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When tested on 133 seizures from a public database, our method successfully detected the end of 132 seizures within 10.3 ± 5.5 seconds of the time determined by an electroencephalographer to represent the electrographic end of seizure.
On the LFW database, our method provides up to 76.33% recognition rate.
Although major differences were identified in how variables and claims were defined in each database, our method enabled us to link these two different databases to compile a complete and accurate assessment of healthcare use and costs for dual-eligible beneficiaries with a costly chronic condition.
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And while conventional KM documents its knowledge in databases, our method expresses the knowledge back on a representation which is easy for production people to comprehend: on a production flow chart.
The first is that given a query protein, SwissPfam and UniProtKB databases are used to extract domain architecture and the corresponding GO terms for each domain; if any GO term for the query protein is missed in these databases, our methods cannot predict this GO term.
After performing one similarity search in the cMap database by our method, the top 10 chemicals with highest scores were presented in Table 1.
In contrast, the SASD database in our method includes 11,919,779 Alternative Splicing peptides covering about 56,630 genes (ensembl gene IDs), 95,260 transcripts (ensembl transcript IDs), 1956 pathways, 6704 diseases, 5615 drugs, and 52 organs.
Extensive experiments have been conducted on three well-known texture databases, and our method achieves the best classification results compared with state-of-the-art approaches investigated.
Experimental results on CASIA database show that our method has a promising performance with an accuracy of 99.31%.
Evaluation results on the noisy-digit Aurora 2.0 database show that our method leads to significant performance improvement.
The evaluation, using the validation set derived from the curated KEGG pathway database, showed that our method performs better than several representative methods available to the public.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com