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HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling
Applying deep learning concepts from image detection and graph theory has greatly advanced protein-ligand binding affinity prediction, a challenge with enormous ramifications for both drug discovery and protein engineering. We build upon these advanc...

MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction.

Journal of chemical information and modeling
The applications of artificial intelligence, machine learning, and deep learning techniques in the field of materials science are becoming increasingly common due to their promising abilities to extract and utilize data-driven information from availa...

iQDeep: an integrated web server for protein scoring using multiscale deep learning models.

Journal of molecular biology
The remarkable recent advances in protein structure prediction have enabled computational modeling of protein structures with considerably higher accuracy than ever before. While state-of-the-art structure prediction methods provide self-assessment c...

AiZynthTrain: Robust, Reproducible, and Extensible Pipelines for Training Synthesis Prediction Models.

Journal of chemical information and modeling
We introduce the AiZynthTrain Python package for training synthesis models in a robust, reproducible, and extensible way. It contains two pipelines that create a template-based one-step retrosynthesis model and a RingBreaker model that can be straigh...

Cue: a deep-learning framework for structural variant discovery and genotyping.

Nature methods
Structural variants (SVs) are a major driver of genetic diversity and disease in the human genome and their discovery is imperative to advances in precision medicine. Existing SV callers rely on hand-engineered features and heuristics to model SVs, w...

MIDRC CRP10 AI interface-an integrated tool for exploring, testing and visualization of AI models.

Physics in medicine and biology
. Developing Machine Learning models (N Gorre et al 2023) for clinical applications from scratch can be a cumbersome task requiring varying levels of expertise. Seasoned developers and researchers may also often face incompatible frameworks and data ...

Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics.

Journal of digital imaging
The purpose of this study is to test the feasibility for deep CNN-based artificial intelligence methods for automatic classification of the mass margin and shape, while radiomic feature-based machine learning methods were also implemented in this stu...

Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review.

Biosensors & bioelectronics
Artificial intelligence (AI) has received great attention since the concept was proposed, and it has developed rapidly in recent years with applications in many fields. Meanwhile, newer iterations of smartphone hardware technologies which have excell...

Support Vector Machine-Based Global Classification Model of the Toxicity of Organic Compounds to .

Molecules (Basel, Switzerland)
is widely used as the model species in toxicity and risk assessment. For the first time, a global classification model was proposed in this paper for a two-class problem (Class - 1 with log1/IBC ≤ 4.2 and Class + 1 with log1/IBC > 4.2, the unit of I...