AIMC Topic: Deep Learning

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TPepRet: a deep learning model for characterizing T-cell receptors-antigen binding patterns.

Bioinformatics (Oxford, England) Dec 26, 2024
MOTIVATION: T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing antigenic peptides, a process pivotal for cancer immunotherapy, vaccine design, and autoimmune disease management. Understanding the intricate binding ...
Computational Biology Receptors, Antigen, T-Cell Protein Binding Humans COVID-19 Peptides Deep Learning SARS-CoV-2
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DDGemb: predicting protein stability change upon single- and multi-point variations with embeddings and deep learning.

Bioinformatics (Oxford, England) Dec 26, 2024
MOTIVATION: The knowledge of protein stability upon residue variation is an important step for functional protein design and for understanding how protein variants can promote disease onset. Computational methods are important to complement experimen...
Deep Learning Computational Biology Databases, Protein Protein Stability Proteins Software
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BetaAlign: a deep learning approach for multiple sequence alignment.

Bioinformatics (Oxford, England) Dec 26, 2024
MOTIVATION: Multiple sequence alignments (MSAs) are extensively used in biology, from phylogenetic reconstruction to structure and function prediction. Here, we suggest an out-of-the-box approach for the inference of MSAs, which relies on algorithms ...
Phylogeny Deep Learning Software Algorithms Sequence Alignment Computational Biology Natural Language Processing
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Quantitative analysis of the dexamethasone side effect on human-derived young and aged skeletal muscle by myotube and nuclei segmentation using deep learning.

Bioinformatics (Oxford, England) Dec 26, 2024
MOTIVATION: Skeletal muscle cells (skMCs) combine together to create long, multi-nucleated structures called myotubes. By studying the size, length, and number of nuclei in these myotubes, we can gain a deeper understanding of skeletal muscle develop...
Humans Algorithms Cell Nucleus Muscle Fibers, Skeletal Muscle, Skeletal Deep Learning Dexamethasone Image Processing, Computer-Assisted
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Ligand identification in CryoEM and X-ray maps using deep learning.

Bioinformatics (Oxford, England) Dec 26, 2024
MOTIVATION: Accurately identifying ligands plays a crucial role in the process of structure-guided drug design. Based on density maps from X-ray diffraction or cryogenic-sample electron microscopy (cryoEM), scientists verify whether small-molecule li...
Cryoelectron Microscopy Deep Learning Models, Molecular Drug Design Ligands Crystallography, X-Ray
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TPepPro: a deep learning model for predicting peptide-protein interactions.

Bioinformatics (Oxford, England) Dec 26, 2024
MOTIVATION: Peptides and their derivatives hold potential as therapeutic agents. The rising interest in developing peptide drugs is evidenced by increasing approval rates by the FDA of USA. To identify the most potential peptides, study on peptide-pr...
Computational Biology Software Proteins Peptides Databases, Protein Deep Learning
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[Research progress on endoscopic image diagnosis of gastric tumors based on deep learning].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Dec 25, 2024
Gastric tumors are neoplastic lesions that occur in the stomach, posing a great threat to human health. Gastric cancer represents the malignant form of gastric tumors, and early detection and treatment are crucial for patient recovery. Endoscopic exa...
Stomach Neoplasms Humans Deep Learning Diagnosis, Computer-Assisted Gastroscopy Algorithms
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[Identification of kidney stone types by deep learning integrated with radiomics features].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Dec 25, 2024
Currently, the types of kidney stones before surgery are mainly identified by human beings, which directly leads to the problems of low classification accuracy and inconsistent diagnostic results due to the reliance on human knowledge. To address thi...
Deep Learning Imaging, Three-Dimensional Tomography, X-Ray Computed Radiomics Kidney Calculi Humans Neural Networks, Computer
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[Cardiac magnetic resonance image segmentation based on lightweight network and knowledge distillation strategy].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Dec 25, 2024
To address the issue of a large number of network parameters and substantial floating-point operations in deep learning networks applied to image segmentation for cardiac magnetic resonance imaging (MRI), this paper proposes a lightweight dilated par...
Heart Image Processing, Computer-Assisted Magnetic Resonance Imaging Neural Networks, Computer Humans Algorithms Deep Learning
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[Coronary artery segmentation based on Transformer and convolutional neural networks dual parallel branch encoder neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Dec 25, 2024
Manual segmentation of coronary arteries in computed tomography angiography (CTA) images is inefficient, and existing deep learning segmentation models often exhibit low accuracy on coronary artery images. Inspired by the Transformer architecture, th...
Neural Networks, Computer Coronary Vessels Convolutional Neural Networks Humans Computed Tomography Angiography Imaging, Three-Dimensional Image Processing, Computer-Assisted Algorithms Coronary Angiography Deep Learning
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