生物医学数据的近似kNN分类
Approximate kNN Classification for Biomedical Data
摘要：
我们正处于大数据分析改变了解释各种生物医学现象的方式的时代，随着生成的数据的增加，对新的机器学习方法来处理这种演变的需求也在增长。一个典型的例子是单细胞RNA-seq（scRNA-seq），这是一种新兴的DNA测序技术，具有很有前途的能力，但由于大规模生成的数据而面临重大的计算挑战。关于scRNA-seq数据的分类过程，一种合适的方法是k最近邻（kNN）分类器，因为它通常用于大规模预测任务，这是由于其简单性、最小参数化和无模型性质。然而，scRNA-seq的超高维度特征造成了计算瓶颈，而预测能力可能受到“诅咒”的影响。在这项工作中，我们提出了利用近似最近邻搜索算法来完成scRNA-seq数据中的kNN分类任务，重点是针对高维数据量身定制的特定方法。我们认为，即使是宽松的近似解决方案将不会影响预测性能显着。实验结果证实了原来的假设，提供了更广泛的适用性的潜力。
英文摘要：
We are in the era where the Big Data analytics has changed the way of interpreting the various biomedical phenomena, and as the generated data increase, the need for new machine learning methods to handle this evolution grows. An indicative example is the single-cell RNA-seq (scRNA-seq), an emerging DNA sequencing technology with promising capabilities but significant computational challenges due to the large-scaled generated data. Regarding the classification process for scRNA-seq data, an appropriate method is the k Nearest Neighbor (kNN) classifier since it is usually utilized for large-scale prediction tasks due to its simplicity, minimal parameterization, and model-free nature. However, the ultra-high dimensionality that characterizes scRNA-seq impose a computational bottleneck, while prediction power can be affected by the "Curse of Dimensionality". In this work, we proposed the utilization of approximate nearest neighbor search algorithms for the task of kNN classification in scRNA-seq data focusing on a particular methodology tailored for high dimensional data. We argue that even relaxed approximate solutions will not affect the prediction performance significantly. The experimental results confirm the original assumption by offering the potential for broader applicability.
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