From: A semi–supervised tensor regression model for siRNA efficacy prediction
1 | To encode each siRNA sequence as an encoding matrix Xrepresenting the nucleotides A, C, G, and U at n positions in the sequence. Thus, siRNAs are represented as n×4 encoding matrices. |
2 | To transform encoding matrices by K transformation matrices T _{ k } into enriched matrices, k=1,…,K. Each transformation matrix characterizes the knockdown ability of nucleotides A, C, G, and U at n positions in the siRNA sequence regarding the kth design rule. Each T _{ k } captures background knowledge of the kth design rule. The enriched matrices of size K×n are considered as second order tensors of the siRNA sequences. |
3 | To build and learn a bilinear tensor regression model. In this step, K transformation matrices as wellas parameters of the model are learned together with the labeled and scored siRNAs and available siRNA design rules. The final model is used to predict the efficacy of new siRNAs. |