Breast cancer is the second most prevalent malignancy affecting women. Postmenopausal women breast tumor is one of the top causes of death in women, accounting for 23% of cancer cases. Type 2 diabetes, a worldwide pandemic, has been connected to a heightened risk of several malignancies, although its association with breast cancer is still uncertain. In comparison to non-diabetic women, women with T2DM had a 23% elevated likelihood of developing breast cancer. It is difficult to determine causative or genetic susceptibility that connect T2DM and breast cancer. We created a large-scale network-based quantitative approach employing unbiased methods to discover abnormally amplified genes in both T2DM and breast cancer, to solve these issues. We performed transcriptome analysis to uncover identical genetic biomarkers and pathways to clarify the connection between T2DM and breast cancer patients. In this study, two RNA-seq datasets (GSE103001 and GSE86468) from the Gene Expression Omnibus (GEO) are used to identify mutually differentially expressed genes (DEGs) for breast cancer and T2DM, as well as common pathways and prospective medicines. Firstly, 45 shared genes (30 upregulated and 15 downregulated) between T2D and breast cancer were detected. We employed gene ontology and pathway enrichment to characterize prevalent DEGs' molecular processes and signal transduction pathways and observed that T2DM has certain connections to the progression of breast cancer. Using several computational and statistical approaches, we created a protein-protein interactions (PPI) network and revealed hub genes. These hub genes can be potential biomarkers, which may also lead to new therapeutic strategies for investigated diseases. We conducted TF-gene interactions, gene-microRNA interactions, protein-drug interactions, and gene-disease associations to find potential connections between T2DM and breast cancer pathologies. We assume that the potential drugs that emerged from this study could be useful therapeutic values. Researchers, doctors, biotechnologists, and many others may benefit from this research.

Expanding data suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. However, the underlying deterministic details are still unspecified. Bioinformatics approaches are designed to investigate the relationships between two pathogens as well as fill this study void. Glioblastoma is a form of cancer that typically occurs in the brain or spinal cord and is highly destructive. A stroke occurs when a brain region starts to lose blood circulation and prevents functioning. Moyamoya disorder is a recurrent and recurring arterial disorder of the brain. To begin, adequate gene expression datasets on glioblastoma, ischemic stroke, and moyamoya disease were gathered from various repositories. Then, the association between glioblastoma, ischemic stroke, and moyamoya was established using the existing pipelines. The framework was developed as a generalized workflow to allow for the aggregation of transcriptomic gene expression across specific tissue; Gene Ontology (GO) and biological pathway, as well as the validation of such data, are carried out using enrichment studies such as protein–protein interaction and gold benchmark databases. The results contribute to a more profound knowledge of the disease mechanisms and unveil the projected correlations among the diseases.

Type 2 diabetes (T2D) is a chronic metabolic disease defined by insulin insensitivity corresponding to impaired insulin sensitivity, decreased insulin production, and eventually failure of beta cells in the pancreas. There is a 30–40 percent higher risk of developing T2D in active smokers. Moreover, T2D patients with active smoking may gradually develop many complications. However, there is still no significant research conducted to solve the issue. Hence, we have proposed a highthroughput network-based quantitative pipeline employing statistical methods. Transcriptomic and GWAS data were analysed and obtained from type 2 diabetes patients and active smokers. Differentially Expressed Genes (DEGs) resulted by comparing T2D patients’ and smokers’ tissue samples to those of healthy controls of gene expression transcriptomic datasets. We have found 55 dysregulated genes shared in people with type 2 diabetes and those who smoked, 27 of which were upregulated and 28 of which were downregulated. These identified DEGs were functionally annotated to reveal the involvement of cell-associated molecular pathways and GO terms. Moreover, protein–protein interaction analysis was conducted to discover hub proteins in the pathways. We have also identified transcriptional and post-transcriptional regulators associated with T2D and smoking. Moreover, we have analysed GWAS data and found 57 common biomarker genes between T2D and smokers. Then, Transcriptomic and GWAS analyses are compared for more robust outcomes and identified 1 significant common gene, 19 shared significant pathways and 12 shared significant GOs. Finally, we have discovered protein–drug interactions for our identified biomarkers.

In the era of the twenty-first century, automation is the biggest fi eld of research. Although several works have been done successfully, there is still so much to do. Increasing trash could be the biggest threat to a better human life in the future. To automate the task, machines need to understand the type of trash to be recycled or separate similar trash for recycling. For this process, the perfect classi cation of trash plays a vital role in making a better life and a cleaner world. There are so many popular convolutional neural network (CNN) models for image classi cation. In this work, we examine and analyze the outcomes of several Residual Network(ResNet) and Visual Geometry Group (VGG) CNN models on a trash dataset. Our main investigation is the accuracy evaluation for different VGG and ResNet models where the training dataset, test dataset, number of epochs, and batch size are the same for all the models. Finally, we compare VGG and ResNet models with each other. We have got the peak accuracy for ResNet152 among all ResNet models and the peak accuracy on VGG16 among all VGG models. And we have got the maximum accuracy on ResNet152 among all the ResNet and VGG models which is about 94%. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 8, Dec 2021 P 17-23

The COVID-19 coronavirus, which primarily affects the lungs, is the source of the disease known as SARS-CoV-2. According to “Smoking and COVID-19: a scoping review,” about 32% of smokers had a severe case of COVID-19 pneumonia at their admission time and 15% of non-smokers had this case of COVID-19 pneumonia. We were able to determine which genes were expressed differently in each group by comparing the expression of gene transcriptomic datasets of COVID-19 patients, smokers, and healthy controls. In all, 37 dysregulated genes are common in COVID-19 patients and smokers, according to our analysis. We have applied all important methods namely protein-protein interaction, hub-protein interaction, drug-protein interaction, tf-gene interaction, and gene-MiRNA interaction of bioinformatics to analyze to understand deeply the connection between both smoking and COVID-19 severity. We have also analyzed Pathways and Gene Ontology where 5 significant signaling pathways were validated with previous literature. Also, we verified 7 hub-proteins, and finally, we validated a total of 7 drugs with the previous study.

The Differential Gene Expression (DGE) approach to find out the expressed genes relies on measures such as log-fold change and adjusted p-values. Although fold change is commonly employed in gene expression studies, especially in microarray and RNA sequencing experiments to quantify alterations in a gene’s expression level, a limitation and potential hazard of relying on fold change in this context is its inherent bias. As a consequence, it might incorrectly categorize genes that have significant differences but minor ratios, resulting in poor detection of mutations in genes with high expression levels. In contrast, machine learning offers a more comprehensive view, adept at capturing the non-linear complexities of gene expression data and providing robustness against noise that inspired us to utilize machine learning models to explore differential gene expression based on feature importance in Type 2 Diabetes (T2D), a significant global health concern, in this study. Moreover, we validated biomarkers based on our findings expressed genes with previous studies to ensure the effectiveness of our ML models in this work which led us to go through to analysis pathways, gene ontologies, protein-protein interactions, transcription factors, miRNAs, and drug predictions to deal with T2D. This study aims to consider the machine learning technique as a good way to know about expressed genes profoundly not relying on the DGE approach, and to control or reduce the risk of T2D patients by helping drug developer researchers.