We present a novel computational framework that combines Agent-Based Modeling (ABM) with Reinforcement Learning (RL) using the Double Deep Q-Network (DDQN) algorithm to determine cellular behavior in response to environmental signals. With this appro...
OBJECTIVE: This research aims to detect genes associated with the extracellular matrix (ECM) in idiopathic pulmonary fibrosis (IPF) using bioinformatics techniques and investigate their relationships with immune infiltration, with the goal of identif...
This study aimed to identify potential interacting genes between abdominal aortic aneurysm (AAA) and periodontitis. To achieve this, we obtained datasets of AAA and periodontitis from the GEO database, conducted differential analysis on the AAA datas...
Biochemical and biophysical research communications
Aug 24, 2025
Accurate prediction of long non-coding RNA (LncRNA) subcellular localization is crucial for understanding its biological functions. In this study, we proposed a novel deep learning framework, LncMamba, which utilizes a two-layer FPN network for multi...
Acute respiratory distress syndrome (ARDS) is one of the most common and serious complications in the development of sepsis. Endoplasmic reticulum stress (ERS) plays an important role in the pathophysiologic process of sepsis-associated ARDS. The aim...
BACKGROUND: Rapid advancements in high-throughput sequencing technologies allow for detailed and accurate measurement of omics features within their biological context. The integration of different omics types creates heterogeneous datasets, presenti...
RNA velocities and generalizations emerge as powerful approaches for extracting time-resolved information from high-throughput snapshot single-cell data. Yet, several inherent limitations restrict applying the approaches to genes not suitable for RNA...
BACKGROUND: Single-cell RNA sequencing analysis faces critical challenges including high dimensionality, sparsity, and complex topological relationships between cells. Current methods struggle to simultaneously preserve global structure, model cellul...
Proceedings of the National Academy of Sciences of the United States of America
Aug 20, 2025
The rapid expansion of protein sequence and structure databases has resulted in a significant number of proteins with ambiguous or unknown function. While advances in machine learning techniques hold great potential to fill this annotation gap, curre...
Proceedings of the National Academy of Sciences of the United States of America
Aug 20, 2025
Large-scale scientific datasets today contain tens of thousands of random variables across millions of samples (for example, the RNA expression levels of 20,000 protein-coding genes across 30 million single cells). Being able to quantify dependencies...
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