DNA methylation change has been useful for cancer biomarker discovery, classification, and potential treatment development. So far, existing methods use either differentially methylated CpG sites or combined CpG sites, namely differentially methylated regions, that can be mapped to genes. However, such methylation signal mapping has limitations. To address these limitations, in this study, we introduced a combinatorial framework using linear regression, differential expression, deep learning method for accurate biological interpretation of DNA methylation through integrating DNA methylation data and corresponding TCGA gene expression data. We demonstrated it for uterine cervical cancer. First, we pre-filtered outliers from the data set and then determined the predicted gene expression value from the pre-filtered methylation data through linear regression. We identified differentially expressed genes (DEGs) by Empirical Bayes test using $\mathit{Limma}$. Then we applied a deep learning method, “nnet” to classify the cervical cancer label of those DEGs to determine all classification metrics including accuracy and area under curve (AUC) through 10-fold cross validation. We applied our approach to uterine cervical cancer DNA methylation dataset (NCBI accession ID: GSE30760, 27,578 features covering 63 tumor and 152 matched normal samples). After linear regression and differential expression analysis, we obtained 6287 DEGs with false discovery rate (FDR) $<0.001$. After performing deep learning analysis, we obtained average classification accuracy $90.69\%$ ($\pm 1.97\%$) of the uterine cervical cancerous labels. This performance is better than that of other peer methods. We performed in-degree and out-degree hub gene network analysis using $\mathit{Cytoscape}$. We reported five top in-degree genes ($\mathit{PAIP}\mathit{2}$, $\mathit{GRWD}\mathit{1}$, $\mathit{VPS}\mathit{4}\mathit{B}$, $\mathit{CRADD}$ and $\mathit{LLPH}$) and five top out-degree genes ($\mathit{MRPL}\mathit{35}$, $\mathit{FAM}\mathit{177}\mathit{A}\mathit{1}$, $\mathit{STAT}\mathit{4}$, $\mathit{ASPSCR}\mathit{1}$ and $\mathit{FABP}\mathit{7}$). After that, we performed KEGG pathway and Gene Ontology enrichment analysis of DEGs using tool WebGestalt(WEB-based Gene SeT AnaLysis Toolkit). In summary, our proposed framework that integrated linear regression, differential expression, deep learning provides a robust approach to better interpret DNA methylation analysis and gene expression data in disease study.

MicroRNAs (miRNAs) are non-coding, short (21-23nt) regulators of protein-coding genes that are generally transcribed first into primary miRNA (pri-miR), followed by the generation of precursor miRNA (pre-miR). This finally leads to the production of the mature miRNA. A large amount of information is available on the pre- and mature miRNAs. However, very little is known about the pri-miRs, due to a lack of knowledge about their transcription start sites (TSSs). Based on the genomic loci, miRNAs can be categorized into two types —intragenic (intra-miR) and intergenic (inter-miR). While it is already an established fact that intra-miRs are commonly transcribed in conjunction with their host genes, the transcription machinery of inter-miRs is poorly understood. Although it is assumed that miRNA promoters are similar in structure to gene promoters, since both are transcribed by RNA polymerase II (Pol II), computational validations exhibit poor performance of gene promoter prediction methods on miRNAs. In this paper, we concentrate on the problem of TSS prediction for miRNAs. The present study begins with the identification of positive and negative promoter samples from recently published data stemming from RNA-sequencing studies. From these samples of experimentally validated miRNA TSSs, a number of standard sequence features are extracted. Furthermore, to account for potential footprints related to promoter regulation by CpG dinucleotide targeted DNA methylation, a number of novel features are defined. We develop a support vector machine (SVM) with RBF kernel for the prediction of miRNA TSSs trained on human miRNA promoters. A novel feature reduction technique based on archived multi-objective simulated annealing (AMOSA) identifies the final set of features. The resulting model trained on miRNA promoters shows improved performance over the one trained on protein-coding gene promoters in terms of classification accuracy, sensitivity and specificity. Results are also reported for a completely independent biologically validated test set. In a part of the investigation, the proposed approach is used to predict protein-coding gene TSSs. It shows a significantly improved performance when compared to previously published gene TSS prediction methods.

In this era, feature clustering is a prominent technique in data mining. Feature clustering has also huge applications in biomedical research for multiple purposes including grouping, feature reduction, and many more. The Internet of Medical Things (IoMT) is a promising and emerging field of research that is having a major impact on knowledge retrieval and networking. IoMT also has significant application in biomedical research regarding remote monitoring and remote healthcare services. In this COVID-19 pandemic situation, psychological effects and human reactions have become a major concern of further research. A dataset can be reduced in size by using feature selection techniques. To facilitate subsequent processing, this will make the data easier to manage. Feature selection is also used to clean, reduce, and reduce dimensions of data. The clustering method has proven to be a powerful tool for finding patterns and structures in both labeled and unlabeled datasets. Our study basically provides various state-of-the-art methods regarding medical IoMT for remote healthcare, feature clustering for information retrieval regarding biomedical science. In this study, we have studied five different types of feature selection algorithms such as minimum redundancy maximum relevance (mRMR), random forest, normalized mutual information feature selection (NMIFS), F-test, and chi-square and five different types of clustering algorithms like hierarchical clustering, density-based spatial clustering of applications with noise (DBSCAN) clustering, K-means clustering, shrinkage clustering, and fuzzy C-means clustering. Finally, this study is very useful to understand and apply the appropriate IoMT, feature clustering, and catharsis on the various biomedical applications for the benevolence of society. Received: 31 March 2023 | Revised: 22 June 2023 | Accepted: 30 June 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in UCSC Xena at https://xenabrowser.net/datapages. The data that support the findings of this study are openly available in National Cancer Institute at https://www.cancer.gov/ccg/access-data. The data that support the findings of this study are openly available in NCBI at https://www.ncbi.nlm.nih.gov/geo/. Author Contribution Statement Atikul Islam: Conceptualization, Methodology, Software, Validation, Formal analysis, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration. Soumita Seth: Methodology, Software, Validation, Formal analysis, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration. Tapas Bhadra: Methodology, Software, Validation, Formal analysis, Resources, Data curation, Writing - review & editing, Visualization, Supervision. Saurav Mallik: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration. Arup Roy: Software, Validation, Formal analysis, Investigation, Resources, Data curation. Aimin Li: Resources, Data curation. Manash Sarkar: Formal analysis, Investigation, Resources, Data curation.

In this current era, the identification of both known and novel cell types, the representation of cells, predicting cell fates, classifying various tumor types, and studying heterogeneity in various cells are the key areas of interest in the analysis of single-cell RNA sequencing (scRNA-seq) data. Due to the nature of the data, cluster identification in single-cell sequencing data with high dimensions presents several difficulties. In this paper, we introduce a new framework that combines various strategies such as imputed matrix, minimum redundancy maximum relevance (MRMR) feature selection, and shrinkage clustering to discover gene signatures from scRNA-seq data. Firstly, we conducted the pre-filtering of the “drop-out” value in the data focusing solely on imputing the identified “drop-out” values. Next, we applied the MRMR feature selection method to the imputed data and obtained the top 100 features based on the MRMR feature selection optimization scores for further downstream analysis. Thereafter, we employed shrinkage clustering on the selected feature matrix to identify the cell clusters using a global optimization approach. Finally, we applied the Limma-Voom R tool employing voom normalization and an empirical Bayes test to detect differentially expressed features with a false discovery rate (FDR) < 0.001. In addition, we performed the KEGG pathway and gene ontology enrichment analysis of the identified biomarkers using David 6.8 software. Furthermore, we conducted miRNA target detection for the top gene markers and performed miRNA target gene interaction network analysis using the Cytoscape online tool. Subsequently, we compared our detected 100 markers with our previously detected top 100 cluster-specified markers ranked by FDR of the latest published article and discovered three common markers; namely, $Cyp2b10$, $Mt1$, $Alpi$, along with 97 novel markers. In addition, the Gene Set Enrichment Analysis (GSEA) of both marker sets also yields similar outcomes. Apart from this, we performed another comparative study with another published method, demonstrating that our model detects more significant markers than that model. To assess the efficiency of our framework, we apply it to another dataset and identify 20 strongly significant up-regulated markers. Additionally, we perform a comparative study of different imputation methods and include an ablation study to prove that every key phase of our framework is essential and strongly recommended. In summary, our proposed integrated framework efficiently discovers differentially expressed stronger gene signatures as well as up-regulated markers in single-cell RNA sequencing data.

Here we propose a novel unsupervised feature selection by combining hierarchical feature clustering with singular value decomposition (SVD). The proposed algorithm first generates several feature clusters by adopting hierarchical clustering on the feature space and then applies SVD to each of these feature clusters to identify the feature that contributes most to the SVD-entropy. The proposed feature selection method selects an optimal feature subset that not only minimizes the mutual dependency among the selected features but also maximizes mutual dependency of the selected features against their nearest neighbor non-selected features. Each of the selected features also contributes the maximum SVD-entropy among all features of the same feature cluster. The experimental results demonstrate that proposed algorithm performs well against state-of-the-art methods of feature selection in terms of various evaluation criteria. The superiority of the proposed algorithm is demonstrated through analysis of Acute Myeloid Leukemia (AML) multi-omics data that consist of five datasets: gene expression, exon expression, methylation, microRNA, and pathway activity dataset (paradigm IPLs) from The Cancer Genome Atlas (TCGA). Our analysis pinpoints a candidate gene-marker, EREG for AML with an integrative omics evidence. EREG is targeted by two top ranked microRNAs, hsa-miR-1286 and hsa-miR-1976 in the datasets.

In this article, we propose an unsupervised feature selection algorithm based on the recently developed shrinking and expansion algorithm (SEA). The SEA is already an established, efficient algorithm to identify dense subgraphs within a weighted graph. To conduct SEA on a weighted graph, the dataset is first mapped onto an equivalent graph notation where each vertex corresponds to a feature, and the weight of each edge represents the normalized mutual information between the associated two features that form the edge. As a result, the feature selection problem is presented as the best way to obtain several dense sub-feature spaces by using the SEA on a weighted feature graph. The proposed feature selection algorithm consists of a two-stage structure, in which the first stage is utilized to find a number of dense feature subgraphs, whereas the second stage is used to identify a representative feature from each of these feature subgraphs. The main advantage of the proposed algorithm is that the users are not required to provide the number of features to be selected, which is a major flaw in most of the existing algorithms. The current findings from the researchers' method are better than the other state-of-the-art algorithms because the feature selection process identifies approximately one-fifth to one-third of the number of original features while providing a high accuracy score. The superiority of the proposed algorithm over the other conventional methods of feature selection is established for a number of real-life datasets. In addition, the performance of the proposed feature selection algorithm is also found to be better than other algorithms in one of the innovative application areas of computational intelligence such as ambient intelligence.