AIMC Topic: Neural Networks, Computer

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Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistant from whole genome sequencing and gene expression.

Antimicrobial agents and chemotherapy
Whole genome sequencing (WGS) potentially represents a rapid approach for antimicrobial resistance genotype-to-phenotype prediction. However, the challenge still exists to predict fully minimum inhibitory concentrations (MICs) and antimicrobial susce...

Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. ...

The Use of Wearable Sensors and Machine Learning Methods to Estimate Biomechanical Characteristics During Standing Posture or Locomotion: A Systematic Review.

Sensors (Basel, Switzerland)
Balance deficits are present in a variety of clinical populations and can negatively impact quality of life. The integration of wearable sensors and machine learning technology (ML) provides unique opportunities to quantify biomechanical characterist...

Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches.

International journal of molecular sciences
The complexities inherent in drug development are multi-faceted and often hamper accuracy, speed and efficiency, thereby limiting success. This review explores how recent developments in machine learning (ML) are significantly impacting target-based ...

Visualization Methods for DNA Sequences: A Review and Prospects.

Biomolecules
The efficient analysis and interpretation of biological sequence data remain major challenges in bioinformatics. Graphical representation, as an emerging and effective visualization technique, offers a more intuitive method for analyzing DNA sequence...

deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction.

Biomolecules
Coarse-grained models have provided researchers with greatly improved computational efficiency in modeling structures and dynamics of biomacromolecules, but, to be practically useful, they need fast and accurate conversion methods back to the all-ato...

Analysis of impact of limb segment length variations during reinforcement learning in four-legged robot.

Scientific reports
Crawling robots are becoming increasingly prevalent in both industrial and private applications. Despite their many advantages over other robot types, they have complex movement mechanics. Artificial intelligence can simplify this by reinforcement le...

Artificial intelligence neural network and fuzzy modelling of unsteady Sisko trihybrid nanofluids for cancer therapy with entropy insights.

Scientific reports
The main objective of the current endeavor is to monitor hypothetical processes utilizing a Sisko tri-hybrid fluid over a rotating disk with entropy generation suspended in Darcy-Forchheimer porous medium. Electro Magneto Hydro Dynamics (EMHD), non-l...

An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks.

eLife
Timely and effective use of antimicrobial drugs can improve patient outcomes, as well as help safeguard against resistance development. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely...

Understanding and modeling human-AI interaction of artificial intelligence tool in radiation oncology clinic using deep neural network: a feasibility study using three year prospective data.

Physics in medicine and biology
Artificial intelligence (AI) based treatment planning tools are being implemented in clinic. However, human interactions with such AI tools are rarely analyzed. This study aims to comprehend human planner's interaction with the AI planning tool and i...