Reciprocal Perspective Machine Learning to Identify Relationships in Sparse Biological Networks

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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.