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Article ; Online: A network-based method for predicting disease-associated enhancers.

Le, Duc-Hau

PloS one

2021 Volume 16, Issue 12, Page(s) e0260432

Abstract: Background: Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them ... ...

Abstract	Background: Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them have been found to be associated with diseases. This raises a pressing need to develop computational methods to predict associations between diseases and enhancers. Results: In this study, we assumed that enhancers sharing target genes could be associated with similar diseases to predict the association. Thus, we built an enhancer functional interaction network by connecting enhancers significantly sharing target genes, then developed a network diffusion method RWDisEnh, based on a random walk with restart algorithm, on networks of diseases and enhancers to globally measure the degree of the association between diseases and enhancers. RWDisEnh performed best when the disease similarities are integrated with the enhancer functional interaction network by known disease-enhancer associations in the form of a heterogeneous network of diseases and enhancers. It was also superior to another network diffusion method, i.e., PageRank with Priors, and a neighborhood-based one, i.e., MaxLink, which simply chooses the closest neighbors of known disease-associated enhancers. Finally, we showed that RWDisEnh could predict novel enhancers, which are either directly or indirectly associated with diseases. Conclusions: Taken together, RWDisEnh could be a potential method for predicting disease-enhancer associations.
MeSH term(s)	Algorithms ; Computational Biology/methods ; Disease/genetics ; Enhancer Elements, Genetic ; Genetic Predisposition to Disease ; Humans ; Neural Networks, Computer ; Transcription, Genetic
Language	English
Publishing date	2021-12-08
Publishing country	United States
Document type	Journal Article
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0260432
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A network-based method for predicting disease-associated enhancers.

Duc-Hau Le

PLoS ONE, Vol 16, Iss 12, p e

2021 Volume 0260432

Abstract: Background Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them have ... ...

Abstract	Background Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them have been found to be associated with diseases. This raises a pressing need to develop computational methods to predict associations between diseases and enhancers. Results In this study, we assumed that enhancers sharing target genes could be associated with similar diseases to predict the association. Thus, we built an enhancer functional interaction network by connecting enhancers significantly sharing target genes, then developed a network diffusion method RWDisEnh, based on a random walk with restart algorithm, on networks of diseases and enhancers to globally measure the degree of the association between diseases and enhancers. RWDisEnh performed best when the disease similarities are integrated with the enhancer functional interaction network by known disease-enhancer associations in the form of a heterogeneous network of diseases and enhancers. It was also superior to another network diffusion method, i.e., PageRank with Priors, and a neighborhood-based one, i.e., MaxLink, which simply chooses the closest neighbors of known disease-associated enhancers. Finally, we showed that RWDisEnh could predict novel enhancers, which are either directly or indirectly associated with diseases. Conclusions Taken together, RWDisEnh could be a potential method for predicting disease-enhancer associations.
Keywords	Medicine ; R ; Science ; Q
Subject code	006 ; 610
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A network-based method for predicting disease-associated enhancers

Duc-Hau Le

PLoS ONE, Vol 16, Iss

2021 Volume 12

Abstract	Background Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them have been found to be associated with diseases. This raises a pressing need to develop computational methods to predict associations between diseases and enhancers. Results In this study, we assumed that enhancers sharing target genes could be associated with similar diseases to predict the association. Thus, we built an enhancer functional interaction network by connecting enhancers significantly sharing target genes, then developed a network diffusion method RWDisEnh, based on a random walk with restart algorithm, on networks of diseases and enhancers to globally measure the degree of the association between diseases and enhancers. RWDisEnh performed best when the disease similarities are integrated with the enhancer functional interaction network by known disease-enhancer associations in the form of a heterogeneous network of diseases and enhancers. It was also superior to another network diffusion method, i.e., PageRank with Priors, and a neighborhood-based one, i.e., MaxLink, which simply chooses the closest neighbors of known disease-associated enhancers. Finally, we showed that RWDisEnh could predict novel enhancers, which are either directly or indirectly associated with diseases. Conclusions Taken together, RWDisEnh could be a potential method for predicting disease-enhancer associations.
Keywords	Medicine ; R ; Science ; Q
Subject code	006 ; 610
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Machine learning-based approaches for disease gene prediction.

Le, Duc-Hau

Briefings in functional genomics

2020 Volume 19, Issue 5-6, Page(s) 350–363

Abstract: Disease gene prediction is an essential issue in biomedical research. In the early days, annotation-based approaches were proposed for this problem. With the development of high-throughput technologies, interaction data between genes/proteins have grown ... ...

Abstract	Disease gene prediction is an essential issue in biomedical research. In the early days, annotation-based approaches were proposed for this problem. With the development of high-throughput technologies, interaction data between genes/proteins have grown quickly and covered almost genome and proteome; thus, network-based methods for the problem become prominent. In parallel, machine learning techniques, which formulate the problem as a classification, have also been proposed. Here, we firstly show a roadmap of the machine learning-based methods for the disease gene prediction. In the beginning, the problem was usually approached using a binary classification, where positive and negative training sample sets are comprised of disease genes and non-disease genes, respectively. The disease genes are ones known to be associated with diseases; meanwhile, non-disease genes were randomly selected from those not yet known to be associated with diseases. However, the later may contain unknown disease genes. To overcome this uncertainty of defining the non-disease genes, more realistic approaches have been proposed for the problem, such as unary and semi-supervised classification. Recently, more advanced methods, including ensemble learning, matrix factorization and deep learning, have been proposed for the problem. Secondly, 12 representative machine learning-based methods for the disease gene prediction were examined and compared in terms of prediction performance and running time. Finally, their advantages, disadvantages, interpretability and trust were also analyzed and discussed.
MeSH term(s)	Algorithms ; Humans ; Machine Learning
Language	English
Publishing date	2020-07-05
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2540916-5
ISSN	2041-2657 ; 2041-2649 ; 2041-2647
ISSN (online)	2041-2657
ISSN	2041-2649 ; 2041-2647
DOI	10.1093/bfgp/elaa013
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: UFO: A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization.

Le, Duc-Hau

PloS one

2020 Volume 15, Issue 7, Page(s) e0235670

Abstract: Background: Biomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated ... ...

Abstract	Background: Biomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated biomedical entities, analyzing enrichment for a set of biomedical entities. Many semantic similarity calculation and enrichment analysis methods have been proposed for such applications. Also, a number of tools implementing the methods have been developed on different platforms. However, these tools have implemented a small number of the semantic similarity calculation and enrichment analysis methods for a certain type of biomedical ontology. Note that the methods can be applied to all types of biomedical ontologies. More importantly, each method can be dominant in different applications; thus, users have more choice with more number of methods implemented in tools. Also, more functions would facilitate their task with ontology. Results: In this study, we developed a Cytoscape app, named UFO, which unifies most of the semantic similarity measures for between-term and between-entity similarity calculation for all types of biomedical ontologies in OBO format. Based on the similarity calculation, UFO can calculate the similarity between two sets of entities and weigh imported entity networks as well as generate functional similarity networks. Besides, it can perform enrichment analysis of a set of entities by different methods. Moreover, UFO can visualize structural relationships between ontology terms, annotating relationships between entities and terms, and functional similarity between entities. Finally, we demonstrated the ability of UFO through some case studies on finding the best semantic similarity measures for assessing the similarity between human disease phenotypes, constructing biomedical entity functional similarity networks for predicting disease-associated biomarkers, and performing enrichment analysis on a set of similar phenotypes. Conclusions: Taken together, UFO is expected to be a tool where biomedical ontologies can be exploited for various biomedical applications. Availability: UFO is distributed as a Cytoscape app, and can be downloaded freely at Cytoscape App (http://apps.cytoscape.org/apps/ufo) for non-commercial use.
MeSH term(s)	Biological Ontologies ; Biomarkers ; Diagnostic Tests, Routine ; Humans ; Semantics ; Software ; Vocabulary, Controlled
Chemical Substances	Biomarkers
Language	English
Publishing date	2020-07-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0235670
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: oCEM: Automatic detection and analysis of overlapping co-expressed gene modules.

Nguyen, Quang-Huy / Le, Duc-Hau

BMC genomics

2022 Volume 23, Issue 1, Page(s) 39

Abstract: Background: When it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. The nature of module detection is the use of ... ...

Abstract	Background: When it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. The nature of module detection is the use of unsupervised clustering approaches and algorithms. Those methods are advanced undoubtedly, but the selection of a certain clustering method for sample- and gene-clustering tasks is separate, in which the latter task is often more complicated. Results: This study presented an R-package, Overlapping CoExpressed gene Module (oCEM), armed with the decomposition methods to solve the challenges above. We also developed a novel auxiliary statistical approach to select the optimal number of principal components using a permutation procedure. We showed that oCEM outperformed state-of-the-art techniques in the ability to detect biologically relevant modules additionally. Conclusions: oCEM helped non-technical users easily perform complicated statistical analyses and then gain robust results. oCEM and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/oCEM .
MeSH term(s)	Algorithms ; Cluster Analysis ; Gene Expression Profiling ; Gene Regulatory Networks
Language	English
Publishing date	2022-01-08
Publishing country	England
Document type	Journal Article
ISSN	1471-2164
ISSN (online)	1471-2164
DOI	10.1186/s12864-021-08072-5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: UFO

Duc-Hau Le

PLoS ONE, Vol 15, Iss 7, p e

A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization.

2020 Volume 0235670

Abstract: Background Biomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated biomedical ... ...

Abstract	Background Biomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated biomedical entities, analyzing enrichment for a set of biomedical entities. Many semantic similarity calculation and enrichment analysis methods have been proposed for such applications. Also, a number of tools implementing the methods have been developed on different platforms. However, these tools have implemented a small number of the semantic similarity calculation and enrichment analysis methods for a certain type of biomedical ontology. Note that the methods can be applied to all types of biomedical ontologies. More importantly, each method can be dominant in different applications; thus, users have more choice with more number of methods implemented in tools. Also, more functions would facilitate their task with ontology. Results In this study, we developed a Cytoscape app, named UFO, which unifies most of the semantic similarity measures for between-term and between-entity similarity calculation for all types of biomedical ontologies in OBO format. Based on the similarity calculation, UFO can calculate the similarity between two sets of entities and weigh imported entity networks as well as generate functional similarity networks. Besides, it can perform enrichment analysis of a set of entities by different methods. Moreover, UFO can visualize structural relationships between ontology terms, annotating relationships between entities and terms, and functional similarity between entities. Finally, we demonstrated the ability of UFO through some case studies on finding the best semantic similarity measures for assessing the similarity between human disease phenotypes, constructing biomedical entity functional similarity networks for predicting disease-associated biomarkers, and performing enrichment analysis on a set of similar phenotypes. Conclusions Taken together, ...
Keywords	Medicine ; R ; Science ; Q
Subject code	005
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Editorial: Computational and Experimental Approaches in Exploring the Role of Genetics and Genomics in Multifactorial Diseases.

Le, Duc-Hau / Nguyen, Quang-Huy / Dao, Lan T M

Frontiers in genetics

2022 Volume 13, Page(s) 873069

Language	English
Publishing date	2022-03-15
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2022.873069
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Similarity Calculation, Enrichment Analysis, and Ontology Visualization of Biomedical Ontologies using UFO.

Nguyen, Quang-Huy / Le, Duc-Hau

Current protocols

2021 Volume 1, Issue 4, Page(s) e115

Abstract: The rapid growth of biomedical ontologies observed in recent years has been reported to be useful in various applications. In this article, we propose two main-function protocols-term-related and entity-related-with the three most common ontology ... ...

Abstract	The rapid growth of biomedical ontologies observed in recent years has been reported to be useful in various applications. In this article, we propose two main-function protocols-term-related and entity-related-with the three most common ontology analyses, including similarity calculation, enrichment analysis, and ontology visualization, which can be done by separate methods. Many previously developed tools implementing those methods run on different platforms and implement a limited number of the methods for similarity calculation and enrichment analysis tools for a specific type of biomedical ontology, although any type can be acceptable. Moreover, depending on each application, methods have distinct advantages; thus, the greater the number of methods a tool has, the better decisions that users make. The protocol here implements all the analyses above using an advanced popular tool called UFO. UFO is a Cytoscape app that unifies most of the semantic similarity measures for between-term and between-entity similarity calculation for biomedical ontologies in OBO format, which can calculate the similarity between two sets of entities and weigh imported entity networks, as well as generate functional similarity networks. The complete protocol can be performed in 30 min and is designed for use by biologists with no prior bioinformatics training. © 2021 Wiley Periodicals LLC. Basic Protocol: Running UFO using a list of input Gene Ontology, Disease Ontology, or Human Phenotype Ontology data.
MeSH term(s)	Biological Ontologies ; Computational Biology ; Diagnostic Tests, Routine ; Gene Ontology ; Humans ; Semantics
Language	English
Publishing date	2021-02-18
Publishing country	United States
Document type	Journal Article
ISSN	2691-1299
ISSN (online)	2691-1299
DOI	10.1002/cpz1.115
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Identification and Validation of a Novel Three Hub Long Noncoding RNAs With m6A Modification Signature in Low-Grade Gliomas

Quang-Huy Nguyen / Tin Nguyen / Duc-Hau Le

Frontiers in Molecular Biosciences, Vol

2022 Volume 9

Abstract: It has been evident that N6-methyladenosine (m6A)-modified long noncoding RNAs (m6A-lncRNAs) involves regulating tumorigenesis, invasion, and metastasis for various cancer types. In this study, we sought to pick computationally up a set of 13 hub m6A- ... ...

Abstract	It has been evident that N6-methyladenosine (m6A)-modified long noncoding RNAs (m6A-lncRNAs) involves regulating tumorigenesis, invasion, and metastasis for various cancer types. In this study, we sought to pick computationally up a set of 13 hub m6A-lncRNAs in light of three state-of-the-art tools WGCNA, iWGCNA, and oCEM, and interrogated their prognostic values in brain low-grade gliomas (LGG). Of the 13 hub m6A-lncRNAs, we further detected three hub m6A-lncRNAs as independent prognostic risk factors, including HOXB-AS1, ELOA-AS1, and FLG-AS1. Then, the m6ALncSig model was built based on these three hub m6A-lncRNAs. Patients with LGG next were divided into two groups, high- and low-risk, based on the median m6ALncSig score. As predicted, the high-risk group was more significantly related to mortality. The prognostic signature of m6ALncSig was validated using internal and external cohorts. In summary, our work introduces a high-confidence prognostic prediction signature and paves the way for using m6A-lncRNAs in the signature as new targets for treatment of LGG.
Keywords	low-grade gliomas ; long noncoding RNA ; N6-methyladenosine ; prognostic index ; hub genes ; signature ; Biology (General) ; QH301-705.5
Language	English
Publishing date	2022-02-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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