TRANSAID: a hybrid deep learning framework for translation site prediction with integrated biological feature scoring.

ABSTRACT:

Introduction

Translation initiation and termination are critical regulatory checkpoints in protein synthesis, yet accurate computational prediction of their sites remains challenging due to training data biases and the complexity of full-length transcripts.

Methods

To address these limitations, we present TRANSAID (TRANSlation AI for Detection), a novel deep learning framework that accurately and simultaneously predicts translation initiation (TIS) and termination (TTS) sites from complete transcript sequences. TRANSAID's hierarchical architecture efficiently processes long transcripts, capturing both local motifs and long-range dependencies. Crucially, the model was trained on a human transcriptome dataset that was rigorously partitioned at the gene level to prevent data le

SUBMITTER: Li Y 

PROVIDER: S-EPMC12862215 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature