1. Introduction

About

leADS (multi-label learning based on active dataset subsampling) is a simple framework, that leverages the idea of subsampling pathway data to reduce the negative impact of training loss due to imbalances in the distribution of pathways in the dataset. Specifically, leADS performs an iterative process to 1)- construct an acquisition model in an ensemble framework; 2) select the most informative data using an appropriate acquisition function; and 3) train on selected subsamples. The ensemble approach was sought to enhance the generalization ability of the multi-label learning systems by concurrently building and executing a group of multi-label base learners, where each is assigned a portion of samples, to ensure proper learning of class labels (e.g. pathways). leADS was evaluated on the pathway prediction task using 10 multi-organism pathway datasets, where the experiments revealed that leADS achieved very compelling and competitive performances against the state-of-the-art pathway inference algorithms. For more information about leADS, please visit our paper.

Citing

If you find leADS useful in your research, please consider citing the following paper:

M. A. Basher, Abdur Rahman and Hallam, Steven J.. "Multi-label pathway prediction based on active dataset subsampling.", bioRxiv (2020).

Contact information

For any inquiries or issues, please contact Abdurrahman Abul-Basher at: [email protected]