平衡配体生化活性的预测相关性
Balancing Predictive Relevance of Ligand Biochemical Activities
摘要：
在本文中，我们提出了一种技术，用于平衡预测相关模型相关的监督建模配体生化活动的生物靶标。我们训练未校准的模型采用传统的监督机器学习技术，即支持向量机。不幸的是，SVM有一个严重的缺点。它们对不平衡的数据集、离群值和训练样本之间的高度共线性敏感，这可能是导致一个组优于另一个组的原因。因此，可能需要额外的校准来平衡模型的预测相关性。作为这种平衡的技术，我们提出了普拉特的缩放。在ExCAPE数据库导出的数据集上训练的单目标模型上证明了所取得的结果。与传统的使用机器技术，我们专注于减少不确定性采用确定性求解器。
英文摘要：
In this paper, we present a technique for balancing predictive relevance models related to supervised modelling ligand biochemical activities to biological targets. We train uncalibrated models employing conventional supervised machine learning technique, namely Support Vector Machines. Unfortunately, SVMs have a serious drawback. They are sensitive to imbalanced datasets, outliers and high multicollinearity among training samples, which could be a cause of preferencing one group over another. Thus, an additional calibration could be required for balancing a predictive relevance of models. As a technique for this balancing, we propose the Platt's scaling. The achieved results were demonstrated on single-target models trained on datasets exported from the ExCAPE database. Unlike traditional used machine techniques, we focus on decreasing uncertainty employing deterministic solvers.
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