AIMC Topic: Normal Distribution

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Active learning and Gaussian processes for the development of dissolution models: An AI-based data-efficient approach.

Journal of controlled release : official journal of the Controlled Release Society Jan 13, 2025
In vitro dissolution testing plays a key role in controlling the quality and optimizing the formulation of solid dosage pharmaceutical products. Data-driven dissolution models can improve the efficiency of testing: their predictions can act as surrog...
Artificial Intelligence Solubility Drug Liberation Normal Distribution Machine Learning Pharmaceutical Preparations
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Uncertainty modeling for inductive knowledge graph embedding.

Neural networks : the official journal of the International Neural Network Society Jan 6, 2025
In the process of refining Knowledge Graphs (KGs), new entities emerge, and old entities evolve, which usually updates their attribute information and neighborhood structures. This results in a distribution shift problem for entity features in the em...
Neural Networks, Computer Uncertainty Machine Learning Algorithms Humans Normal Distribution
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Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings.

Neural networks : the official journal of the International Neural Network Society Dec 4, 2024
This article proposes a novel deep clustering model based on the variational autoencoder (VAE), named GamMM-VAE, which can learn latent representations of training data for clustering in an unsupervised manner. Most existing VAE-based deep clustering...
Humans Neural Networks, Computer Cluster Analysis Bayes Theorem Normal Distribution Algorithms Deep Learning Autoencoder
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Mining soil heavy metal inversion based on Levy Flight Cauchy Gaussian perturbation sparrow search algorithm support vector regression (LSSA-SVR).

Ecotoxicology and environmental safety Nov 8, 2024
Soil heavy metal pollution in mining areas poses severe challenges to the ecological environment. In recent years, machine learning has been widely used in heavy metal inversion by hyperspectral data. However, deterministic algorithms and probabilist...
Mining Normal Distribution Soil Environmental Monitoring Soil Pollutants Metals, Heavy Support Vector Machine Algorithms
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Negative-Free Self-Supervised Gaussian Embedding of Graphs.

Neural networks : the official journal of the International Neural Network Society Oct 31, 2024
Graph Contrastive Learning (GCL) has recently emerged as a promising graph self-supervised learning framework for learning discriminative node representations without labels. The widely adopted objective function of GCL benefits from two key properti...
Humans Supervised Machine Learning Neural Networks, Computer Normal Distribution Algorithms
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Beyond strong labels: Weakly-supervised learning based on Gaussian pseudo labels for the segmentation of ellipse-like vascular structures in non-contrast CTs.

Medical image analysis Oct 30, 2024
Deep learning-based automated segmentation of vascular structures in preoperative CT angiography (CTA) images contributes to computer-assisted diagnosis and interventions. While CTA is the common standard, non-contrast CT imaging has the advantage of...
Normal Distribution Aorta, Abdominal Computed Tomography Angiography Humans Algorithms Radiographic Image Interpretation, Computer-Assisted Supervised Machine Learning Deep Learning
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gGN: Representing the Gene Ontology as low-rank Gaussian distributions.

Computers in biology and medicine Oct 11, 2024
Computational representations of knowledge graphs are critical for several tasks in bioinformatics, including large-scale graph analysis and gene function characterization. In this study, we introduce gGN, an unsupervised neural network for learning ...
Gene Ontology Normal Distribution Neural Networks, Computer Computational Biology Humans
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Towards a configurable and non-hierarchical search space for NAS.

Neural networks : the official journal of the International Neural Network Society Sep 3, 2024
Neural Architecture Search (NAS) outperforms handcrafted Neural Network (NN) design. However, current NAS methods generally use hard-coded search spaces, and predefined hierarchical architectures. As a consequence, adapting them to a new problem can ...
Neural Networks, Computer Algorithms Normal Distribution Bayes Theorem Humans
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Conformations of KRAS4B Affected by Its Partner Binding and G12C Mutation: Insights from GaMD Trajectory-Image Transformation-Based Deep Learning.

Journal of chemical information and modeling Aug 28, 2024
Binding of partners and mutations highly affects the conformational dynamics of KRAS4B, which is of significance for deeply understanding its function. Gaussian accelerated molecular dynamics (GaMD) simulations followed by deep learning (DL) and prin...
Proto-Oncogene Proteins p21(ras) Protein Conformation Normal Distribution Humans Principal Component Analysis Mutation Deep Learning Molecular Dynamics Simulation Protein Binding
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Unveiling Conformational States of CDK6 Caused by Binding of Vcyclin Protein and Inhibitor by Combining Gaussian Accelerated Molecular Dynamics and Deep Learning.

Molecules (Basel, Switzerland) Jun 5, 2024
CDK6 plays a key role in the regulation of the cell cycle and is considered a crucial target for cancer therapy. In this work, conformational transitions of CDK6 were identified by using Gaussian accelerated molecular dynamics (GaMD), deep learning (...
Cyclin-Dependent Kinase 6 Thermodynamics Normal Distribution Protein Kinase Inhibitors Protein Binding Humans Molecular Dynamics Simulation Binding Sites Principal Component Analysis Protein Conformation Deep Learning
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