Ahsen Mehmet Eren, Vogel Robert, Stolovitzky Gustavo
Department of Business Administration, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, United States.
Department of Biomedical and Translational Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States.
Bioinform Adv. 2024 Jun 25;4(1):vbae093. doi: 10.1093/bioadv/vbae093. eCollection 2024.
The integration of vast, complex biological data with computational models offers profound insights and predictive accuracy. Yet, such models face challenges: poor generalization and limited labeled data.
To overcome these difficulties in binary classification tasks, we developed the Method for Optimal Classification by Aggregation (MOCA) algorithm, which addresses the problem of generalization by virtue of being an ensemble learning method and can be used in problems with limited or no labeled data. We developed both an unsupervised (uMOCA) and a supervised (sMOCA) variant of MOCA. For uMOCA, we show how to infer the MOCA weights in an unsupervised way, which are optimal under the assumption of class-conditioned independent classifier predictions. When it is possible to use labels, sMOCA uses empirically computed MOCA weights. We demonstrate the performance of uMOCA and sMOCA using simulated data as well as actual data previously used in Dialogue on Reverse Engineering and Methods (DREAM) challenges. We also propose an application of sMOCA for transfer learning where we use pre-trained computational models from a domain where labeled data are abundant and apply them to a different domain with less abundant labeled data.
GitHub repository, https://github.com/robert-vogel/moca.
将海量、复杂的生物数据与计算模型相结合可提供深刻的见解和预测准确性。然而，此类模型面临挑战：泛化能力差和标记数据有限。
为了克服二元分类任务中的这些困难，我们开发了聚合最优分类方法（MOCA）算法，该算法作为一种集成学习方法解决了泛化问题，并且可用于标记数据有限或无标记数据的问题。我们开发了MOCA的无监督（uMOCA）和有监督（sMOCA）变体。对于uMOCA，我们展示了如何以无监督方式推断MOCA权重，这些权重在类条件独立分类器预测的假设下是最优的。当可以使用标签时，sMOCA使用经验计算的MOCA权重。我们使用模拟数据以及先前用于逆向工程与方法对话（DREAM）挑战的实际数据展示了uMOCA和sMOCA的性能。我们还提出了sMOCA在迁移学习中的一种应用，即我们使用来自标记数据丰富的领域的预训练计算模型，并将它们应用于标记数据较少的不同领域。
GitHub仓库，https://github.com/robert-vogel/moca 。
