Reciprocal Perspective Machine Learning to Identify Relationships in Sparse Biological Networks

交互视角机器学习识别稀疏生物网络中的关系

• 批准号: RGPIN-2021-04184
• 负责人: Green, James
• 金额: $ 2.55万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742915
• 关键词: Reciprocal; Perspective; Machine; Learning; Identify

Our lab focuses on the development of machine learning (ML) approaches to solve problems in biomedical informatics, particularly in the presence of class imbalance where events of interest are rare among all data. Recently, our lab has developed a number of semi-supervised ML approaches that learn from both labeled and unlabeled data. Such approaches are relevant for many problem domains, especially within bioinformatics, where we often have large quantities of unlabeled data (e.g. genomic or proteomic sequence data), but acquiring labeled data requires costly experiments. Our research is motivated by a common story in bioinformatics: we create the best predictor possible for a given task, given the limited training data available. At great computational expense, we then apply the predictor to all available unlabeled data and report the "top predictions" representing novel testable hypotheses for wet-lab collaborators. But can't we leverage this enormous computational investment to improve the underlying prediction model? This story led to one of our most exciting recent discoveries, the Reciprocal Perspective (RP) paradigm, which learns from the structure of the distribution of prediction scores over a large body of unlabeled samples. In this way, each prediction is evaluated in the context of all possible predictions involving each element. RP is particularly well-suited to pairwise prediction problems where one aims to predict links between nodes in a sparse network. Using RP, we have demonstrated significant performance improvements for predicting protein-protein interactions and microRNA targets. We propose to extend and apply these semi-supervised ML approaches to continue our long-term research goals of improving the predictive performance of ML models applied to domains challenged by class imbalance. This research program will extend and generalize the RP paradigm to create a robust semi-supervised ML framework, broadly applicable to disparate domains. In particular, we will develop the fusion of RP with multi-view co-training; explore RP as a means to combine multiple experts to arrive at improved consensus decisions; achieve the conceptual merger of RP with semi-supervised transductive learning; and extend the RP paradigm beyond pairwise predictions to the N-dimensional case. Our lab has a demonstrated record of successful, innovative, and impactful interdisciplinary research in biomedical informatics. The novel ML methodology to be developed within this research program will be translated and applied to several problem domains via established and effective collaborations. This research will achieve impact in fundamental ML research, in biomedical informatics, and beyond.

我们的实验室重点是解决生物医学信息学问题的机器学习方法（ML）方法，尤其是在所有数据中很少见的阶级失衡的情况下。最近，我们的实验室开发了许多半监督的ML方法，这些方法从标记和未标记的数据中学习。这种方法与许多问题域有关，尤其是在生物信息学中，在这种情况下，我们通常拥有大量未标记的数据（例如基因组或蛋白质组学序列数据），但是获取标记的数据需要昂贵的实验。 我们的研究是出于生物信息学中常见故事的动机：鉴于可用的培训数据有限，我们为给定任务创造了最佳的预测指标。然后，我们以大量的计算费用将预测变量应用于所有可用的未标记数据，并报告代表湿LAB协作者可检验的新假设的“顶级预测”。但是，我们不能利用这项巨大的计算投资来改善基本预测模型吗？ 这个故事导致了我们最近的最令人兴奋的发现之一，即互惠观点（RP）范式，该范式从大量未标记的样本中的预测分数分布的结构中学习。这样，每个预测都会在涉及每个元素的所有可能预测的上下文中进行评估。 RP特别适合成对预测问题，其中一个人旨在预测稀疏网络中的节点之间的联系。 使用RP，我们显示了预测蛋白质 - 蛋白质相互作用和microRNA靶标的显着性能改善。我们建议扩展和应用这些半监督的ML方法，以继续我们的长期研究目标，以提高应用于阶级不平衡挑战的领域的ML模型的预测性能。该研究计划将扩展和推广RP范式，以创建一个强大的半监督ML框架，该框架广泛适用于不同的域。特别是，我们将开发RP与多视图共同训练的融合；探索RP作为结合多个专家的一种手段，以提高共识决定；通过半监督的跨示导性学习，实现RP的概念合并；并将RP范式扩展到成对预测之外的N维情况。 我们的实验室在生物医学信息学领域有了成功，创新和有影响力的跨学科研究的记录。该研究计划中要开发的新型ML方法将通过既定有效的合作进行翻译并应用于几个问题领域。这项研究将对基本ML研究，生物医学信息学及其他地区产生影响。