Seq-HyGAN: Sequence Classification via Hypergraph Attention Network

Abstract

Sequence classification has a wide range of real-world applications in different domains, such as genome classification in health and anomaly detection in business. However, the lack of explicit features in sequence data makes it difficult for machine learning models. While Neural Network (NN) models address this with learning features automatically, they are limited to capturing adjacent structural connections and ignore global, higher-order information between the sequences. To address these challenges in the sequence classification problems, we propose a novel Hypergraph Attention Network model, namely Seq-HyGAN. To capture the complex structural similarity between sequence data, we first create a hypergraph where the sequences are depicted as hyperedges and subsequences extracted from sequences are depicted as nodes. Additionally, we introduce an attention-based Hypergraph Neural Network model that utilizes a two-level attention mechanism. This model generates a sequence representation as a hyperedge while simultaneously learning the crucial subsequences for each sequence. We conduct extensive experiments on four data sets to assess and compare our model with several state-of-the-art methods. Experimental results demonstrate that our proposed Seq-HyGAN model can effectively classify sequence data and significantly outperform the baselines. We also conduct case studies to investigate the contribution of each module in Seq-HyGAN.

Hist2Vec: Kernel-Based Embeddings for Biological Sequence Classification

“…Methods such as the spectrum kernel and string kernel [24] have been proposed for sequence classification, leveraging the concept of k-mer frequencies to construct similarity measures. However, these methods may suffer from high computational complexity and memory requirements, limiting their scalability [34]. Kernel methods have been successfully applied to various bioinformatics tasks, including protein fold recognition [31], protein-protein interaction prediction [39], and protein function prediction [7].…”