Neural Distance Embeddings for Biological Sequences
Neural Distance Embeddings for Biological Sequences
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发表时间：
2021-09
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通讯作者：
Gabriele Corso;Rex Ying;Michal P'andy;Petar Velivckovi'c;J. Leskovec;P. Lio’
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作者：
Gabriele Corso;Rex Ying;Michal P'andy;Petar Velivckovi'c;J. Leskovec;P. Lio’
发展生物序列的数据依赖性分析和表征，反映它们的进化距离，是大规模生物学研究的关键。然而，基于连续欧几里得空间的流行机器学习方法一直在努力解决模拟进化的编辑距离的离散组合公式和表征真实世界数据集的层次关系。我们提出了神经距离嵌入（NeuroSEED），一个通用的框架，以嵌入序列的几何向量空间，并说明双曲空间的有效性，捕获的层次结构，并提供了一个平均22%的减少嵌入RMSE对最好的竞争几何。的能力的框架和这些改进的意义，然后证明设计监督和无监督的NeuroSEED方法在生物信息学的多个核心任务。以公共基线为基准，所提出的方法在真实世界的数据集上显示出显着的准确性和/或运行时间改进。作为分层聚类的一个例子，所提出的预训练和从头开始的方法分别以30倍和15倍的运行时间减少来匹配竞争基线的质量。
The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research. However, popular machine learning approaches, based on continuous Euclidean spaces, have struggled with the discrete combinatorial formulation of the edit distance that models evolution and the hierarchical relationship that characterises real-world datasets. We present Neural Distance Embeddings (NeuroSEED), a general framework to embed sequences in geometric vector spaces, and illustrate the effectiveness of the hyperbolic space that captures the hierarchical structure and provides an average 22% reduction in embedding RMSE against the best competing geometry. The capacity of the framework and the significance of these improvements are then demonstrated devising supervised and unsupervised NeuroSEED approaches to multiple core tasks in bioinformatics. Benchmarked with common baselines, the proposed approaches display significant accuracy and/or runtime improvements on real-world datasets. As an example for hierarchical clustering, the proposed pretrained and from-scratch methods match the quality of competing baselines with 30x and 15x runtime reduction, respectively.