# NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification

arXiv: https://arxiv.org/abs/2108.06158 · HTML: https://mlsanity.com/paper/2108.06158/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 65 references that are in the map.
- First posted on arXiv 13 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models](https://mlsanity.com/paper/1402.3144/) (2014)

## Built on by (most cited first, 1 of 1)

- [VitaGraph: Building a Knowledge Graph for Biologically Relevant Learning Tasks](https://mlsanity.com/paper/2505.11185/) (2025)

## Abstract (arXiv)

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.