Current Bioinformatics - Volume 20, Issue 2, 2025

One of the most prevalent epigenetic alterations in all three kingdoms of life is 5mC, which plays a part in a wide range of biological functions. Although in-vitro techniques are more effective in detecting epigenetic alterations, they are time and cost-intensive. Artificial intelligence-based in silico approaches have been used to overcome these obstacles.

This study aimed to develop a ML-based predictor for the detection of 5mC sites in Poaceae.

The objective of this study was the evaluation of machine learning and deep learning models for the prediction of 5mC sites in rice.

In this study, the vectorization of DNA sequences has been performed using three distinct feature sets- Oligo Nucleotide Frequencies (k = 2), Mono-nucleotide Binary Encoding, and Chemical Properties of Nucleotides. Two deep learning models, long short-term memory (LSTM) and Bidirectional LSTM (Bi-LSTM), as well as nine machine learning models, including random forest, gradient boosting, naïve bayes, regression tree, k-Nearest neighbour, support vector machine, adaboost, multiple logistic regression, and artificial neural network, were investigated. Also, bootstrap resampling was used to build more efficient models along with a hybrid feature selection module for dimensional reduction and removal of irrelevant features of the vector space.

Random Forest gains the maximum accuracy, specificity and MCC, i.e., 92.6%, 86.41% and 0.84. Gradient Boosting obtained the maximum sensitivity, i.e., 96.85%. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique showed that the best three models were Random Forest, Gradient Boosting, and Support Vector Machine in terms of accurate prediction of 5mC sites in rice. We developed an R-package, ‘GB5mCPred,’ and it is available in CRAN (https://cran.r-project.org/web/packages/GB5mcPred/index.html). Also, a user-friendly prediction server was made based on this algorithm (http://cabgrid.res.in:5474/).

With nearly equal TOPSIS scores, Random Forest, Gradient Boosting, and Support Vector Machine ended up being the best three models. The major rationale may be found in their architectural design since they are gradual learning models that can capture the 5mC sites more correctly than other learning models.

Breast Cancer (BC) is a significant cause of high mortality amongst women globally and probably will remain a disease posing challenges about its detectability. Advancements in medical imaging technology have improved the accuracy and efficiency of breast cancer classification. However, tumor features' complexity and imaging data variability still pose challenges.

This study proposes the Ensemble Residual-VGG-16 model as a novel combination of the Deep Residual Network (DRN) and VGG-16 architecture. This model is purposely engineered with maximal precision for the task of breast cancer diagnosis based on mammography images. We assessed its performance by accuracy, recall, precision, and the F1-Score. All these metrics indicated the high performance of this Residual-VGG-16 model. The diagnostic residual-VGG16 performed exceptionally well with an accuracy of 99.6%, precision of 99.4%, recall of 99.7%, F1 score of 98.6%, and Mean Intersection over Union (MIoU) of 99.8% with MIAS datasets.

Similarly, the INBreast dataset achieved an accuracy of 93.8%, a precision of 94.2%, a recall of 94.5%, and an F1-score of 93.4%.

The proposed model is a significant advancement in breast cancer diagnosis, with high accuracy and potential as an automated grading.

Circular RNAs (circRNAs) play important regulatory roles in the progression of gastric cancer (GC), but the exact mechanisms governing their regulation remain incompletely understood. Prior studies typically used back-spliced junctions (BSJs) to represent a range of circRNA isoforms, overlooking the prevalence of alternative splicing (AS) events within circRNAs, which could lead to unreliable or even incorrect conclusions in subsequent analyses, hindering our comprehension of the specific functions of circRNAs in GC.

This study aimed to explore the potential functional roles of the dysregulated circRNA transcripts in GC and provide new biomarkers and effective novel therapeutic strategies for GC treatment.

RNA-seq data with rRNA depletion and RNase R treatment was employed to characterize the expression profiles of circRNAs in GC, and RNA-seq data only with rRNA depletion was employed to identify differentially expressed mRNAs in GC. Based on the full-sequence information and accurate isoform-level quantification of circRNA transcripts calculated by the CircAST tool, we performed a series of bioinformatic analyses. A circRNA-miRNA-hub gene regulatory network was constructed to reveal the circRNA-mediated regulation of competing endogenous RNAs in GC, and then the protein-protein interaction (PPI) network was built to identify hub genes.

A total of 18,398 circular transcripts were successfully reconstructed in the samples. Herein, 351 upregulated and 177 downregulated circRNA transcripts were identified. Functional enrichment analysis revealed that their parental genes were strongly associated with GC. After several screening steps, 19 dysregulated circRNA transcripts, 40 related miRNAs, and 65 target genes (mRNAs) were selected to construct the ceRNA network. Through PPI analysis, five hub genes (COL5A2, PDGFRB, SPARC, COL1A2, and COL4A1) were excavated. All these hub genes may play vital roles in gastric cancer cell proliferation and invasion.

Our study revealed a comprehensive profile of full-length circRNA transcripts in GC, which could provide potential prognostic biomarkers and targets for GC treatment. The results would be helpful for further studies on the biological roles of circRNAs in GC and offer new mechanistic insights into the pathogenesis of GC.

Cleft lip and palate are two of the most common craniofacial congenital malformations in humans. It influences tens of millions of patients worldwide. The hazards of this disease are multifaceted, extending beyond the obvious facial malformation to encompass physiological functions, oral health, psychological well-being, and social aspects.

The primary objective of our study is to demonstrate the importance of imaging in detecting cleft lip and palate. By observing the morphological and structural abnormalities involving the lip and palate through imaging methods, this study aims to establish imaging as the primary diagnostic approach for this disease.

In this work, we proposed a novel model to analyze unilateral complete cleft lip and palate after velopharyngeal closure and non-left lip and palate patients from the Department of Stomatology of Xuzhou First People's Hospital, Conical Beam CT (CBCT) images in silicon. In order to demonstrate the generalization, the simulated dataset was constructed using the random disturbance factor, which is from the actual dataset. We extracted several raw features from CBCT images in detail. Then, we proposed a novel feature reconstruction method, including six types of reconstructed factors, to reconstruct the existing features. Then, the reconstructed features weretrained with machine learning algorithms. Finally, the testing and independent data model was utilized to analyze the performance of this work.

By comparing different operator features, the min operator, max operator, average operator, and all operators can achieve good performances in both the testing set and the independent set.

With the different operator features, the majority of classification models, including Gradient Boosting, Hist Gradient Boosting, Multilayer Perceptron, lightGBM, and broadened learning, classification algorithms can get the well-performances in the selected reconstructed feature operators.