Identification of Biomarkers and Molecular Pathways Implicated in Smoking and COVID-19 Associated Lung Cancer Using Bioinformatics and Machine Learning Approaches.

Lung cancer (LC) is a significant global health issue, with smoking as the most common cause. Recent epidemiological studies have suggested that individuals who smoke are more susceptible to COVID-19. In this study, we aimed to investigate the influence of smoking and COVID-19 on LC using bioinformatics and machine learning approaches.

A comprehensive cotton leaf disease dataset for enhanced detection and classification.

The creation and use of a comprehensive cotton leaf disease dataset offer significant benefits in agricultural research, precision farming, and disease management. This dataset enables the development of accurate machine learning models for early disease detection, reducing manual inspections and facilitating timely interventions. It serves as a benchmark for testing algorithms and training deep learning models, aiding in automated monitoring and decision support tools in precision agriculture.

Machine learning to reveal an astute risk predictive framework for Gynecologic Cancer and its impact on women psychology: Bangladeshi perspective.

Background: In this research, an astute system has been developed by using machine learning and data mining approach to predict the risk level of cervical and ovarian cancer in association to stress.

Results: For functioning factors and subfactors, several machine learning models like Logistics Regression, Random Forest, AdaBoost, Naïve Bayes, Neural Network, kNN, CN2 rule Inducer, Decision Tree, Quadratic Classifier were compared with standard metrics e.g.

Identification of molecular biomarkers and pathways of NSCLC: insights from a systems biomedicine perspective.

Background: Worldwide, more than 80% of identified lung cancer cases are associated to the non-small cell lung cancer (NSCLC). We used microarray gene expression dataset GSE10245 to identify key biomarkers and associated pathways in NSCLC.

Results: To collect Differentially Expressed Genes (DEGs) from the dataset GSE10245, we applied the R statistical language.

Dataset on significant risk factors for Type 1 Diabetes: A Bangladeshi perspective.

In this article, dataset and detailed data analysis results of Type-1 Diabetes has been given. Now-a-days Type-1 Diabetes is an appalling disease in Bangladesh. Total 306 person data (Case group- 152 and Control Group- 154) has been collected from Dhaka based on a specific questioner.

Hybrid photonic crystal fiber in chemical sensing.

Background: In this article, a hybrid photonic crystal fiber has been proposed for chemical sensing. A FEM has been applied for numerical investigation of some propagation characteristics of the PCF at a wider wavelength from 0.7 to 1.

Association Assessment among Risk Factors and Breast Cancer in a Low Income Country: Bangladesh.

Background: In the low incoming country Bangladesh, breast cancer is second most common neoplasm and is increasing at an alarming rate among females. Lack of awareness and illiteracy are contributory factors for late presentation and therefore mortality.

Purpose: To examine associations of different factors with breast cancer mortality and to raise awareness among the women of society in Bangladesh.