AI Medical Compendium Topic:
Models, Biological

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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...

Phenotyping Women Based on Dietary Macronutrients, Physical Activity, and Body Weight Using Machine Learning Tools.

Nutrients
Nutritional phenotyping can help achieve personalized nutrition, and machine learning tools may offer novel means to achieve phenotyping. The primary aim of this study was to use energy balance components, namely input (dietary energy intake and macr...

A machine-learning approach to calibrate generic Raman models for real-time monitoring of cell culture processes.

Biotechnology and bioengineering
The manufacture of biotherapeutic proteins consists of complex upstream unit operations requiring multiple raw materials, analytical techniques, and control strategies to produce safe and consistent products for patients. Raman spectroscopy is a ubiq...

Artificial neural networks accurately predict intra-abdominal infection in moderately severe and severe acute pancreatitis.

Journal of digestive diseases
OBJECTIVE: The aim of this study was to evaluate the efficacy of artificial neural networks (ANN) in predicting intra-abdominal infection in moderately severe (MASP) and severe acute pancreatitis (SAP) compared with that of a logistic regression mode...

Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients.

Computational and mathematical methods in medicine
The diagnostic performance of an artificial neural network model for chronic HBV-induced liver fibrosis reverse is not well established. Our research aims to construct an ANN model for estimating noninvasive predictors of fibrosis reverse in chronic ...

Assessment of lipid peroxidation and artificial neural network models in early Alzheimer Disease diagnosis.

Clinical biochemistry
OBJECTIVE: Lipid peroxidation constitutes a molecular mechanism involved in early Alzheimer Disease (AD) stages, and artificial neural network (ANN) analysis is a promising non-linear regression model, characterized by its high flexibility and utilit...