23 papers · ranked by Valyu relevance
Yael Amitay, Yuval Bussi, Ben Feinstein, Shai Bagon + 2 more
Multiplexed imaging enables measurement of multiple proteins in situ, offering an unprecedented opportunity to chart various cell types and states in tissues. However, cell classification, the task of identifying the type of individual cells, remains challenging, labor-intensive, and limiting to throughput. Here, we…
Benjamin Misselwitz, Gerhard Strittmatter, Balamurugan Periaswamy, Markus C Schlumberger + 4 more
'Markus C Schlumberger' 'Samuel Rout' 'Peter Horvath' 'Karol Kozak' 'Wolf-Dietrich Hardt'] Background Light microscopy is of central importance in cell biology. The recent introduction of automated high content screening has expanded this technology towards automation of experiments and performing large scale…
Vy Nguyen, Johannes Griss
Automatic cell type identification in scRNA-seq datasets is an essential method to alleviate a key bottleneck in scRNA-seq data analysis. While most existing tools show good sensitivity and specificity in classifying cell types, they often fail to adequately not-classify cells that are not present in the used…
Rikard Forlin, Pouria Tajvar, Nana Wang, Dimos Dimarogonas + 1 more
Grouping individual cells in clusters and annotating these based on feature expression is a common procedure in single-cell analysis pipelines. Multiple methods have been reported for single-cell mRNA sequencing and cytometry datasets where the vast majority rely on sequential 2-step procedures involving I) cell…
Matthew N. Bernstein, Zhongjie Ma, Michael Gleicher, Colin N. Dewey
Cell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification by considering the rich hierarchical…
Sarah Rudigkeit, Julian B. Reindl, Nicole Matejka, Rika Ramson + 3 more
'Matthias Sammer' 'Günther Dollinger' 'Judith Reindl'] The fundamental basis in the development of novel radiotherapy methods is in-vitro cellular studies. To assess different endpoints of cellular reactions to irradiation like proliferation, cell cycle arrest, and cell death, several assays are used in radiobiological…
Yusuke Ozaki, Hidenao Yamada, Hirotoshi Kikuchi, Amane Hirotsu + 13 more
It is demonstrated that cells can be classified by pattern recognition of the subcellular structure of non-stained live cells, and the pattern recognition was performed by machine learning. Human white blood cells and five types of cancer cell lines were imaged by quantitative phase microscopy, which provides…
Andriy Didovyk, Oleg Kanakov, Mikhail Ivanchenko, Jeff Hasty + 2 more
'Ramón Huerta' 'Lev S. Tsimring'] from a population of weak or simple classifiers. We create a master population of cells with randomized synthetic biosensor circuits that have a broad range of sensitivities towards chemical signals of interest that form the input vectors subject to classification. The randomized…
Noga Nissim, Matan Dudaie, Itay Barnea, Natan T. Shaked
We present a method for a real-time visualization and automatic processing for detection and classification of untouched cancer cells in blood during stain‐free imaging flow cytometry using digital holographic microscopy and machine learning in throughput of 15 cells per second. As a preliminary model for circulating…
Weiruo Zhang, Irene Li, Nathan E. Reticker-Flynn, Zinaida Good + 13 more
Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We propose a novel machine…
Dhananjay Bhaskar, Darrick Lee, Hildur Knútsdóttir, Cindy Tan + 4 more
Cell morphology is an important indicator of cell state, function, stage of development, and fate in both normal and pathological conditions. Cell shape is among key indicators used by pathologists to identify abnormalities or malignancies. With rapid advancements in the speed and amount of biological data acquisition…
Mohammad Shifat‐E‐Rabbi, Xuwang Yin, Cailey E. Fitzgerald, Gustavo K. Rohde
'Gustavo K. Rohde'] Cell image classification methods are currently being used in numerous applications in cell biology and medicine. Applications include understanding the effects of genes and drugs in screening experiments, understanding the role and subcellular localization of different proteins, as well as…
Shann-Ching Chen, Robert F Murphy
Background Knowledge of the subcellular location of a protein is critical to understanding how that protein works in a cell. This location is frequently determined by the interpretation of fluorescence microscope images. In recent years, automated systems have been developed for consistent and objective interpretation…
Youssef Marrakchi, Davide D'Ascenzo, Sebastiano Cultrera di Montesano
Most classification problems assume the classes are roughly separable, so that an individual sample can usually be assigned to one class. Single-cell perturbation data violates this assumption: two perturbations can produce different populations of cells while overlapping so much that an individual cell could belong to…
Merlin Veronika, Roy Welsch, Alvin Ng, Paul Matsudaira + 1 more
'Jagath C Rajapakse'] Background Essential events of cell development and homeostasis are revealed by the associated changes of cell morphology and therefore have been widely used as a key indicator of physiological states and molecular pathways affecting various cellular functions via cytoskeleton. Cell motility is a…
David M. J. Tax, Veronika Cheplygina, Marco Loog
In the diagnosis of autoimmune diseases, an important task is to classify images of slides containing several HEp-2 cells. All cells from one slide share the same label, and by classifying cells from one slide independently, some information on the global image quality and intensity is lost. Considering one whole slide…
Quan Wang, Yan Ou, A. Agung Julius, Kim L. Boyer + 1 more
Matching cells over time has long been the most difficult step in cell tracking. In this paper, we approach this problem by recasting it as a classification problem. We construct a feature set for each cell, and compute a feature difference vector between a cell in the current frame and a cell in a previous frame. Then…
Dennis Pischel, Jörn H. Buchbinder, Kai Sundmacher, Inna N. Lavrik + 2 more
'Robert J. Flassig' 'Arun Rishi'] Imaging flow cytometry is a powerful experimental technique combining the strength of microscopy and flow cytometry to enable high-throughput characterization of cell populations on a detailed microscopic scale. This approach has an increasing importance for distinguishing between…
Felix Schönenberger, Anja Deutzmann, Elisa Ferrando-May, Dorit Merhof
'Dorit Merhof'] Background Protein function in eukaryotic cells is often controlled in a cell cycle-dependent manner. Therefore, the correct assignment of cellular phenotypes to cell cycle phases is a crucial task in cell biology research. Nuclear proteins whose localization varies during the cell cycle are valuable…
Authors not listed
This paper addresses the challenges in cell line development (CLD), the lengthy and ambiguous clone screening in upstream biopharmaceutical production. Typically, only a small subset of the later stages of CLD data is used for manually selecting lead clones. Addressing this issue, we introduce a multivariate data…
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While metallodrugs have been known for decades, developing new effective formulations still remains a challenge, underscoring the need for new tools to assist identification of potent metal-containing anticancer agents. In this work, we developed a straightforward data-driven approach to predict cytotoxicity of metal…
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Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…
Juerg Straubhaar, Alexandria D’Souza, Zachary Niziolek, Bogdan Budnik
Single-cell analysis has clearly established itself in biology and biomedical fields as an invaluable tool that allows one to comprehensively understand the relationship between cells, including their types, states, transitions, trajectories, and spatial position. Scientific methods such as fluorescence labeling…