Ribosome stagnation relative strength prediction method of MambaPlus and Transform parallel architecture

By using the parallel architecture of MambaPlus and Transformer, combined with K-mer encoding and multi-scale CNN, the problems of multi-scale regulatory element identification and bidirectional regulatory modeling in the prediction of ribosome stationary relative intensity were solved, achieving more accurate prediction of ribosome stationary relative intensity and improving the model's decoding ability and biological rationality.

CN122090915APending Publication Date: 2026-05-26GUANGDONG UNIV OF TECH
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Patent Information

Application Number
CN202511821110.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-05
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing methods for predicting the relative intensity of ribosome cessation suffer from several problems, including insufficient recognition of multi-scale regulatory elements, lack of modeling of bidirectional transcriptional regulatory mechanisms, coarse cross-modal integration of sequence and biophysical features, and imbalance between long-range dependence and local model biology.

Method used

Employing a parallel architecture of MambaPlus and Transformer, this study utilizes K-mer and One-Hot serial composite encoding, parallel multi-scale CNN convolutional neural networks, bidirectional MambaPlus modules, and dynamic feature fusion technology, combined with biophysical features, to achieve multi-scale feature extraction and multi-source feature fusion, simulating the bidirectional regulatory characteristics and long-range dependence of DNA sequences.

Benefits of technology

It significantly improves the accuracy and generalization ability of predicting the relative intensity of ribosome stagnation, enabling a more comprehensive capture of the complex regulatory logic and biological processes in DNA sequences, and providing traceable prediction results.

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Abstract

The invention discloses a ribosome stagnation relative strength prediction method of a MambaPlus and Transform parallel architecture, and belongs to the crossing field of bioinformatics and artificial intelligence. According to the model, firstly, a DNA sequence is converted into multi-dimensional feature representation through K-mer and One-hot composite coding, a local sequence pattern is extracted by adopting a parallel multi-scale convolutional neural network, and long-range dependency and global context information are modeled respectively in combination with a MambaPlus encoder and a Transform encoder; multi-modal features are integrated through an adaptive weighted fusion strategy, dynamic aggregation is carried out on sequences in combination with attention pooling, and finally prediction of ribosome stagnation relative strength is realized in combination with biophysical features. The error of the depth model for predicting the relative strength of ribosome stagnation is smaller than 0.27, and compared with a traditional model, the depth model has higher accuracy.
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