Tecm-Chi: A Tecm Network-Based Method for Chromatin Interaction Prediction

Abstract

Chromatin interactions refer to regulatory relationships formed between chromatin regions through physical contact or spatial proximity, playing a crucial role in genome function, structure, and the development of diseases. In cancer research, for example, thinking of chromatin as a gel can help explain the spread of cancer. Traditional experimental methods, such as Hi-C and ChIA-PET, are costly, time-consuming, and applicable to only a limited number of cell lines. Increasing evidence shows that DNA sequences and genomic features (chromatin-associated proteins e.g.) are essential predictors of chromatin interactions. However, existing computational methods based on these features suffer from data imbalance and low prediction accuracy, which limits their broader application in biomedical research. To address this, we proposes an entirely new model to investigate the existence of chromatin interactions based on DNA sequences and genomic features, called TECM-ChI. In this model, we first design the FCR(Forward Combine Reverse) method to balance the positive and negative samples in the K562, IMR90, and GM12878 datasets to achieve a 1:1 ratio. Additionally, to fully extract meaningful information from the gene sequences, we develop a preprocessing Three-Encoding module that uses three encoding methods to concatenate each nucleotide into a 45-dimensional vector. Next, we propose the CMANet network model, which combines multi-layer convolution with multiple attention mechanisms. CMANet effectively extracts local features within sequence information and enhances focus on key regions, improving the ability to recognize chromatin interactions. To evaluate TECM-ChI's effectiveness, we conducted model variant experiments, loss performance analysis, and comparative analysis with existing computational methods across three cell lines. Experimental results demonstrate that, compared to the current best models, TECM-ChI achieves accuracy improvements of 4.68%, 1.31%, and 2.41% on the K562, IMR90, and GM12878 datasets, respectively, proving its effectiveness and generalization ability in predicting chromatin interactions. The source code for TECM-ChI is available at https://github.com/Fated-2/TECM-ChI.git.

Note:
Funding declaration: This work was supported by the National Natural Science Foundation of China (No. 72301060).

Conflict of Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Keywords: Three-dimensional genome organization, gene expression, genomic characteristics, chromatin interactions, Deep Learning