Predicting circRNA–Disease Associations with Shared Units and Multi-Channel Attention Mechanisms

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Bioengineering,
circRNA   disease association   multi-view learning   attention mechanism   contrastive learning   computational model   bioinformatics  

Background Introduction In recent years, circular RNAs (circRNAs), as a novel class of non-coding RNA molecules, have played a significant role in the occurrence, development, and treatment of diseases. Due to their unique circular structure, circRNAs are resistant to degradation by nucleases, making them potential biomarkers and therapeutic target...

ACImpute: A Constraint-Enhancing Smooth-Based Approach for Imputing Single-Cell RNA Sequencing Data

Information Science, Computer Science, Life Sciences, Bioengineering,
Single-cell RNA sequencing   Data imputation   Constraint-enhancing smooth   Cluster analysis   Trajectory inference  

Single-cell RNA sequencing (scRNA-seq) technology has been widely applied in biological and medical research in recent years. It can reveal the transcriptomic information of individual cells, helping scientists better understand cellular heterogeneity and complexity. However, a common issue in scRNA-seq data is “dropout events,” which result in man...

APNet: An Explainable Sparse Deep Learning Model to Discover Differentially Active Drivers of Severe COVID-19

Information Science, Artificial Intelligence, Medicine, Infectious Diseases, Life Sciences, Immunology,
COVID-19   deep learning   bioinformatics   proteomics   network biology  

Academic Background The COVID-19 pandemic has had a significant impact on global public health systems. Although the pandemic has somewhat subsided, its complex immunopathological mechanisms, long-term sequelae (such as “long COVID”), and the potential for similar threats in the future continue to drive in-depth research. Severe COVID-19 cases are ...

SP-DTI: Subpocket-Informed Transformer for Drug–Target Interaction Prediction

Chemistry, Medicinal Chemistry, Information Science, Computer Science, Artificial Intelligence, Life Sciences, Biochemistry, Bioengineering,
Drug-Target Interaction   Deep Learning   Subpocket Analysis   Transformer Model   Drug Discovery  

Academic Background Drug-Target Interaction (DTI) prediction is a critical step in drug discovery, significantly reducing the cost and time of experimental screening. However, despite the advancements in deep learning that have improved the accuracy of DTI prediction, existing methods still face two major challenges: lack of generalizability and ne...

ABVS Breast Tumour Segmentation via Integrating CNN with Dilated Sampling Self-Attention and Feature Interaction Transformer

Information Science, Artificial Intelligence, Medicine, Medical Imaging,
Breast Tumour Segmentation   Automated Breast Volume Scanner   3D Transformer-CNN Segmentation Network   Mask Image Modelling   Deep Learning   Medical Image Analysis   Self-Supervised Learning  

ABVS Breast Tumor Segmentation Research Based on CNN and Dilated Sampling Self-Attention Academic Background Breast cancer is the second most common cancer worldwide, and early and accurate detection is crucial for improving patient prognosis and reducing mortality. Although various imaging techniques (such as X-ray mammography, magnetic resonance ...

A General Debiasing Framework with Counterfactual Reasoning for Multimodal Public Speaking Anxiety Detection

Information Science, Computer Science, Artificial Intelligence,
public speaking anxiety detection   multimodal debiasing   spurious correlation   counterfactual reasoning   dataset  

Academic Background and Problem Introduction In the field of education today, Public Speaking Anxiety (PSA) is a widespread phenomenon, especially among non-native language learners. This anxiety not only affects learners’ ability to express themselves but may also hinder their personal development. To help learners overcome this issue, researchers...