AIMC Topic: Machine Learning

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Learning spatio-temporal patterns with Neural Cellular Automata.

PLoS computational biology
Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and Partial Differential Equation (PDE) trajectories. Our method is designed to...

Study on design optimization of GFRP tubular column composite structure based on machine learning method.

PloS one
Circular reinforced concrete wound glass fiber reinforced polymer (GFRP) columns and reinforced concrete filled GFRP columns are extensively utilized in civil engineering practice. Various factors influence the performance of these two types of GFRP ...

Development of new materials for electrothermal metals using data driven and machine learning.

PloS one
After adopting a combined approach of data-driven methods and machine learning, the prediction of material performance and the optimization of composition design can significantly reduce the development time of materials at a lower cost. In this rese...

Applications and challenges in designing VHH-based bispecific antibodies: leveraging machine learning solutions.

mAbs
The development of bispecific antibodies that bind at least two different targets relies on bringing together multiple binding domains with different binding properties and biophysical characteristics to produce a drug-like therapeutic. These buildin...

Automated facial landmark measurement using machine learning: A feasibility study.

The Journal of prosthetic dentistry
STATEMENT OF PROBLEM: Information regarding facial landmark measurement using machine learning (ML) techniques in prosthodontics is lacking.

Integrated machine learning-based virtual screening and biological evaluation for identification of potential inhibitors against cathepsin K.

Molecular diversity
Cathepsin K is a type of cysteine proteinase that is primarily expressed in osteoclasts and has a key role in the breakdown of bone matrix protein during bone resorption. Many studies suggest that the deficiency of cathepsin K is concomitant with a s...

Artificial Intelligence-Derived Risk Prediction: A Novel Risk Calculator Using Office and Ambulatory Blood Pressure.

Hypertension (Dallas, Tex. : 1979)
BACKGROUND: Quantification of total cardiovascular risk is essential for individualizing hypertension treatment. This study aimed to develop and validate a novel, machine-learning-derived model to predict cardiovascular mortality risk using office bl...

Should the Use of Adaptive Machine Learning Systems in Medicine be Classified as Research?

The American journal of bioethics : AJOB
A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use o...

Artificial Intelligence for Quantifying Cumulative Small Bowel Disease Severity on CT-Enterography in Crohn's Disease.

The American journal of gastroenterology
INTRODUCTION: Assessing the cumulative degree of bowel injury in ileal Crohn's disease (CD) is difficult. We aimed to develop machine learning (ML) methodologies for automated estimation of cumulative ileal injury on computed tomography-enterography ...

Salience Interest Option: Temporal abstraction with salience interest functions.

Neural networks : the official journal of the International Neural Network Society
Reinforcement Learning (RL) is a significant machine learning subfield that emphasizes learning actions based on environment to obtain optimal behavior policy. RL agents can make decisions at variable time scales in the form of temporal abstractions,...