Paolo Mignone1,2, Gianvito Pio1,2, Domenica D'Elia3, Michelangelo Ceci1,2,4. 1. Department of Computer Science, University of Bari Aldo Moro, Bari 70125, Italy. 2. National Interuniversity Consortium for Informatics (CINI), Roma 00185, Italy. 3. Institute for Biomedical Technologies, CNR, Institute for Biomedical Technologies, Bari 70126, Italy. 4. Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana 1000, Slovenia.
Abstract
MOTIVATION: The reconstruction of gene regulatory networks (GRNs) from gene expression data has received increasing attention in recent years, due to its usefulness in the understanding of regulatory mechanisms involved in human diseases. Most of the existing methods reconstruct the network through machine learning approaches, by analyzing known examples of interactions. However, (i) they often produce poor results when the amount of labeled examples is limited, or when no negative example is available and (ii) they are not able to exploit information extracted from GRNs of other (better studied) related organisms, when this information is available. RESULTS: In this paper, we propose a novel machine learning method that overcomes these limitations, by exploiting the knowledge about the GRN of a source organism for the reconstruction of the GRN of the target organism, by means of a novel transfer learning technique. Moreover, the proposed method is natively able to work in the positive-unlabeled setting, where no negative example is available, by fruitfully exploiting a (possibly large) set of unlabeled examples. In our experiments, we reconstructed the human GRN, by exploiting the knowledge of the GRN of Mus musculus. Results showed that the proposed method outperforms state-of-the-art approaches and identifies previously unknown functional relationships among the analyzed genes. AVAILABILITY AND IMPLEMENTATION: http://www.di.uniba.it/∼mignone/systems/biosfer/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The reconstruction of gene regulatory networks (GRNs) from gene expression data has received increasing attention in recent years, due to its usefulness in the understanding of regulatory mechanisms involved in human diseases. Most of the existing methods reconstruct the network through machine learning approaches, by analyzing known examples of interactions. However, (i) they often produce poor results when the amount of labeled examples is limited, or when no negative example is available and (ii) they are not able to exploit information extracted from GRNs of other (better studied) related organisms, when this information is available. RESULTS: In this paper, we propose a novel machine learning method that overcomes these limitations, by exploiting the knowledge about the GRN of a source organism for the reconstruction of the GRN of the target organism, by means of a novel transfer learning technique. Moreover, the proposed method is natively able to work in the positive-unlabeled setting, where no negative example is available, by fruitfully exploiting a (possibly large) set of unlabeled examples. In our experiments, we reconstructed the humanGRN, by exploiting the knowledge of the GRN of Mus musculus. Results showed that the proposed method outperforms state-of-the-art approaches and identifies previously unknown functional relationships among the analyzed genes. AVAILABILITY AND IMPLEMENTATION: http://www.di.uniba.it/∼mignone/systems/biosfer/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Henry E Pratt; Gregory R Andrews; Nishigandha Phalke; Michael J Purcaro; Arjan van der Velde; Jill E Moore; Zhiping Weng Journal: Nucleic Acids Res Date: 2022-01-07 Impact factor: 19.160