DNA Sequence Specificity Prediction Algorithm Based on Artificial Intelligence
Mathematical Problems in Engineering, ISSN: 1563-5147, Vol: 2022, Page: 1-8
2022
- 8Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures8
- Readers8
Article Description
DNA sequence specificity refers to the ability of DNA sequences to bind specific proteins. These proteins play a central role in gene regulation such as transcription and alternative splicing. Obtaining DNA sequence specificity is very important for establishing the regulatory model of the biological system and identifying pathogenic variants. Motifs are sequence patterns shared by fragments of DNA sequences that bind to specific proteins. At present, some motif mining algorithms have been proposed, which perform well under the condition of given motif length. This research is based on deep learning. As for the description of motif level, this paper constructs an AI based method to predict the length of the motif. The experimental results show that the prediction accuracy on the test set is more than 90%.
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