pairs above the minimal STRING confidence value of 0.15.
To further validate our method, we manually inspected a randomly selected 1% subset of the nucleotide strings and found that all were valid DNA sequences, implying that most extracted strings represent true DNA sequences.
Finally I will explain how to differentiate cosmic superstrings from conventional vortex cosmic strings, and how this can be used to extract information about string parameters and extra dimensions.
Our assumption for using the path string method to extract web data is that the path string for schema data and for real data share little in common.
There are many alternatives to extract existing domain knowledge, e.g., STRING, HPRD, or manual construction.
Comparative protein inventories of sequenced genomes were first assessed using a string comparison to extract matches at the 100% identity level.
For 8,098 terms, it is possible to extract specific modules from the STRING network, containing a total of 33,315 proteins (see "Module extraction" section for details).
By applying Algorithm 2 on each type of page (list page, detail page, and profile page) we are able to extract a group of characteristic path strings for each type.
