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Cells

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Deep learning large-scale drug discovery and repurposing.

Nature computational science
Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochon...

Cell-type-directed design of synthetic enhancers.

Nature
Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. It has been a long-standing goal in the field to decode the regulatory logic of an enhan...

A Compound Loss Function With Shape Aware Weight Map for Microscopy Cell Segmentation.

IEEE transactions on medical imaging
Microscopy cell segmentation is a crucial step in biological image analysis and a challenging task. In recent years, deep learning has been widely used to tackle this task, with promising results. A critical aspect of training complex neural networks...

Cell type discovery and representation in the era of high-content single cell phenotyping.

BMC bioinformatics
BACKGROUND: A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs a...

Will microfluidics enable functionally integrated biohybrid robots?

Proceedings of the National Academy of Sciences of the United States of America
The next robotics frontier will be led by biohybrids. Capable biohybrid robots require microfluidics to sustain, improve, and scale the architectural complexity of their core ingredient: biological tissues. Advances in microfluidics have already revo...

Automated human cell classification in sparse datasets using few-shot learning.

Scientific reports
Classifying and analyzing human cells is a lengthy procedure, often involving a trained professional. In an attempt to expedite this process, an active area of research involves automating cell classification through use of deep learning-based techni...

Cell Recognition Using BP Neural Network Edge Computing.

Contrast media & molecular imaging
This exploration is to solve the efficiency and accuracy of cell recognition in biological experiments. Neural network technology is applied to the research of cell image recognition. The cell image recognition problem is solved by constructing an im...

CyAnno: a semi-automated approach for cell type annotation of mass cytometry datasets.

Bioinformatics (Oxford, England)
MOTIVATION: For immune system monitoring in large-scale studies at the single-cell resolution using CyTOF, (semi-)automated computational methods are applied for annotating live cells of mixed cell types. Here, we show that the live cell pool can be ...

Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images.

The American journal of pathology
With applications in object detection, image feature extraction, image classification, and image segmentation, artificial intelligence is facilitating high-throughput analysis of image data in a variety of biomedical imaging disciplines, ranging from...

Hierarchical progressive learning of cell identities in single-cell data.

Nature communications
Supervised methods are increasingly used to identify cell populations in single-cell data. Yet, current methods are limited in their ability to learn from multiple datasets simultaneously, are hampered by the annotation of datasets at different resol...