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Biological Phenomena

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Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Colorectal cancer is one of the highest causes of cancer-related death, especially in men. Polyps are one of the main causes of colorectal cancer, and early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps ...

Machine learning for cluster analysis of localization microscopy data.

Nature communications
Quantifying the extent to which points are clustered in single-molecule localization microscopy data is vital to understanding the spatial relationships between molecules in the underlying sample. Many existing computational approaches are limited in...

DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning.

Nature methods
An outstanding challenge in single-molecule localization microscopy is the accurate and precise localization of individual point emitters in three dimensions in densely labeled samples. One established approach for three-dimensional single-molecule l...

Regression plane concept for analysing continuous cellular processes with machine learning.

Nature communications
Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool e...

A Bioinspired Stress-Response Strategy for High-Speed Soft Grippers.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The stress-response strategy is one of the nature's greatest developments, enabling animals and plants to respond quickly to environmental stimuli. One example is the stress-response strategy of the Venus flytrap, which enables such a delicate plant ...

Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures.

NPJ systems biology and applications
The utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of appl...

Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase.

Database : the journal of biological databases and curation
ABSTRACT: Reactome is a database of human biological pathways manually curated from the primary literature and peer-reviewed by experts. To evaluate the utility of Reactome pathways for predicting functional consequences of genetic perturbations, we ...

Real-Time Prediction of Growth Characteristics for Individual Fruits Using Deep Learning.

Sensors (Basel, Switzerland)
Understanding the growth status of fruits can enable precise growth management and improve the product quality. Previous studies have rarely used deep learning to observe changes over time, and manual annotation is required to detect hidden regions o...

Constrained disorder principle-based variability is fundamental for biological processes: Beyond biological relativity and physiological regulatory networks.

Progress in biophysics and molecular biology
The constrained disorder principle (CDP) defines systems based on their degree of disorder bounded by dynamic boundaries. The principle explains stochasticity in living and non-living systems. Denis Noble described the importance of stochasticity in ...

Enhanced Deep-Learning-Based Automatic Left-Femur Segmentation Scheme with Attribute Augmentation.

Sensors (Basel, Switzerland)
This research proposes augmenting cropped computed tomography (CT) slices with data attributes to enhance the performance of a deep-learning-based automatic left-femur segmentation scheme. The data attribute is the lying position for the left-femur m...