Supervised, semi-supervised and unsupervised inference of gene regulatory networks

Maetschke, Stefan; Madhamshettiwar, Piyush B.; Davis, Melissa J.; Ragan, Mark A.

doi:10.1093/bib/bbt034

Cited by 151 publications

(136 citation statements)

References 38 publications

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“…Based on a review article by Maetschke et al [17], an SVM-based method is the best performing method to predict gene regulatory networks among these methods.…”

Section: Supervised Inference Of Regulatory Networkmentioning

confidence: 99%

“…One common solution is to use co-expression-based network algorithms for modeling gene co-functionality networks under the guilt-by-association principle. Applying these well-studied methods on public data sets has significantly improved our understanding of the genome systematically [2,17]. Because of the increasing availability of high-throughput data, many efforts have been devoted to inferring functional relationship networks using heterogeneous genomic data sets.…”

Section: Introductionmentioning

confidence: 99%

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Algorithms for modeling global and context-specific functional relationship networks

Zhu¹,

Panwar²,

Guan³

2015

Brief Bioinform

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Functional genomics has enormous potential to facilitate our understanding of normal and disease-specific physiology. In the past decade, intensive research efforts have been focused on modeling functional relationship networks, which summarize the probability of gene co-functionality relationships. Such modeling can be based on either expression data only or heterogeneous data integration. Numerous methods have been deployed to infer the functional relationship networks, while most of them target the global (non-context-specific) functional relationship networks. However, it is expected that functional relationships consistently reprogram under different tissues or biological processes. Thus, advanced methods have been developed targeting tissue-specific or developmental stage-specific networks. This article brings together the state-of-the-art functional relationship network modeling methods, emphasizes the need for heterogeneous genomic data integration and context-specific network modeling and outlines future directions for functional relationship networks.

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“…Based on a review article by Maetschke et al [17], an SVM-based method is the best performing method to predict gene regulatory networks among these methods.…”

Section: Supervised Inference Of Regulatory Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Algorithms for modeling global and context-specific functional relationship networks

Zhu¹,

Panwar²,

Guan³

2015

Brief Bioinform

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“…The second one is supervised learning method, in which gene expression data and a list of known regulation relationships are required. Inf erence of gene regulatory network is considered as a binary classification problem [13][14]. For each target gene, the regulatory factors which regulate target gene are set as positive samples, while the regulatory factors which could not regulate target ge ne are set as negative samples.…”

Section: Introductionmentioning

confidence: 99%

Infer Gene Regulatory Network Based on the Novel Classifiers Fusion

Zhang¹,

Yang²,

Lv³

2017

IJHIT

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“…Machine learning techniques has been successfully applied to solve various important biomedical problems [8,9] such as genome annotation [10], pattern recognition [11], classification of microarray data [12], inference of gene regulatory networks [13], prediction of drug-target [14] and discovery of gene-gene interaction in disease data [15,16]. In particular, they have been applied to identifying diseaseassociated genes [17 − 22].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Classification-Based Machine Learning Methods for Novel Disease Gene Prediction

Hoài²,

Kwon

2015

Advances in Intelligent Systems and Computing

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Abstract. Prediction of novel genes associated to a disease is an important issue in biomedical research. At early days, annotation-based methods were proposed for this problem. In next stage, with high-throughput technologies, data of interaction between genes/proteins has grown quickly and covered almost genome and proteome, and therefore network-based methods for the issue is becoming prominent. Besides those two methods, the prediction problem can be also approached using machine learning techniques because it can be formulated as a classification task of machine learning. To date, a number of supervised learning techniques and various types of gene/protein annotation data have been used to solve the disease gene classification/prediction problem. However, to the best of our knowledge, there has been no study on the comparison of these methods that work on comprehensive biomedical annotation data. In addition, it is generally true that no classifier is better than others for all classification problems. Therefore, in this study, we compare the performance of disease gene prediction of several supervised learning techniques that have been used in the literature such as Decision Tree Learning, k-Nearest Neighbor, Naive Bayesian, Artificial Neural Networks and Support Vector Machines. We additionally assess Random Forest, a relatively new decision-tree-based ensemble learning method. The simulation results indicate that Random Forest obtained the best performance of all. Also, all methods are stable with the change of known disease genes used as positive training samples.

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Supervised, semi-supervised and unsupervised inference of gene regulatory networks

Cited by 151 publications

References 38 publications

Algorithms for modeling global and context-specific functional relationship networks

Algorithms for modeling global and context-specific functional relationship networks

Infer Gene Regulatory Network Based on the Novel Classifiers Fusion

A Comparative Study of Classification-Based Machine Learning Methods for Novel Disease Gene Prediction

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