Current Bioinformatics - Current Issue

Inflammatory Bowel Disease (IBD), encompassing ulcerative colitis and Crohn’s disease, affects millions worldwide. Characterized by a complex interplay of genetic, microbial, and environmental factors, IBD challenges conventional treatment approaches, necessitating precision medicine. This paper reviews the role of bioinformatics in leveraging transcriptional data for novel IBD diagnostics and therapeutics. It highlights th Read More

BackgroundCancer has emerged as the “leading killer” of human health. Survival prediction is a crucial branch of cancer prognosis. It aims to estimate patients' survival risk based on their disease conditions. Accurate and efficient survival prediction is vital in cancer patients' treatment and clinical management, preventing unnecessary suffering and conserving precious medical resources. Deep learning has been extensively Read More

Enhancers are the short functional regions (50–1500bp) in the genome, which play an effective character in activating gene-transcription in the presence of transcription-factors. Many human diseases, such as cancer and inflammatory bowel disease, are correlated with the enhancers’ genetic variations. The precise recognition of the enhancers provides useful insights for understanding the pathogenesis of human disea Read More

IntroductionCancer driver genes are genes responsible for cancer genesis; thus, identifying cancer-related genes is crucial in fostering cancer treatment. The accuracy in identifying cancer driver genes within the vast pool of normal passenger genes directly influences the efficacy of treatment approaches.ObjectiveThis research aimed to effectively identify cancer driver genes using the List-based Simulated Annealing Read More

BackgroundAccurately predicting survival in hospitalized COVID-19 patients is crucial but challenging due to multiple risk factors. This study addresses the limitations of existing research by proposing a comprehensive machine-learning framework to identify key mortality risk factors and develop a robust predictive model.ObjectiveThis study proposes an analytical framework that leverages various machine learning techni Read More