Exact(8)
This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.
For detailed materials and methods, see Supporting Methods in Text S1.
Supporting information include supporting data with five figures and two tables, and supporting methods in Text S1.
Other statistical methods in text S1 section 2. We thank Uri Alon for comments on an earlier version of the manuscript.
We also showed that recombinant segments were randomly distributed in the M66-2 aN16961961 lineages, and those attributed to the O/MN divergence are probably also randomly distributed, but there is some distortion probably because with 32% of the genome affected, some recombinant segments overlap (see Supporting Methods in Text S1).
We excluded PC2 and upper mandible length from these and subsequent analyses, because these traits were not significantly associated with percent tree cover the environmental feature with which we define the rainforest-savanna gradient (Table S1; additional methods in Text S1).
As predicted from our analysis of individual gene sequences, we could distinguish recombinant segments with a relatively higher frequency of base substitutions, and developed a formal procedure for identifying recombinant segments in the genome (for details see Supporting Methods in Text S1; Figure 4 and Figure S1).
Annotated collections of documents are crucial components for developing new methods in text mining, such as extraction of named entities and relationships from the scientific literature.
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