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Cell Physiological Phenomena

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Using deep learning to model the hierarchical structure and function of a cell.

Nature methods
Although artificial neural networks are powerful classifiers, their internal structures are hard to interpret. In the life sciences, extensive knowledge of cell biology provides an opportunity to design visible neural networks (VNNs) that couple the ...

Computational Analysis of Cell Dynamics in Videos with Hierarchical-Pooled Deep-Convolutional Features.

Journal of computational biology : a journal of computational molecular cell biology
Computational analysis of cellular appearance and its dynamics is used to investigate physiological properties of cells in biomedical research. In consideration of the great success of deep learning in video analysis, we first introduce two-stream co...

A portable structural analysis library for reaction networks.

Bio Systems
The topology of a reaction network can have a significant influence on the network's dynamical properties. Such influences can include constraints on network flows and concentration changes or more insidiously result in the emergence of feedback loop...

Evaluation of Stein-O'Brien et al.: To See a World in a Grain of Sand-How to Reveal a Common Latent Space through Multiple Platform Omics Data.

Cell systems
One snapshot of the peer review process for "Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species" (Stein-O'Brien et al., 2019).

Accurate and fast mitotic detection using an anchor-free method based on full-scale connection with recurrent deep layer aggregation in 4D microscopy images.

BMC bioinformatics
BACKGROUND: To effectively detect and investigate various cell-related diseases, it is essential to understand cell behaviour. The ability to detection mitotic cells is a fundamental step in diagnosing cell-related diseases. Convolutional neural netw...

Moonlighting protein prediction using physico-chemical and evolutional properties via machine learning methods.

BMC bioinformatics
BACKGROUND: Moonlighting proteins (MPs) are a subclass of multifunctional proteins in which more than one independent or usually distinct function occurs in a single polypeptide chain. Identification of unknown cellular processes, understanding novel...

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 Preliminary Study on Retro-reconstruction of Cell Fission Dynamic Process using Convolutional LSTM Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Cell morphological analysis has great impact towards our understanding of cell biology. It is however technically challenging to acquire the complete process of cell cycles under microscope inspection. Using convolutional long short-term memory (Conv...

MPI-VGAE: protein-metabolite enzymatic reaction link learning by variational graph autoencoders.

Briefings in bioinformatics
Enzymatic reactions are crucial to explore the mechanistic function of metabolites and proteins in cellular processes and to understand the etiology of diseases. The increasing number of interconnected metabolic reactions allows the development of in...