利用Kolmogorov-Arnold分析检测基因组序列中的体细胞突变
Detecting somatic mutations in genomic sequences by means of Kolmogorov-Arnold analysis
摘要：
Kolmogorov-Arnold随机参数技术首次应用于癌症基因组测序研究，以揭示突变。使用下一代测序技术产生的数据，我们分析了具有匹配肿瘤和正常血液的脑肿瘤患者的外显子组序列。我们表明，使用这种技术可以揭示测序数据中包含的突变，从而提供了一种新的方法来确定给定长度的包含突变的序列，即其值不同于没有突变的序列。这项技术的潜在应用包括简化发现突变片段的程序，加快基因组研究，并加速其在临床诊断中的实施。此外，纯粹基于Kolmogorov函数的值预测与许多类型的癌症中的频繁突变家族相关的突变，表明这种应用的标记物可以识别丰度极低的基因组序列，并且可以用于揭示新类型的突变。
英文摘要：
The Kolmogorov-Arnold stochasticity parameter technique is applied for the first time to the study of cancer genome sequencing, to reveal mutations. Using data generated by next generation sequencing technologies, we have analyzed the exome sequences of brain tumor patients with matched tumor and normal blood. We show that mutations contained in sequencing data can be revealed using this technique thus providing a new methodology for determining subsequences of given length containing mutations i.e. its value differs from those of subsequences without mutations. A potential application for this technique involves simplifying the procedure of finding segments with mutations, speeding up genomic research, and accelerating its implementation in clinical diagnostic. Moreover, the prediction of a mutation associated to a family of frequent mutations in numerous types of cancers based purely on the value of the Kolmogorov function, indicates that this applied marker may recognize genomic sequences that are in extremely low abundance and can be used in revealing new types of mutations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
