AIMC Topic: Models, Biological

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Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.

PloS one
Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Fores...

Eigenvalue-based entropy in directed complex networks.

PloS one
Entropy is an important index for describing the structure, function, and evolution of network. The existing research on entropy is primarily applied to undirected networks. Compared with an undirected network, a directed network involves a special a...

Machine Learning to Support the Presentation of Complex Pathway Graphs.

IEEE/ACM transactions on computational biology and bioinformatics
Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult and time consuming process. Node duplication i...

Predictive analysis of the number of human brucellosis cases in Xinjiang, China.

Scientific reports
Brucellosis is one of the major public health problems in China, and human brucellosis represents a serious public health concern in Xinjiang and requires a prediction analysis to help making early planning and putting forward science preventive and ...

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease.

International journal of medical sciences
Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity...

The evaluation of COVID-19 prediction precision with a Lyapunov-like exponent.

PloS one
In the field of machine learning, building models and measuring their performance are two equally important tasks. Currently, measures of precision of regression models' predictions are usually based on the notion of mean error, where by error we mea...

An artificial neural network-pharmacokinetic model and its interpretation using Shapley additive explanations.

CPT: pharmacometrics & systems pharmacology
We developed a method to apply artificial neural networks (ANNs) for predicting time-series pharmacokinetics (PKs), and an interpretable the ANN-PK model, which can explain the evidence of prediction by applying Shapley additive explanations (SHAP). ...

Structure-based protein function prediction using graph convolutional networks.

Nature communications
The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting pro...

Fin sweep angle does not determine flapping propulsive performance.

Journal of the Royal Society, Interface
The importance of the leading-edge sweep angle of propulsive surfaces used by unsteady swimming and flying animals has been an issue of debate for many years, spurring studies in biology, engineering, and robotics with mixed conclusions. In this work...

Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis.

Biomolecules
Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine learning tools are increasingly used to integrate clinical, histopathological, and genomic data in lymp...