AIMC Topic: Algorithms

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Dynamics-Based Peptide-MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO.

Journal of chemical theory and computation
An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen-major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we appl...

Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain.

Scientific reports
Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data inform...

Visual prototypes in the ventral stream are attuned to complexity and gaze behavior.

Nature communications
Early theories of efficient coding suggested the visual system could compress the world by learning to represent features where information was concentrated, such as contours. This view was validated by the discovery that neurons in posterior visual ...

DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation.

Nature communications
Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining he...

Risk Factors of Restroke in Patients with Lacunar Cerebral Infarction Using Magnetic Resonance Imaging Image Features under Deep Learning Algorithm.

Contrast media & molecular imaging
This study was aimed to explore the magnetic resonance imaging (MRI) image features based on the fuzzy local information C-means clustering (FLICM) image segmentation method to analyze the risk factors of restroke in patients with lacunar infarction....

Genetic CFL: Hyperparameter Optimization in Clustered Federated Learning.

Computational intelligence and neuroscience
Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of i...

Protein-Protein Docking: Past, Present, and Future.

The protein journal
The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliabl...

Low-dose CT reconstruction with Noise2Noise network and testing-time fine-tuning.

Medical physics
PURPOSE: Deep learning-based image denoising and reconstruction methods demonstrated promising performance on low-dose CT imaging in recent years. However, most existing deep learning-based low-dose CT reconstruction methods require normal-dose image...

Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of .

Sensors (Basel, Switzerland)
The problems that the key biomass variables in fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by p...

Multiple Cylinder Extraction from Organized Point Clouds.

Sensors (Basel, Switzerland)
Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the differe...