AIMC Topic: Biological Science Disciplines

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ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family prediction.

BMC bioinformatics
BACKGROUND: Although research on non-coding RNAs (ncRNAs) is a hot topic in life sciences, the functions of numerous ncRNAs remain unclear. In recent years, researchers have found that ncRNAs of the same family have similar functions, therefore, it i...

Transformer-based deep learning for predicting protein properties in the life sciences.

eLife
Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap b...

BASIN: A Semi-automatic Workflow, with Machine Learning Segmentation, for Objective Statistical Analysis of Biomedical and Biofilm Image Datasets.

Journal of molecular biology
Micrograph comparison remains useful in bioscience. This technology provides researchers with a quick snapshot of experimental conditions. But sometimes a two- condition comparison relies on researchers' eyes to draw conclusions. Our Bioimage Analysi...

DIY liquid handling robots for integrated STEM education and life science research.

PloS one
Automation has played a key role in improving the safety, accuracy, and efficiency of manufacturing and industrial processes and has the potential to greatly increase throughput in the life sciences. However, the lack of accessible entry-point automa...

Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research.

Journal of biomedical semantics
BACKGROUND: In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and met...

Open-source personal pipetting robots with live-cell incubation and microscopy compatibility.

Nature communications
Liquid handling robots have the potential to automate many procedures in life sciences. However, they are not in widespread use in academic settings, where funding, space and maintenance specialists are usually limiting. In addition, current robots r...

Flexible IoT Gas Sensor Node for Automated Life Science Environments Using Stationary and Mobile Robots.

Sensors (Basel, Switzerland)
In recent years the degree of automation in life science laboratories increased considerably by introducing stationary and mobile robots. This trend requires intensified considerations of the occupational safety for cooperating humans, since the robo...

DeepImageJ: A user-friendly environment to run deep learning models in ImageJ.

Nature methods
DeepImageJ is a user-friendly solution that enables the generic use of pre-trained deep learning models for biomedical image analysis in ImageJ. The deepImageJ environment gives access to the largest bioimage repository of pre-trained deep learning m...

Artificial intelligence and the future of life sciences.

Drug discovery today
Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials;...

AIDeveloper: Deep Learning Image Classification in Life Science and Beyond.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Artificial intelligence (AI)-based image analysis has increased drastically in recent years. However, all applications use individual solutions, highly specialized for a particular task. Here, an easy-to-use, adaptable, and open source software, call...