AI Medical Compendium Topic:
Models, Biological

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The hepatotoxic potential of protein kinase inhibitors predicted with Random Forest and Artificial Neural Networks.

Toxicology letters
Protein kinases (PKs) play a role in many pivotal aspects of cellular function. Dysregulation and mutations of protein kinases are involved in the development of different diseases, which might be treated by inhibition of the corresponding kinase. Pr...

Application of Machine Learning Techniques for Clinical Predictive Modeling: A Cross-Sectional Study on Nonalcoholic Fatty Liver Disease in China.

BioMed research international
BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases. Machine learning techniques were introduced to evaluate the optimal predictive clinical model of NAFLD.

Variable importance for sustaining macrophyte presence via random forests: data imputation and model settings.

Scientific reports
Data sets plagued with missing data and performance-affecting model parameters represent recurrent issues within the field of data mining. Via random forests, the influence of data reduction, outlier and correlated variable removal and missing data i...

A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites.

International journal of molecular sciences
Protein hydroxylation is one type of post-translational modifications (PTMs) playing critical roles in human diseases. It is known that protein sequence contains many uncharacterized residues of proline and lysine. The question that needs to be answe...

Modeling the Human Visuo-Motor System to Support Remote-Control Operation.

Sensors (Basel, Switzerland)
The working hypothesis in this project is that gaze interactions play a central role in structuring the joint control and guidance strategy of the human operator performing spatial tasks. Perceptual guidance and control is the idea that the visual an...

Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC.

Journal of theoretical biology
The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this study, we propose a novel model called MACC-PSSM by integrating Moran autocorrelat...

Extracting Biomedical Events with Parallel Multi-Pooling Convolutional Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Biomedical event extraction is important for medical research and disease prevention, which has attracted much attention in recent years. Traditionally, most of the state-of-the-art systems have been based on shallow machine learning methods, which r...

Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology.

Computers in biology and medicine
Hospital readmission is one of the critical metrics used for measuring the performance of hospitals. The HITECH Act imposes penalties when patients are readmitted to hospitals if they are diagnosed with one of the six conditions mentioned in the Act....

Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease.

PloS one
Prognostic modelling is important in clinical practice and epidemiology for patient management and research. Electronic health records (EHR) provide large quantities of data for such models, but conventional epidemiological approaches require signifi...

Mapping the Potential Global Codling Moth (Cydia pomonella L.) Distribution Based on a Machine Learning Method.

Scientific reports
The spread of invasive species may pose great threats to the economy and ecology of a region. The codling moth (Cydia pomonella L.) is one of the 100 worst invasive alien species in the world and is the most destructive apple pest. The economic losse...