## 1 Introduction  
Translation initiation is a pivotal process in the regulation of gene expression, determining where protein synthesis begins on messenger RNA (mRNA). The dysregulation of this initiation process can cause various human diseases, including cancers and metabolic disorders [1, 2, 3]. Translation initiation site (TIS) prediction is an essential step in transcriptome annotation that aims to elucidate the biological relevance [4]. The accurate prediction of TISs is not only important for profiling the protein coding fraction of the transcriptome but also for the accurate identification of untranslated regions (UTRs), which are known as important regulators in the translation process [4]. Thus, TIS prediction holds significant importance for studying the disease mechanisms of occurrence and development. The identification of TISs in uncharacterized mRNA sequences presents an inherently challenging task due to several key factors. These include the following: (1) Weak sequence conservation. In many organisms, translation initiation is guided by specific sequence motifs, such as the Kozak sequence [5] in eukaryotes and the Shine-Dalgarno sequence [6] in prokaryotes. However, these motifs are not universally conserved or sufficiently distinctive across all species, and TISs are surrounded by relatively poorly conserved sequences [7]. (2) Complexity in the translation initiation mechanism. In many mRNAs, there can be multiple potential translation initiation sites that may produce alternative protein isoforms or regulatory proteins. For instance, numerous mRNAs harbor several open reading frames (ORFs), among which upstream ORFs (uORFs) often inhibit the translation of the downstream CDS [8; 9; 10]. These alternative initiation sites necessitate sophisticated computational techniques to resolve ambiguities. In this paper, we focus on TIS prediction in the main ORF with tri-nucleotide AUG, which is biologically prevalent in eukaryotes. Over the past few decades, numerous computational methods have been proposed for TIS prediction, primarily categorized by their classifiers. The commonly employed techniques encompass artificial neural networks (ANNs) [3; 11; 12; 13], support vector machine (SVM) [14; 15; 16], linear discriminant analysis (LDA) [17], and Gaussian-based models [18]. According to the existing research, the study in [17] proposed a method called AUGpr for TIS identification using LDA, which utilizes six effective features around AUG, such as a position triplet weight matrix and ORF hexanucleotide features. The improved version of AUGpr, called AUGpr_sim, was proposed by [19], which exploits both statistical information and similarity to other known proteins to achieve higher prediction performance in cDNA sequences. Ref. [14] explored SVMs with different kernel functions for TIS prediction. The authors claimed that the careful design of kernel functions helps to improve prediction accuracy. Ref. [20] proposed a novel method for eukaryotic gene structure prediction based on a modular neural network system. The prediction task was performed by detecting different signals and contents based on different neural networks. In [21], a novel ensemble method was developed for TIS prediction, integrating two specialized neural networks: one detecting conserved motifs and the other one analyzing coding features around start codons. In [22], a modular approach for TIS prediction, called MANTS, was proposed, which mainly consists of three models: consensus, coding region classification, and AUG positioning. Three dedicated classifiers were utilized to execute these models in MANTS, whose outputs were subsequently fused by the ultimate decision classifier. Its enhanced variant, StackTIS, employing modified learning procedures and training strategies, was subsequently documented in [23]. All these methods demonstrate the significance of fusing multiple features (e.g., consensus motifs and coding features) for TIS prediction.