NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification
This work addresses the challenge of discovering new disease genes for biomedical research, but it is incremental as it builds on existing positive-unlabeled learning and network-based approaches.
The authors tackled the problem of identifying disease-associated genes using positive-unlabeled learning, proposing network-based features and a Markov diffusion labeling strategy, and found their integrated methodology to be competitive against state-of-the-art algorithms on ten disease datasets.
Gene-disease associations are fundamental for understanding disease etiology and developing effective interventions and treatments. Identifying genes not yet associated with a disease due to a lack of studies is a challenging task in which prioritization based on prior knowledge is an important element. The computational search for new candidate disease genes may be eased by positive-unlabeled learning, the machine learning setting in which only a subset of instances are labeled as positive while the rest of the data set is unlabeled. In this work, we propose a set of effective network-based features to be used in a novel Markov diffusion-based multi-class labeling strategy for putative disease gene discovery. The performances of the new labeling algorithm and the effectiveness of the proposed features have been tested on ten different disease data sets using three machine learning algorithms. The new features have been compared against classical topological and functional/ontological features and a set of network- and biological-derived features already used in gene discovery tasks. The predictive power of the integrated methodology in searching for new disease genes has been found to be competitive against state-of-the-art algorithms.