PredIL13: Stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides.

Interleukin (IL)-13 has emerged as one of the recently identified cytokine. Since IL-13 causes the severity of COVID-19 and alters crucial biological processes, it is urgent to explore novel molecules or peptides capable of including IL-13. Computational prediction has received attention as a complementary method to in-vivo and in-vitro experimental identification of IL-13 inducing peptides, because experimental identification is time-consuming, laborious, and expensive.

Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2'-O-methylation sites in human RNA.

2'-O-methylation (2-OM or Nm) is a widespread RNA modification observed in various RNA types like tRNA, mRNA, rRNA, miRNA, piRNA, and snRNA, which plays a crucial role in several biological functional mechanisms and innate immunity. To comprehend its modification mechanisms and potential epigenetic regulation, it is necessary to accurately identify 2-OM sites. However, biological experiments can be tedious, time-consuming, and expensive.

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models.

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer's disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases.

Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach.

Dihydrouridine (DHU, D) is one of the most abundant post-transcriptional uridine modifications found in tRNA, mRNA, and snoRNA, closely associated with disease pathogenesis and various biological processes in eukaryotes. Identifying D sites is important for understanding the modification mechanisms and/or epigenetic regulation. However, biological experiments for detecting D sites are time-consuming and expensive.

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer.

Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations.

Rapid isothermal point-of-care test for screening of SARS-CoV-2 (COVID-19).

Rapid on-site diagnosis of emerging pathogens is key for early identification of infected individuals and for prevention of further spreading in a population. Currently available molecular diagnostic tests are instrument-based whereas rapid antibody and antigen tests are often not sufficiently sensitive for detection in pre-symptomatic subjects. There is a need for rapid point of care molecular screening tests that can be easily adapted to emerging pathogens and are selective, sensitive, reliable in different settings around the world.

Engineered biosensors for the quorum sensing molecule 3,5-dimethyl-pyrazine-2-ol (DPO) reveal its presence in humans, animals, and bacterial species beyond Vibrio cholerae.

A biosensor was engineered to enable the study of the novel quorum sensing molecule (QSM), 3,5-dimethylpyrazin-2-ol (DPO), employed by Vibrio cholerae to regulate biofilm formation and virulence factor production. Investigations into bacterial quorum sensing (QS), a form of communication based on the production and detection of QSMs to coordinate gene expression in a population dependent manner, offer a unique window to study the molecular underpinnings of microbial behavior and host interactions. Herein, we report the construction of an engineered microbial whole-cell bioluminescent biosensing system that incorporates the recognition of the VqmA regulatory protein of Vibrio cholerae with the bioluminescent reporting signal of luciferase for the selective, sensitive, stable, and reproducible detection of DPO in a variety of samples.

Identification of Drug Targets and Agents Associated with Hepatocellular Carcinoma through Integrated Bioinformatics Analysis.

Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death globally. The mechanisms underlying the development of HCC are mostly unknown till now.

Objective: The main goal of this study was to identify potential drug target proteins and agents for the treatment of HCC.

A comprehensive meta-analysis comprising 149 case-control studies to investigate the association between IL-6 gene rs1800795 polymorphism and multiple disease risk.

Background: Individual genome-wide association studies (GWAS) or single case-specific meta-analyses may not be sufficient evidence to take action against a specific gene function. Thus, we tried to determine a consensus association between the IL-6 gene rs1800795 polymorphism and multiple disease risks through an updated statistical meta-analysis.

Method: After systematically searching online databases, we found 149 case-control relevant datasets with a sample size of 96,153 (cases: 38,291 and controls: 57862) and conducted the meta-analysis using updated statistical models.

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies.

Cervical cancer (CC) is considered as the fourth most common women cancer globally.that shows malignant features of local infiltration and invasion into adjacent organs and tissues. There are several individual studies in the literature that explored CC-causing hub-genes (HubGs), however, we observed that their results are not so consistent.

Association of hypoxia inducible factor 1-Alpha gene polymorphisms with multiple disease risks: A comprehensive meta-analysis.

HIF1A gene polymorphisms have been confirmed the association with cancer risk through the statistical meta-analysis based on single genetic association (SGA) studies. A good number SGA studies also investigated the association of HIF1A gene with several other diseases, but no researcher yet performed statistical meta-analysis to confirm this association more accurately. Therefore, in this paper, we performed a statistical meta-analysis to draw a consensus decision about the association of HIF1A gene polymorphisms with several diseases except cancers giving the weight on large sample size.

Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer.

Bioinformatics analysis has been playing a vital role in identifying potential genomic biomarkers more accurately from an enormous number of candidates by reducing time and cost compared to the wet-lab-based experimental procedures for disease diagnosis, prognosis, and therapies. Cervical cancer (CC) is one of the most malignant diseases seen in women worldwide. This study aimed at identifying potential key genes (KGs), highlighting their functions, signaling pathways, and candidate drugs for CC diagnosis and targeting therapies.

Statistical meta-analysis to investigate the association between the Interleukin-6 (IL-6) gene polymorphisms and cancer risk.

A good number of genome-wide association studies (GWAS), including meta-analyses, reported that single nucleotide polymorphisms (SNPs) of the IL-6 gene are significantly associated with various types of cancer risks, though some other studies reported insignificant association with cancers, in the literature. These contradictory results may be due to variations in sample sizes and/or deficiency of statistical modeling. Therefore, an attempt is made to provide a more comprehensive understanding of the association between the IL-6 gene SNPs (rs1800795, rs1800796, rs1800797) and different cancer risks, giving the weight on a large sample size, including different cancer types and appropriate statistical modeling with the meta-dataset.