Convolutional codes are error-correcting linear codes that utilize shift registers to encode. These codes have an arbitrary block size and they can incorporate both past and current information bits. DNA codes represent DNA sequences and are defined as sets of words comprised of the alphabet A, C, T, G satisfying certain mathematical bounds and constraints. The application of convolutional code models to DNA codes is a growing field of biocomputation. As opposed to block codes, convolutional codes factor in nearby information bits, which makes them an optimal model for representing biological phenomena. This study explores the properties of both convolutional codes and DNA codes, as well as how convolutional codes are applied to DNA codes. It also proposes revisions to improve a current convolutional code model for DNA sequences.
翻译：革命代码是纠正错误的线性代码,它使用转移登记册编码。这些代码具有任意的区块大小,可以包含过去和现在的信息位数。DNA代码代表DNA序列,被定义为由字母A、C、T、G组成的一组单词,满足了某些数学界限和限制。将革命代码模型应用于DNA代码是一个不断增长的生物计算领域。相对于区块代码,相近信息位数中的革命代码系数,这使它们成为代表生物现象的最佳模式。本研究探索了革命代码和DNA代码的特性,以及如何将革命代码应用于DNA代码。它还提出了修改建议,以改进当前DNA序列的革命代码模型。
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