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The details of the IASPLS algorithm to classify short prokaryotic genes are described in the methods section.
In this study we (1) presents the improved prediction results of IASPLS to classify short prokaryotic genes (2) describes the IASPLS algorithm in details.
(i) CLARK is able to classify short metagenomic reads with high accuracy at multiple taxonomic ranks (i.e., species and genus level) and its assignments on real metagenomic samples are consistent with findings published in the literature.
TETRA is a DNA-based fingerprinting technique for genomic fragment correlation based on tetranucleotide usage pattern, while MetaCV is an algorithm based on composition and phylogeny to classify short metagenomic reads (75 100 bp) into specific taxonomic and functional groups.
Because the 75th percentile for doctor delay was only 2 days (see above), we used a cut-off of 30 days (corresponding to the 91st percentile) to classify short versus long doctor delay.
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The proposed classification method, on the other hand, has the capability to classify short-time windows, segments or the entire frame (spectrogram).
Our research tackled the problem of classifying short texts.
The algorithm comprises three stages: The first stage classifies short frames of the noisy speech as speech-dominated or noise-dominated.
Our approach for event detection will include a step of context recognition by classifying short intervals, before the main step of event detection.
Classifying short texts to one category or clustering semantically related texts is challenging, and the importance of both is growing due to the rise of microblogging platforms, digital news feeds, and the like.
This simple and effective algorithm classifies short text segments (such as paragraphs) from heterogeneous text corpora gathered from various resources to the in-domain and out-of-domain data.
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