AIMC Topic: Models, Biological

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Semantic similarity and machine learning with ontologies.

Briefings in bioinformatics
Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background kno...

Toward a living soft microrobot through optogenetic locomotion control of .

Science robotics
Learning from the locomotion of natural organisms is one of the most effective strategies for designing microrobots. However, the development of bioinspired microrobots is still challenging because of technical bottlenecks such as design and seamless...

Microbes and complex diseases: from experimental results to computational models.

Briefings in bioinformatics
Studies have shown that the number of microbes in humans is almost 10 times that of cells. These microbes have been proven to play an important role in a variety of physiological processes, such as enhancing immunity, improving the digestion of gastr...

Dr AFC: drug repositioning through anti-fibrosis characteristic.

Briefings in bioinformatics
Fibrosis is a key component in the pathogenic mechanism of a variety of diseases. These diseases involving fibrosis may share common mechanisms and therapeutic targets, and therefore common intervention strategies and medicines may be applicable for ...

A novel deep learning method for predictive modeling of microbiome data.

Briefings in bioinformatics
With the development and decreasing cost of next-generation sequencing technologies, the study of the human microbiome has become a rapid expanding research field, which provides an unprecedented opportunity in various clinical applications such as d...

Real-Time Analysis of the Dynamic Foot Function: A Machine Learning and Finite Element Approach.

Journal of biomechanical engineering
Finite element analysis (FEA) has been widely used to study foot biomechanics and pathological functions or effects of therapeutic solutions. However, development and analysis of such foot modeling is complex and time-consuming. The purpose of this s...

A machine-learning approach to map landscape connectivity in with genetic and environmental data.

Proceedings of the National Academy of Sciences of the United States of America
Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interact...

COVID-19 mortality rate prediction for India using statistical neural networks and gaussian process regression model.

African health sciences
The primary purpose of this research is to identify the best COVID-19 mortality model for India using regression models and is to estimate the future COVID-19 mortality rate for India. Specifically, Statistical Neural Networks (Radial Basis Function ...

Collective dynamics in entangled worm and robot blobs.

Proceedings of the National Academy of Sciences of the United States of America
Living systems at all scales aggregate in large numbers for a variety of functions including mating, predation, and survival. The majority of such systems consist of unconnected individuals that collectively flock, school, or swarm. However, some agg...