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Prediction of non-coding RNAs in Fusobacterium nucleatum-infected mice using machine learning
Pradeep Kumar Yadalam1, Thilagar Sivasankari1, Muthupandian Saravanan2
1Department of Periodontics, Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India.
Background:
The anaerobic commensal Fusobacterium nucleatum is scarce in healthy subgingival dental biofilms but is highly prevalent in periodontal pockets. Numerous genome-wide association studies and gene expression studies using microarrays or RNA sequencing (RNA-Seq) have been performed to better understand the genetic architecture of periodontal disease. However, these investigations have limited predictive capacity for identifying RNAs, particularly non-coding RNAs (ncRNAs). The mechanism of regulation of ncRNAs by F. nucleatum to alter disease progression in mice has not been thoroughly investigated.
Objectives:
The aim of the study was to predict previously uncharacterized ncRNAs in F. nucleatum-infected mice using machine learning (ML).
Material And Methods:
Long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) were identified from the periodontitis gene expression dataset (GSE225589) obtained from the Gene Expression Omnibus (GEO) database and subsequently preprocessed. Long non-coding RNAs and circRNAs were labeled based on the gene expression. Transcriptomic features were analyzed using 3 ML algorithms: random forest (RF); adaptive boosting (AdaBoost); and naïve Bayes (NB). The dataset was labeled and divided into training (80%) and testing (20%) subsets with cross-validation. Additionally, receiver operating characteristic (ROC) curves, confusion matrices and area under the ROC curve (AUC) values were determined.
Results:
The RF and AdaBoost models outperformed the NB model in classifying lncRNAs and circRNAs. Both RF and AdaBoost achieved an AUC of 100%, whereas the NB model achieved a slightly lower AUC of 92%.
Conclusions:
This study is the first to apply ML to predict ncRNAs in F. nucleatum-infected mice using transcriptomic data. Random forest and AdaBoost showed superior classification performance in identifying lncRNAs and circRNAs associated with the infection. Further studies with larger cohorts and external validation are needed to confirm these findings.
