ConvNTC: Convolutional neural tensor completion for predicting the disease-related miRNA pairs and cell-related drug pairs

Abstract

Systematic investigation of high-order molecular interactions can deepen our understanding of the mechanisms underlying biological systems. However, effectively capturing both multilinear and nonlinear relationships to accurately identify the complex triplet relationships remains a challenge. In this paper, we present a novel Convolutinal Neural Tensor Completion (ConvNTC) to model triplet network connections. ConvNTC consists of a multilinear module and nonlinear module. The former is a tensor decomposition approach that integrates multiple constraints to learn the tensor factor embeddings. The latter contains three components: an embedding generator to produce position-specific index embeddings for each tensor entry in addition to the factor embeddings, a convolutional encoder to perform nonlinear feature mapping while preserving the tensors rank-one property, and a Kolmogorov-Arnold Network (KAN) based predictor to effectively capture high-dimensional relationships aligned with the intrinsic structure of real-world data. We demonstrate ConvNTC on two triplet prediction tasks of the disease-related miRNA pairs and cell-related drug pairs, respectively. Comprehensive experiments against ten state-of-the-art methods demonstrate the superiority of ConvNTC in terms of triplet imputation. ConvNTC reveals promising prognostic values of the miRNA-miRNA interactions on breast cancer and detects synergistic drug combinations in cancer cell lines. The source code is available at https://github.com/Liangyushi/ConvNTC.