Research on Gene Identification Algorithms Based on Signal Processing Techniques

With the rapid advancements in genomics and bioinformatics, gene identification has assumed a pivotal role in contemporary biological research. Gene identification involves pinpointing regions within DNA sequences that encode proteins or functional RNAs, which is of profound significance for understanding the fundamental mechanisms of life, exploring the genetic underpinnings of diseases, and drug development. While traditional experimental biology methods offer reliable tools, they are often time-consuming and costly. Consequently, the application of signal processing techniques for gene identification has emerged as a focal point of current research. Signal processing, with its formidable analytical capabilities when dealing with vast amounts of data, is particularly well-suited for high-dimensional and complex data such as DNA sequences. By converting DNA sequences into numerical signals and employing spectral analysis methods like Fourier and wavelet transforms, intrinsic features of gene structures can be unveiled. Moreover, integrating machine learning algorithms such as Support Vector Machines (SVM) further enhances the accuracy of gene identification. Signal processing methods provide robust support for the efficient and automated processing of complex genomic data from both prokaryotic and eukaryotic organisms.