## 2 Related Work  
Protein secondary structure prediction (PSSP) has long been a central research focus in bioinformatics and computational biology. Its primary goal is to predict local structural motifs--such as \(\alpha\) helices, \(\beta\) sheets, and loops--based on amino acid sequences. These local arrangements play a crucial role in determining a protein's three-dimensional (3D) conformation and its biological functions. Over the years, a wide range of machine learning and deep learning techniques have been proposed to enhance prediction accuracy and computational efficiency.  
Early PSSP methods were predominantly rule-based and statistical, utilizing the physicochemical properties of amino acids and probabilistic analysis of structural motifs. Approaches like the Chou-Fasman and GOR (Garnier-Oguthorpe-Robson) methods were among the first widely used models [17]. However, these techniques often lack generalization capabilities and are computationally intensive, limiting their practical utility.  
The integration of machine learning into PSSP began with feedforward neural networks, which classify residues using local sequence patterns [18]. This was followed by the application of Support Vector Machines (SVMs) and Hidden Markov Models (HMMs), which introduce more robust mechanisms for modeling residue dependencies. Nevertheless, these models struggle in capturing long-range interactions, which are critical for accurate secondary structure prediction.  
A major breakthrough came with the rise of deep learning. Convolutional Neural Networks (CNNs) proved effective in recognizing local spatial features [6], while Long Short-Term Memory (LSTM) networks improved sequence modeling by learning long-range dependencies [19]. Bidirectional LSTMs (Bi-LSTMs) further enhanced performance by enabling simultaneous forward and backward context propagation [20].  
In the late 2010s, research increasingly focused on deeper and more complex architectures. [PERSON] et al. emphasized the limitations of shallow models and advocated for deeper networks with improved abstraction capabilities [10]. Similarly, [PERSON] et al. provided a comprehensive review of neural network-based approaches and their impact on PSSP performance [21].  
The year 2020 marked a turning point, with the introduction of hybrid architectures. [PERSON] et al. proposed a CNN-LSTM hybrid that captures both local and global dependencies [22]. [PERSON] et al. later introduced a lightweight CNN combined with label distribution-aware marginal loss to improve generalization [10]. [PERSON] and [PERSON] advanced the field further with a cascaded 1D ConvNet-Bi-LSTM architecture specifically tailored for PSSP [23].  
In 2022, CulledPDB-based datasets contributed to more accurate and generalizable models [24]. [PERSON] et al. also explored deep metric learning strategies to better represent structural relationships between proteins [10]. Meanwhile, the release of AlphaFold revolutionized protein structure prediction by offering near-experimental accuracy, reinforcing the need for strong secondary structure predictors as a foundation for tertiary structure modeling [2].  
In 2023, [PERSON] and [PERSON] proposed a hierarchical feature learning model for PSSP, showcasing the continued evolution of deep learning in computational biology [9]. Transformer-based architectures, particularly those leveraging self-attention mechanisms, gained momentum due to their capacity to model both short- and long-range residue interactions. AlphaFold's success exemplifies the power of these architectures in achieving state-of-the-art structural predictions [25].  
This historical trajectory--from early statistical models to modern deep learning frameworks--has significantly advanced the field of PSSP. Each innovation builds upon prior methodologies, forming a rich body of research that continues to evolve with the rapid emergence of new techniques and high-quality biological datasets.