Integrating Gene Ontology Based Grouping and Ranking into the Machine Learning Algorithm for Gene Expression Data Analysis

Yousef, Malik; Sayici, Ahmet; Bakır-Güngör, Burcu

doi:10.1007/978-3-030-87101-7_20

Cited by 11 publications

(11 citation statements)

References 17 publications

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“…filter, wrapper, and embedded methods such as ( Yousef et al, 2020 ). Recent feature selection methods make use of the biological knowledge, which is embedded in the machine learning algorithm ( Yousef, Sayıcı& Bakir-Gungor, 2021 ; Yousef, Abdallah & Allmer, 2019 ; Yousef et al, 2021 ). Applications of biological domain knowledge based feature selection methods for gene expression data can be found in: Yousef, Kumar & Bakir-Gungor (2021) .…”

Section: Methodsmentioning

confidence: 99%

Inflammatory bowel disease biomarkers of human gut microbiota selected via different feature selection methods

Bakır-Güngör

Hacılar

Jabeer

et al. 2022

PeerJ

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The tremendous boost in next generation sequencing and in the “omics” technologies makes it possible to characterize the human gut microbiome—the collective genomes of the microbial community that reside in our gastrointestinal tract. Although some of these microorganisms are considered to be essential regulators of our immune system, the alteration of the complexity and eubiotic state of microbiota might promote autoimmune and inflammatory disorders such as diabetes, rheumatoid arthritis, Inflammatory bowel diseases (IBD), obesity, and carcinogenesis. IBD, comprising Crohn’s disease and ulcerative colitis, is a gut-related, multifactorial disease with an unknown etiology. IBD presents defects in the detection and control of the gut microbiota, associated with unbalanced immune reactions, genetic mutations that confer susceptibility to the disease, and complex environmental conditions such as westernized lifestyle. Although some existing studies attempt to unveil the composition and functional capacity of the gut microbiome in relation to IBD diseases, a comprehensive picture of the gut microbiome in IBD patients is far from being complete. Due to the complexity of metagenomic studies, the applications of the state-of-the-art machine learning techniques became popular to address a wide range of questions in the field of metagenomic data analysis. In this regard, using IBD associated metagenomics dataset, this study utilizes both supervised and unsupervised machine learning algorithms, (i) to generate a classification model that aids IBD diagnosis, (ii) to discover IBD-associated biomarkers, (iii) to discover subgroups of IBD patients using k-means and hierarchical clustering approaches. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF), min redundancy max relevance (mRMR), Select K Best (SKB), Information Gain (IG) and Extreme Gradient Boosting (XGBoost). In our experiments with 100-fold Monte Carlo cross-validation (MCCV), XGBoost, IG, and SKB methods showed a considerable effect in terms of minimizing the microbiota used for the diagnosis of IBD and thus reducing the cost and time. We observed that compared to Decision Tree, Support Vector Machine, Logitboost, Adaboost, and stacking ensemble classifiers, our Random Forest classifier resulted in better performance measures for the classification of IBD. Our findings revealed potential microbiome-mediated mechanisms of IBD and these findings might be useful for the development of microbiome-based diagnostics.

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Section: Methodsmentioning

confidence: 99%

Inflammatory bowel disease biomarkers of human gut microbiota selected via different feature selection methods

Bakır-Güngör

Hacılar

Jabeer

et al. 2022

PeerJ

Self Cite

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“…In general, G-S-M is a grouping-based feature selection approach, where the groups are associated with a pre-existing biological knowledge. This generic approach has been used by several bioinformatics tools such as miRcorrNet ( Yousef and Goy., 2021 ), maTE ( Yousef et al, 2019 ), SVM-RNE ( Yousef et al, 2009 ), Integrating Gene Ontology Based Grouping and Ranking ( Yousef et al, 2021 ), CogNet ( Yousef et al, 2021 ), SVM-RCE ( Yousef et al, 2007 ), SVM-RCE-R ( Yousef and Bakir-Gungor, 2021 ), PriPath ( Yousef et al, 2022 ), miRModuleNet ( Yousef et al, 2022 ), TextNetTopics ( Yousef and Voskergian, 2022 ), GediNet ( Qumsiyeh et al, 2022 ). These different G-S-M approaches are also reviewed in ( Yousef et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning

et al. 2023

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During recent years, biological experiments and increasing evidence have shown that microRNAs play an important role in the diagnosis and treatment of human complex diseases. Therefore, to diagnose and treat human complex diseases, it is necessary to reveal the associations between a specific disease and related miRNAs. Although current computational models based on machine learning attempt to determine miRNA-disease associations, the accuracy of these models need to be improved, and candidate miRNA-disease relations need to be evaluated from a biological perspective. In this paper, we propose a computational model named miRdisNET to predict potential miRNA-disease associations. Specifically, miRdisNET requires two types of data, i.e., miRNA expression profiles and known disease-miRNA associations as input files. First, we generate subsets of specific diseases by applying the grouping component. These subsets contain miRNA expressions with class labels associated with each specific disease. Then, we assign an importance score to each group by using a machine learning method for classification. Finally, we apply a modeling component and obtain outputs. One of the most important outputs of miRdisNET is the performance of miRNA-disease prediction. Compared with the existing methods, miRdisNET obtained the highest AUC value of .9998. Another output of miRdisNET is a list of significant miRNAs for disease under study. The miRNAs identified by miRdisNET are validated via referring to the gold-standard databases which hold information on experimentally verified microRNA-disease associations. miRdisNET has been developed to predict candidate miRNAs for new diseases, where miRNA-disease relation is not yet known. In addition, miRdisNET presents candidate disease-disease associations based on shared miRNA knowledge. The miRdisNET tool and other supplementary files are publicly available at: https://github.com/malikyousef/miRdisNET.

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“…miRModuleNet was developed based on the generic approach named G-S-M. This generic approach was adopted by different tools such as SVM RCE, SVM-RCE-R ( Yousef et al, 2007 ; Yousef et al, 2021a ), maTE ( Yousef et al, 2019 ), CogNet ( Yousef et al, 2021d ), miRcorrNet ( Yousef et al, 2021b ), and Integrating Gene Ontology Based Grouping and Ranking ( Yousef et al, 2021c ). Recently, these tools and their competitors were reviewed in ( Yousef et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

miRModuleNet: Detecting miRNA-mRNA Regulatory Modules

2022

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Increasing evidence that microRNAs (miRNAs) play a key role in carcinogenesis has revealed the need for elucidating the mechanisms of miRNA regulation and the roles of miRNAs in gene-regulatory networks. A better understanding of the interactions between miRNAs and their mRNA targets will provide a better understanding of the complex biological processes that occur during carcinogenesis. Increased efforts to reveal these interactions have led to the development of a variety of tools to detect and understand these interactions. We have recently described a machine learning approach miRcorrNet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and the group members are genes with expression patterns that are correlated with this specific miRNA. The miRcorrNet tool requires two types of -omics data, miRNA and mRNA expression profiles, as an input file. In this study we describe miRModuleNet, which groups mRNA (genes) that are correlated with each miRNA to form a star shape, which we identify as a miRNA-mRNA regulatory module. A scoring procedure is then applied to each module to further assess their contribution in terms of classification. An important output of miRModuleNet is that it provides a hierarchical list of significant miRNA-mRNA regulatory modules. miRModuleNet was further validated on external datasets for their disease associations, and functional enrichment analysis was also performed. The application of miRModuleNet aids the identification of functional relationships between significant biomarkers and reveals essential pathways involved in cancer pathogenesis. The miRModuleNet tool and all other supplementary files are available at https://github.com/malikyousef/miRModuleNet/

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Integrating Gene Ontology Based Grouping and Ranking into the Machine Learning Algorithm for Gene Expression Data Analysis

Cited by 11 publications

References 17 publications

Inflammatory bowel disease biomarkers of human gut microbiota selected via different feature selection methods

Inflammatory bowel disease biomarkers of human gut microbiota selected via different feature selection methods

miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning

miRModuleNet: Detecting miRNA-mRNA Regulatory Modules

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