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. 2019 Nov 30;18:20–26. doi: 10.1016/j.csbj.2019.11.004

Fig. 2.

Fig. 2

The skip-gram word embedding model. Lnc2vec and pro2vec model were trained by using this model and genome-wide human lncRNA and protein sequences. Skip-gram is trained by predicting words surrounding the central word, after training, the weights matrix W of the hidden layer is obtained, that is word vectors.