Current Bioinformatics - Volume 19, Issue 8, 2024

Background: This study addresses the need for analyzing viral genome sequences and understanding their genetic relationships. The focus is on introducing a novel natural graph approach as a solution. Objective: The objective of this study is to demonstrate the effectiveness and advantages of the proposed natural graph approach in clustering viral genome sequences into distinct clades, subtypes, or districts. Additionally, the Read More

Background: Numerous studies have shown that mitochondrial damage induces inflammation and activates inflammatory cells, leading to sepsis, while sepsis, a systemic inflammatory response syndrome, also exacerbates mitochondrial damage and hyperactivation. Mitochondrial autophagy eliminates aged, abnormal or damaged mitochondria to reduce intracellular mitochondrial stress and the release of mitochondria-assoc Read More

Background: With new variants of COVID-19 causing challenges, we need to focus on integrating multiple deep-learning frameworks to develop intelligent healthcare systems for early detection and diagnosis. Objective: This article suggests three hybrid deep learning models, namely CNN-LSTM, CNN-Bi- LSTM, and CNN-GRU, to address the pressing need for an intelligent healthcare system. These models are designed to ca Read More

Background: With increasing rates of polypharmacy, the vigilant surveillance of clinical drug toxicity has emerged as an important concern. Named Entity Recognition (NER) stands as an indispensable undertaking, essential for the extraction of valuable insights regarding drug safety from the biomedical literature. In recent years, significant advancements have been achieved in the deep learning models on NER tasks. Nonethe Read More

Background: Time-course single-cell RNA sequencing (scRNA-seq) data represent dynamic gene expression values that change over time, which can be used to infer causal relationships between genes and construct dynamic gene regulatory networks (GRNs). However, most of the existing methods are designed for bulk RNA sequencing (bulk RNA-seq) data and static scRNA-seq data, and only a few methods, such as CNNC Read More

Introduction: Transcriptional gene expressions and their corresponding spatial information are critical for understanding the biological function, mutual regulation, and identification of various cell types. Materials and Methods: Recently, several computational methods have been proposed for clustering using spatial transcriptional expression. Although these algorithms have certain practicability, they cannot utilize spatial infor Read More

Background: Comparing directed networks using the alignment-free technique offers the advantage of detecting topologically similar regions that are independent of the network size or node identity. Objective: We propose a novel method to compare directed networks by decomposing the network into small modules, the so-called network subgraph approach, which is distinct from the network motif approach because it doe Read More