论文标题
将变压器和自动编码器技术与光谱图算法集成，以预测几乎没有标记的分子数据
Integrating Transformer and Autoencoder Techniques with Spectral Graph Algorithms for the Prediction of Scarcely Labeled Molecular Data
论文作者
论文摘要
在分子和生物学科学中，实验昂贵，耗时，并且通常受到道德约束。因此，人们经常面临着具有挑战性的任务，即从小型数据集或几乎没有标记的数据集中预测理想的属性。尽管转移学习可能是有利的，但它需要存在相关的大数据集。这项工作介绍了三个基于图的模型，其中包含了Merriman-Bence-osher（MBO）技术来应对这一挑战。具体而言，基于图形的MBO方案的修改与最新技术（包括自制的变压器和自动编码器）集成在一起，以处理几乎没有标记的数据集。此外，还详细介绍了共识技术。使用五个基准数据集对所提出的模型进行验证。我们还提供了与其他竞争方法的彻底比较，例如支持向量机，随机森林和梯度增强决策树，这些决策树以其在小型数据集上的良好性能而闻名。使用残基相似度（R-S）分数和R-S指数分析各种方法的性能。广泛的计算实验和理论分析表明，即使将数据集的1％用作标记的数据，新模型的性能也很好。
In molecular and biological sciences, experiments are expensive, time-consuming, and often subject to ethical constraints. Consequently, one often faces the challenging task of predicting desirable properties from small data sets or scarcely-labeled data sets. Although transfer learning can be advantageous, it requires the existence of a related large data set. This work introduces three graph-based models incorporating Merriman-Bence-Osher (MBO) techniques to tackle this challenge. Specifically, graph-based modifications of the MBO scheme are integrated with state-of-the-art techniques, including a home-made transformer and an autoencoder, in order to deal with scarcely-labeled data sets. In addition, a consensus technique is detailed. The proposed models are validated using five benchmark data sets. We also provide a thorough comparison to other competing methods, such as support vector machines, random forests, and gradient boosting decision trees, which are known for their good performance on small data sets. The performances of various methods are analyzed using residue-similarity (R-S) scores and R-S indices. Extensive computational experiments and theoretical analysis show that the new models perform very well even when as little as 1% of the data set is used as labeled data.
