A method based on k-mer with SVM called gkm-SVM achieves better performance than the classical k-mer SVM for classification in the ENCODE Chip-seq dataset. As an extensive DNA transcription factor analysis tool, MEME analyzes DNA sequences by building a maximum expectation model. Precisely because of this, progressively some computational or machine learning based methods started to be applied to sequence analysis. The sophistication of these data makes it very difficult to accurately predict the functionality or properties of sequences by conventional biological experiments. With the recent development of high-throughput technologies, different sequencing technologies such as MNase-seq and ATAC-seq have been generated to fit different research purposes, and these technologies have led to a significant enrichment of relevant sequence datasets. Recently, research has increasingly focused on the challenges of properly predicting the functionality or properties of sequences in traditional biological experiments, such as DNA transcription factors. These achievements have not only contributed to the development of biology itself, but also enabled the advancement of the studies surrounding biology, such as cancer. The drawbacks of these biological experiments that continue to facilitate the development of computational methods for uncovering the genetic information contained in DNA transcription factors sequences. However, these biological experiments are expensive and time-consuming in the face of large-scale classification tasks, which often become labor-intensive to obtain complete results. Gene expression is regulated by the activation or repression of transcription factors, which are essential for a number of critical cellular processes. Transcription factors are important molecules that control gene expression and directly control the timing and extent of gene expression. The effective identification and recognition of functional and genetic properties in DNA can be achieved by relying on traditional biological experiments, especially in the study of DNA transcription factors. Genes, the basic unit of genetics, are special segments of DNA that have genetic utility, while DNA is the important participant in biological processes such as splicing, translation and transcription. The genome contains two regions, open and closed, and most of the transcriptional processes occur in the open region. Therefore, it is particularly important to discover the functional locations of the genome in order to gain a broader understanding of how genes work. These genetic variants also generally contain information that is mostly hidden in certain regions of the genome. Most of the known genetic variants of human diseases are often closely related to human genes. Additionally, the effectiveness of our approach is validated in accurately predicting DNA transcription factor sequences. Through training with labeled data, experiments demonstrate that this approach significantly improves performance while requiring fewer parameters compared to existing methods. This approach reduces the overall number of parameters in the model while harnessing the computational power of sequence data from multi-head self-attention. Simultaneously, the multi-head self-attention mechanism enhances the identification of hidden features with long-distant dependencies. By employing convolutional neural networks, it can effectively capture local hidden features in the sequences. This method leverages deep convolutional neural networks with a multi-head self-attention mechanism. To overcome these limitations, it is proposed a novel approach for analyzing DNA transcription factor sequences, which is named as DeepCAC. Nevertheless, the pursuit of improved experimental results has led to the inclusion of numerous complex analysis function modules, resulting in models with a growing number of parameters. To address these challenges, the field of bioinformatics has increasingly turned to deep learning technologies for analyzing gene sequences. However, these techniques suffer from inherent limitations such as time consumption and high costs. Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies.
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