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Cells

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Image-based phenotyping of disaggregated cells using deep learning.

Communications biology
The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undes...

Learning to encode cellular responses to systematic perturbations with deep generative models.

NPJ systems biology and applications
Cellular signaling systems play a vital role in maintaining homeostasis when a cell is exposed to different perturbations. Components of the systems are organized as hierarchical networks, and perturbing different components often leads to transcript...

Identification of the human DPR core promoter element using machine learning.

Nature
The RNA polymerase II (Pol II) core promoter is the strategic site of convergence of the signals that lead to the initiation of DNA transcription, but the downstream core promoter in humans has been difficult to understand. Here we analyse the human ...

Machine learning uncovers cell identity regulator by histone code.

Nature communications
Conversion between cell types, e.g., by induced expression of master transcription factors, holds great promise for cellular therapy. Our ability to manipulate cell identity is constrained by incomplete information on cell identity genes (CIGs) and t...

Automated Classification of Apoptosis in Phase Contrast Microscopy Using Capsule Network.

IEEE transactions on medical imaging
Automatic and accurate classification of apoptosis, or programmed cell death, will facilitate cell biology research. The state-of-the-art approaches in apoptosis classification use deep convolutional neural networks (CNNs). However, these networks ar...

Large-Scale Multi-Class Image-Based Cell Classification With Deep Learning.

IEEE journal of biomedical and health informatics
Recent advances in ultra-high-throughput microscopy have enabled a new generation of cell classification methodologies using image-based cell phenotypes alone. In contrast to current single-cell analysis techniques that rely solely on slow and costly...

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).

Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks.

Scientific reports
Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second appr...

DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy.

Bioinformatics (Oxford, England)
MOTIVATION: Super-resolution fluorescence microscopy with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advanc...

Cell ontology in an age of data-driven cell classification.

BMC bioinformatics
BACKGROUND: Data-driven cell classification is becoming common and is now being implemented on a massive scale by projects such as the Human Cell Atlas. The scale of these efforts poses a challenge. How can the results be made searchable and accessib...