Current Bioinformatics - Volume 19, Issue 5, 2024

Background: RNA editing enriches post-transcriptional sequence changes. Currently detecting RNA editing sites is mostly based on the Sanger sequencing platform and second-generation sequencing. However, detection with Sanger sequencing is limited by the disturbing background peaks using the direct sequencing method and the clone number using the clone sequencing method, while second- generation sequencing de Read More

Background: The high mutability of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) makes it easy for mutations to occur during transmission. As the epidemic continues to develop, several mutated strains have been produced. Researchers worldwide are working on the effective identification of SARS-CoV-2. Objective: In this paper, we propose a new deep learning method that can effectively identify SAR Read More

Background: Preeclampsia (PE) is a severe pregnancy complication associated with autophagy. Objective: This research sought to uncover autophagy-related genes in pre-eclampsia through bioinformatics and machine learning. Methods: GSE75010 from the GEO series was subjected to WGCNA to identify key modular genes in PE. Autophagy genes retrieved from the THANATOS overlapped with the modular genes to yiel Read More

Introduction: Protein ubiquitylation is an important post-translational modification (PTM), which is considered to be one of the most important processes regulating cell function and various diseases. Therefore, accurate prediction of ubiquitylation proteins and their PTM sites is of great significance for the study of basic biological processes and the development of related drugs. Researchers have developed some large-s Read More

Background: As we all know, finding new pharmaceuticals requires a lot of time and money, which has compelled people to think about adopting more effective approaches to locate drugs. Researchers have made significant progress recently when it comes to using Deep Learning (DL) to create DTI. Methods: Therefore, we propose a deep learning model that applies Transformer to DTI prediction. The model uses a Transf Read More

Background: Even after surgery, breast cancer patients still suffer from recurrence and metastasis. Thus, it is critical to predict accurately the risk of recurrence and metastasis for individual patients, which can help determine the appropriate adjuvant therapy. Methods: The purpose of this study is to investigate and compare the performance of several categories of molecular biomarkers, i.e., microRNA (miRNA), long non-c Read More

Introduction: Differentially methylated regions (DMRs), including tissue-specific DMRs and disease-specific DMRs, can be used in revealing the mechanisms of gene regulation and screening diseases. Up until now, many methods have been proposed to detect DMRs from bisulfite sequencing data. In these methods, differentially methylated CpG sites and DMRs are usually identified based on statistical tests or distri Read More

Background: Antibacterial resistance has been one of the most important causes of death in the last few decades, necessitating the need to discover new antibiotics. Antimicrobial peptides (AMPs) are among the best candidates due to their broad-spectrum and potent activity against bacteria and low probability of developing resistance against them. Objective: In this study, we proposed a novel filtration method using Read More