CellDeathPred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting

Article Biochemistry & Molecular Biology

Machine Learning Classifies Ferroptosis and Apoptosis Cell Death Modalities with TfR1 Immunostaining

Jenny Jin et al.

Summary: A machine learning approach combined with staining techniques was developed to unbiasedly classify different cell death mechanisms based on specific cell features, providing a novel analytic framework.

ACS CHEMICAL BIOLOGY (2022)

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Review Biochemistry & Molecular Biology

Ferroptosis turns 10: Emerging mechanisms, physiological functions, and therapeutic applications

Brent R. Stockwell

Summary: This review describes the key regulators of ferroptosis and provides an overview of metabolism, ROS biology, and iron biology. Additionally, it highlights the important concepts and unanswered questions in the field of ferroptosis. The future holds promise for further breakthroughs in understanding the mechanisms of ferroptosis and utilizing it for therapeutic purposes.

CELL (2022)

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Proceedings Paper Computer Science, Artificial Intelligence

S5CL: Unifying Fully-Supervised, Self-supervised, and Semi-supervised Learning Through Hierarchical Contrastive Learning

Manuel Tran et al.

Summary: In computational pathology, a unified framework called S5CL is introduced for fully-supervised, self-supervised, and semi-supervised learning. It combines two-stage training into one and uses three contrastive losses to learn feature representations. The evaluations show significant improvements in accuracy and F1-score on sparse label datasets.

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT II (2022)

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Article Computer Science, Artificial Intelligence

Trans-channel fluorescence learning improves high-content screening for Alzheimer's disease therapeutics

Daniel R. Wong et al.

Summary: This study develops a deep learning method for generating images in drug discovery, with broad applicability across diverse fluorescence microscopy datasets.

NATURE MACHINE INTELLIGENCE (2022)

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Article Cell Biology

Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis

Joost Verduijn et al.

Summary: The combination of digital holographic microscopy and deep learning algorithm enables accurate distinction of cell states in a label-free manner, with potential profound impact on research in cell death regulation, biomedicine, and cell biology in general.

CELL DEATH DISCOVERY (2021)

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Article Computer Science, Artificial Intelligence

DeepHCS plus plus : Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening

Gyuhyun Lee et al.

Summary: The proposed method in this paper aims to transform bright-field microscopy images into synthetic fluorescence images for observing cell apoptosis, nuclei, and cytoplasm, commonly used in drug screening. By leveraging deep learning and multi-task learning with adversarial losses, the proposed method generates accurate and realistic synthetic fluorescence images, reducing preparation time and improving screening efficiency.

MEDICAL IMAGE ANALYSIS (2021)

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Article Computer Science, Artificial Intelligence

Integration of millions of transcriptomes using batch-aware triplet neural networks

Lukas M. Simon et al.

Summary: The study introduced a novel deep learning algorithm called INSCT for efficient integration of heterogeneous single-cell RNA sequencing data, demonstrating high scalability and accuracy. INSCT can integrate cells across different experiments, platforms, and species, and is suitable for large-scale data integration in a desktop computer environment.

NATURE MACHINE INTELLIGENCE (2021)

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Editorial Material Biochemistry & Molecular Biology