AIMC Topic: Cell Line, Tumor

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Antigen-independent single-cell circulating tumor cell detection using deep-learning-assisted biolasers.

Biosensors & bioelectronics Nov 22, 2024
Circulating tumor cells (CTCs) in the bloodstream are important biomarkers for clinical prognosis of cancers. Current CTC identification methods are based on immuno-labeling, which depends on the differential expression of specific antigens between t...
Neoplastic Cells, Circulating Lasers Lung Neoplasms Biosensing Techniques Pancreatic Neoplasms Humans Deep Learning Cell Line, Tumor Single-Cell Analysis
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Human essential gene identification based on feature fusion and feature screening.

IET systems biology Nov 22, 2024
Essential genes are necessary to sustain the life of a species under adequate nutritional conditions. These genes have attracted significant attention for their potential as drug targets, especially in developing broad-spectrum antibacterial drugs. H...
Humans ROC Curve Machine Learning Genes, Essential Computational Biology Algorithms Cell Line, Tumor
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ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning.

Nature communications Nov 22, 2024
Despite the significant potential of generative models, low synthesizability of many generated molecules limits their real-world applications. In response to this issue, we develop ClickGen, a deep learning model that utilizes modular reactions like ...
Poly(ADP-ribose) Polymerase Inhibitors Click Chemistry Cell Line, Tumor Cell Proliferation Poly (ADP-Ribose) Polymerase-1 Molecular Docking Simulation Antineoplastic Agents Deep Learning Humans
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Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach.

eLife Nov 22, 2024
Understanding the cell cycle at the single-cell level is crucial for cellular biology and cancer research. While current methods using fluorescent markers have improved the study of adherent cells, non-adherent cells remain challenging. In this study...
Humans Machine Learning Leukemia, Myeloid, Acute Cell Line, Tumor Cell Cycle Image Processing, Computer-Assisted Cell Adhesion Workflow Automation
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MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells.

Analytical chemistry Nov 21, 2024
Single-cell mass spectrometry (SCMS) is an emerging tool for studying cell heterogeneity according to variation of molecular species in single cells. Although it has become increasingly common to employ machine learning models in SCMS data analysis, ...
Machine Learning Phenotype Melanoma Humans Mass Spectrometry Cell Line, Tumor Single-Cell Analysis
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Resolution-dependent MRI-to-CT translation for orthotopic breast cancer models using deep learning.

Physics in medicine and biology Nov 21, 2024
This study aims to investigate the feasibility of utilizing generative adversarial networks (GANs) to synthesize high-fidelity computed tomography (CT) images from lower-resolution MR images. The goal is to reduce patient exposure to ionizing radiati...
Breast Neoplasms Animals Deep Learning Humans Tomography, X-Ray Computed Female Magnetic Resonance Imaging Mice, Nude Mice, Inbred BALB C Cell Line, Tumor Image Processing, Computer-Assisted Mice
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Exploring the cytotoxic and antioxidant properties of lanthanide-doped ZnO nanoparticles: a study with machine learning interpretation.

Journal of nanobiotechnology Nov 10, 2024
BACKGROUND: Lanthanide-based nanomaterials offer a promising alternative for cancer therapy because of their selectivity and effectiveness, which can be modified and predicted by leveraging the improved accuracy and enhanced decision-making of machin...
Caco-2 Cells Metal Nanoparticles Zinc Oxide Lanthanoid Series Elements Ytterbium Erbium Antioxidants Hep G2 Cells Cell Survival Machine Learning Cell Line, Tumor Nanoparticles Humans
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DeSide: A unified deep learning approach for cellular deconvolution of tumor microenvironment.

Proceedings of the National Academy of Sciences of the United States of America Nov 8, 2024
Cellular deconvolution via bulk RNA sequencing (RNA-seq) presents a cost-effective and efficient alternative to experimental methods such as flow cytometry and single-cell RNA-seq (scRNA-seq) for analyzing the complex cellular composition of tumor mi...
Humans Transcriptome Single-Cell Analysis Deep Learning RNA-Seq Gene Expression Regulation, Neoplastic Cell Line, Tumor Sequence Analysis, RNA Tumor Microenvironment Neoplasms Gene Expression Profiling
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Deep learning-based automatic image classification of oral cancer cells acquiring chemoresistance in vitro.

PloS one Nov 1, 2024
Cell shape reflects the spatial configuration resulting from the equilibrium of cellular and environmental signals and is considered a highly relevant indicator of its function and biological properties. For cancer cells, various physiological and en...
Drug Resistance, Neoplasm Humans Cell Line, Tumor Cell Shape Neural Networks, Computer Image Processing, Computer-Assisted Deep Learning Mouth Neoplasms
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Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC.

BioFactors (Oxford, England) Oct 31, 2024
Most patients with non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the disease, which complicates treatment due to a heightened risk of metastasis. Consequently, the timely identification of biomarkers associated with lymph n...
Male Middle Aged Cell Line, Tumor Prognosis Female Biomarkers, Tumor Neoplastic Stem Cells Carcinoma, Non-Small-Cell Lung Lymphatic Metastasis GTPase-Activating Proteins Lung Neoplasms Humans Gene Expression Regulation, Neoplastic Machine Learning Cell Proliferation
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