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Identifying critical states prior to critical transitions in complex biological processes is essential for disease forecasting and early interventional therapy. Due to the complexity of the underlying mechanisms, the currently proposed methods based on ...

Volume electron microscopy (vEM) is becoming a prominent technique in three-dimensional (3D) cellular visualization. vEM collects a series of two-dimensional (2D) images and reconstructs ultrastructures at the nanometer scale by rational axial ...

Integrating imaging and genetic data provides a comprehensive approach to analyze brain disorders from different perspectives, which has important implications for the early diagnosis of Alzheimer’s Disease (AD) and the exploration of its ...

The advancements in Cryo-Electron Tomography (cryo-ET) have made it possible to visualize molecules in their natural cellular settings in three-dimensional space. Such visualizations play a crucial role in investigating the functions of biological ...$\mathrm{}$$\mathrm{}$$\mathrm{}$$\mathrm{}$

As an autoimmune-mediated inflammatory demyelinating disease of the central nervous system, multiple sclerosis (MS) is often confused with cerebral small vessel disease (cSVD), which is a regional pathological change in brain tissue with unknown ...

• The study pioneers an interpretable deep learning models for distinguishing between MS and cSVD based on T2-weighted FLAIR images.
• Employing pre-trained CNNs with attention modules, the model not only enhances feature learning ...

In multi-view environment, it would yield missing observations due to the limitation of the observation process. The most current representation learning methods struggle to explore complete information by lacking either cross-generative via simply ...

Cryo-Electron Microscopy (cryo-EM) is a revolutionary technique for determining the structures of proteins and macromolecules. Physical limitations of the imaging conditions cause a very low Signal-to-Noise Ratio (SNR) in cryo-EM micrographs, ...

Three-dimensional (3D) reconstruction in cryo-electron tomography (cryo-ET) plays an important role in studying in situ biological macromolecular structures at the nanometer level. Owing to limited tilt angle, 3D reconstruction of cryo-ET always ...

Whole Slide Image (WSI) classification is an important part of pathological diagnosis. Although previous approaches (such as DSMIL and CLAM) have achieved good results, the classification performance is still unsatisfactory because the learned ...

Microsatellite instability (MSI) is a crucial biomarker to clinical immunotherapy in gastrointestinal cancer, while additional immunohistochemical or genetic tests for MSI are generally missing due to lack of medical resources. Deep learning has ...

Survival analysis is a significant study in cancer prognosis, and the multi-modal data, including histopathological images, genomic data, and clinical information, provides unprecedented opportunities for its development. However, because of the high ...

Predicting drug-disease associations (DDAs) is a significant part of drug discovery. With the continuous accumulation of biomedical data, multidimensional metrics about drugs and diseases are obtained, therefore how to effectively integrate them ...

At present, with the continuous improvement of residents' quality of life, the increasing number of private cars and the expansion of urban residents, the traffic pressure is increasing. How to choose the best route of urban traffic, shorten the travel ...

Background and objective: Cryo-electron tomography (cryo-ET) with subtomogram averaging (STA) is indispensable when studying macromolecule structures and functions in their native environments. Due to the low signal-to-noise ratio, the missing ...

Cryo-electron microscopy single particle analysis requires tens of thousands of particle projections for the structural determination of macromolecules. To free researchers from laborious particle picking work, a number of fully automatic and semi-...

With the increasing cost of traditional drug discovery, drug repositioning methods at low cost have attracting increasing attention. The generation of large amounts of biomedical data also provides unprecedented opportunities for drug ...

Building upon the clinical evidence supporting that decomposing a pathological image into different components can improve diagnostic value, in this paper we propose a Decomposition-and-Fusion Network (DFNet) for HE-stained pathological image ...