miRcorrNet: machine learning-based integration of miRNA and mRNA expression profiles, combined with feature grouping and ranking

Yousef, Malik; Goy, Gokhan; Mitra, Ramkrishna; Eischen, Christine M.; Jabeer, Amhar; Bakir-Gungor, Burcu

doi:10.7717/peerj.11458

Cited by 26 publications

(22 citation statements)

References 65 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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“…Moreover, more advanced approaches that integrate biological knowledge into the machine learning algorithm for performing feature selection or for selecting groups of features are used in different recent tools. Such an approach was adopted by different tools such as SVM RCE, SVM-RCE-R [71]- [73], maTE [74], CogNet [75], miRcorrNet [76], and Integrating Gene Ontology Based Grouping and Ranking [77]. Recently, these tools and their competitors were reviewed in [78].…”

Section: Resultsmentioning

confidence: 99%

Prediction of Linear Cationic Antimicrobial Peptides Active against Gram Negative and Positive Bacteria Based on Machine Learning Models

Söylemez¹,

Yousef²,

Kesmen³

et al. 2022

Preprint

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Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in-vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies.

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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%

“…To the best of our knowledge, there are only two available tools that can adequately address the classification problem using integrated miRNA-mRNA groups. These bioinformatics tools are maTE ( Yousef et al, 2019 ) and miRcorrNet ( Yousef et al, 2021b ). The main difference between these two tools is the miRNA-mRNA grouping methodology.…”

Section: Introductionmentioning

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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miRcorrNet: machine learning-based integration of miRNA and mRNA expression profiles, combined with feature grouping and ranking

Cited by 26 publications

References 65 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

Prediction of Linear Cationic Antimicrobial Peptides Active against Gram Negative and Positive Bacteria Based on Machine Learning Models

miRModuleNet: Detecting miRNA-mRNA Regulatory Modules

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