AIMC Topic: Models, Biological

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A controller for walking derived from how humans recover from perturbations.

Journal of the Royal Society, Interface
Humans can walk without falling despite some external perturbations, but the control mechanisms by which this stability is achieved have not been fully characterized. While numerous walking simulations and robots have been constructed, no full-state ...

Personalized oncology with artificial intelligence: The case of temozolomide.

Artificial intelligence in medicine
PURPOSE: Using artificial intelligence techniques, we compute optimal personalized protocols for temozolomide administration in a population of patients with variability.

Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry.

Artificial intelligence in medicine
INTRODUCTION: Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learnin...

Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity.

Scientific reports
Human age estimation is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age estimation, each with its advantages and limitations. In this work, we investigate whether physical activi...

Increasing productivity of Spirulina platensis in photobioreactors using artificial neural network modeling.

Biotechnology and bioengineering
Although production of microalgae in open ponds is conventionally practiced due to its economy, exposure of the algae to uncontrollable elements impedes achievement of quality and it is desirable to develop closed reactor cultivation methods for the ...

Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions.

Scientific reports
A neural network model was previously developed to predict melatonin rhythms accurately from blue light and skin temperature recordings in individuals on a fixed sleep schedule. This study aimed to test the generalizability of the model to other slee...

Development and experiments of a bio-inspired robot with multi-mode in aerial and terrestrial locomotion.

Bioinspiration & biomimetics
This paper introduces a new multi-modal robot capable of terrestrial and aerial locomotion, aiming to operate in a wider range of environments. The robot was built to achieve two locomotion modes of walking and gliding while preventing one modality h...

Prodromal clinical, demographic, and socio-ecological correlates of asthma in adults: a 10-year statewide big data multi-domain analysis.

The Journal of asthma : official journal of the Association for the Care of Asthma
To identify prodromal correlates of asthma as compared to chronic obstructive pulmonary disease and allied-conditions (COPDAC) using a multi domain analysis of socio-ecological, clinical, and demographic domains. This is a retrospective case-risk-co...

Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.

Artificial intelligence in medicine
BACKGROUND: Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood glucose (BG) regulation that might result in short and long-term health complications and even death if not properly managed. Currently, there is no cure for diabet...

Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks.

International journal of molecular sciences
Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networ...