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ConvNTC: convolutional neural tensor completion for detecting "A-A-B" type biological triplets.

Pei Liu1,2, Xiao Liang1, Yue Li2

  • 1Department of Computer Science, College of Computer Science and Electronic Engineering, 116 Lu Shan South Road, Hunan University, Changsha 410082, Hunan, China.

Briefings in Bioinformatics
|August 1, 2025
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Summary
This summary is machine-generated.

This study introduces Convolutional Neural Tensor Completion (ConvNTC) to model complex molecular interactions. ConvNTC accurately predicts molecular triplets, aiding in disease mechanism discovery and drug development.

Keywords:
deep learningdrug combinationmiRNA–miRNA interactiontensor completiontriplet prediction

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Network science

Background:

  • Understanding molecular interactions is key to disease research and therapeutics.
  • The "A-A-B" triplet paradigm models context-specific molecular relationships.
  • Tensor-based methods struggle to capture both multilinear and nonlinear interaction characteristics.

Purpose of the Study:

  • To develop a novel framework, Convolutional Neural Tensor Completion (ConvNTC), for modeling "A-A-B" type triplet interactions.
  • To integrate multilinear and nonlinear modeling approaches for enhanced triplet prediction.
  • To improve the accuracy of identifying context-specific molecular interactions.

Main Methods:

  • Proposed a Convolutional Neural Tensor Completion (ConvNTC) framework with multilinear and nonlinear modules.
  • Utilized tensor decomposition with constraints for factor embeddings.
  • Incorporated an embedding generator, convolutional encoder, and Kolmogorov-Arnold Network (KAN) predictor for nonlinear feature mapping and relationship capture.

Main Results:

  • ConvNTC demonstrated superior performance in triplet prediction compared to 11 state-of-the-art methods.
  • Evaluated on miRNA-miRNA-disease and drug-drug-cell triplet datasets.
  • Identified prognostic values of miRNA-miRNA interactions in breast cancer and synergistic drug combinations in cancer cell lines.

Conclusions:

  • ConvNTC effectively models multilinear and nonlinear characteristics for accurate triplet prediction.
  • The framework shows potential for advancing disease mechanism research and therapeutic strategy development.
  • ConvNTC offers a powerful tool for analyzing complex biological networks.