Department of Computer Science, University of New Orleans, New Orleans, LA 70148, USA.
Adv Exp Med Biol. 2010;680:99-108. doi: 10.1007/978-1-4419-5913-3_12.
Pattern recognition-informed (PRI) feedback using channel current cheminformatics (CCC) software is shown to be possible in "real-time" experimental efforts. The accuracy of the PRI classification is shown to inherit the high accuracy of our offline classifier: 99.9% accuracy in distinguishing between terminal base pairs of two DNA hairpins. The pattern recognition software consists of hidden Markov model (HMM) feature extraction software, and support vector machine (SVM) classification/ clustering software that is optimized for data acquired on a nanopore channel detection system. For general nanopore detection, the distributed HMM and SVM processing used here provides a processing speedup that allows pattern recognition to complete within the time frame of the signal acquisition - where the sampling is halted if the blockade signal is identified as not in the desired subset of events (or once recognized as nondiagnostic in general). We demonstrate that Nanopore Detection with PRI offers significant advantage when applied to data acquisition on antibody-antigen system, or other complex biomolecular mixtures, due to the reduction in wasted observation time on eventually rejected "junk" (nondiagnostic) signals.
基于模式识别的（PRI）反馈使用通道电流化学信息学（CCC）软件，可以在“实时”实验中实现。PRI 分类的准确性继承了我们离线分类器的高精度：在区分两个 DNA 发夹的末端碱基对时，准确率达到 99.9%。模式识别软件由隐马尔可夫模型（HMM）特征提取软件和支持向量机（SVM）分类/聚类软件组成，这些软件针对在纳米孔通道检测系统上采集的数据进行了优化。对于一般的纳米孔检测，这里使用的分布式 HMM 和 SVM 处理提供了处理加速，使得模式识别可以在信号采集的时间框架内完成——如果阻塞信号被识别为不在期望的事件子集（或者通常被识别为非诊断），则停止采样。我们证明，当应用于抗体-抗原系统或其他复杂生物分子混合物的数据采集时，基于模式识别的纳米孔检测具有显著优势，因为它减少了最终被拒绝的“垃圾”（非诊断）信号的浪费观察时间。
