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. Author manuscript; available in PMC: 2019 Sep 4.
Published in final edited form as: Cell Rep. 2019 Aug 6;28(6):1623–1634.e4. doi: 10.1016/j.celrep.2019.07.017

Figure 4. Machine Learning Predicts “Pain-like” Probability for Each Paw Withdrawal Reflex.

Figure 4.

A trained support vector machine (SVM) analyzed each behavior trial and output its probability of being pain-like.

(A) Graphical representation of the SVM process. Step (1): generate PCA1 eigenvalues from PCA datasets from Table 1. Step (2): calculate PC score of the PCA dataset. Step (3): train SVM with PC scores of fitting data (red circles). Step (4): predict pain-like probability (P [pain-like]) of all PC scores.

(B–E) Predictions made in CD1 males (B), CD1 females (C), C57 males (D), and C57 females (E) following training with CS and HP trials from CD1 males (outlined).

(F–I) Predictions made in CD1 males (F), CD1 females (G), C57 males (H), and C57 females (I) following training with CS and HP trials from CD1 females (outlined).

(J–M) Predictions made in CD1 males (J), CD1 females (K), C57 males (L), and C57 females (M) following training with CS and HP trials from C57 males (outlined).

(N–Q) Predictions made in CD1 males (N), CD1 females (O), C57 males (P), and C57 females (Q) following training with CS and HP trials from C57 females (outlined).

n = 10 animals for all groups.