<abstract>
<p>Aiming at the problems of the wet experiment method in identifying the types of conotoxins, such as the complexity, low efficiency and high cost, this study proposes a method that uses the sequence information of the conotoxin peptides combined with long short term memory networks (LSTM) models to predict the Methods of spirotoxin category. This method only needs to take the conotoxin peptide sequence as input, and adopts the character embedding method in text processing to automatically map the sequence to the feature vector representation, and the model extracts features for training and prediction. Experimental results show that the correct index of this method on the test set reaches 0.80, and the AUC value reaches 0.817. For the same test set, the AUC value of the KNN algorithm is 0.641, and the AUC value of the method proposed in this paper is 0.817, indicating that this method can effectively assist in identifying the type of conotoxin.</p>
</abstract>
The perioperative preoperative evaluation occupies an important guiding position in the perioperative process, but the data to be evaluated has the characteristics of clutter, inefficiency, and high redundancy, and the manual evaluation effect is difficult to guarantee. This article
proposes the concept of constructing a regional perioperative pre-operative evaluation platform, collecting medical data in a certain spatial area, and using data mining technology to mine hidden associations in medical data to provide a reference for pre-operative evaluation. We propose a
verification mining method based on the original frequent item mining technology, which greatly improves the mining speed. At the same time, to protect privacy, when publishing data in the FP-tree mode, we count the support of decision attributes and the order of item set. The experimental
results show that while satisfying the privacy protection, it has high mining accuracy and has certain clinical feasibility.
Background: Conotoxin is a valuable peptide that targets ion channels and neuronal receptors. The toxin has been proven to be an effective drug for treating a series of diseases, but the process of identifying the type of toxin through traditional wet experiments is very complicated, low efficiency and high cost, but the method of machine learning is used to identify the cono toxin. Training in the process can effectively change this status quo.Methods: A method to predict the type of spiral toxin using the sequence information of the toxin combined with the long-term short-term memory network (LSTM) method model. This method only needs to take the conotoxin peptide sequence as input, and uses the character embedding method in text processing to automatically map the sequence to the feature vector representation, and extract the features for training and prediction.Results: Experimental results show that the correct index of this method on the test set reaches 0.80, and the AUC (area under the ROC curve) value reaches 0.817. For the same test set, the AUC value of the KNN algorithm is 0.641, and the AUC value of the method proposed in this paper is 0.817.Conclusions: The algorithm does not require manual feature extraction and feature reconstruction steps, thereby simplifying the algorithm design, and can use the advantages of the long-term dependence of LSTM according to the characteristics of the cono toxin sequence, so that its classification can be better predicted, and the classification of the cono toxin can be better predicted. The sequence information of spirotoxin combined with the LSTM method can be better than the KNN classification algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.