Cai Zhuo

TL;DR: In this paper, a drug target affinity prediction method based on deep learning was proposed, which relates to the technical field of drug target affinities prediction, and consists the steps of: obtaining a drug compound and target protein data from a Davis data set and a KIBA data set; encoding the compound, and representing the protein by using a position specificity scoring matrix; inputting a compound label code into a CNN model, and performing feature extraction on the compound to obtain molecular representation of the compound; and learning an order relationship between amino acids in a protein sequence.

TL;DR: In this paper, a traditional Chinese medicine chemical component naming entity identification method and a device are presented, which comprises the following steps of S1, acquiring traditional Chinesemedicine chemical component NER related literatures; S2, performing information filtering on the acquired related literature for obtaining a corpus with a standard text content; S3, performing coding and marking on the corpus, thereby obtaining a marked corpus; S4, training the marked corpus as the training sample of the BiLSTM, and S5, inputting the to-be-identified traditional Chinese

TL;DR: Wang et al. as mentioned in this paper presented a method and system for identifying traditional Chinese medicine pharmacological actions based on machine learning and text rules, which consists of constructing a training corpus based on a BIO rule, extracting and digitizing text features, and constructing a pharmacological action identification model by using multi-classification SVM; and finally, post-processing an annotation result output by the SVM model using a rule-based error-driven learning(TBL) method to improve the accuracy of entity recognition.

TL;DR: Wang et al. as mentioned in this paper proposed a deep belief network-based method to identify adverse effects of traditional Chinese medicine entities, which can be used for adverse effect identification and relationship mining for specific traditional Chinese medicines.

TL;DR: In this article, a traditional Chinese medicine planting resource named entity identification method is proposed, which consists of the following steps: S1, acquiring literatures of the traditional Chinese medicinal material planting resources; S2, labeling the text literature according to a certain rule, and splitting the labeled literature into text sentences; S3, respectively searching a word vector and a character vector corresponding to each text sentence one by one, and training a GRU-CRF model by utilizing the word vectors and the character vectors.