AIMC Topic: Cell Proliferation

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Deep-learning based classification distinguishes sarcomatoid malignant mesotheliomas from benign spindle cell mesothelial proliferations.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Jun 10, 2021
Sarcomatoid mesothelioma is an aggressive malignancy that can be challenging to distinguish from benign spindle cell mesothelial proliferations based on biopsy, and this distinction is crucial to patient treatment and prognosis. A novel deep learning...
Humans Diagnosis, Differential Neural Networks, Computer Prognosis Area Under Curve Sensitivity and Specificity Mesothelioma Deep Learning Mesothelioma, Malignant Pleural Neoplasms ROC Curve Image Processing, Computer-Assisted Cell Proliferation
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Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography.

Scientific reports Apr 29, 2021
This cross-sectional study aimed to build a deep learning model for detecting neovascular age-related macular degeneration (AMD) and to distinguish retinal angiomatous proliferation (RAP) from polypoidal choroidal vasculopathy (PCV) using a convoluti...
Polyploidy Tomography, Optical Coherence Macular Degeneration Retinal Neovascularization Choroidal Neovascularization Case-Control Studies Female Humans Cell Proliferation Aged Neural Networks, Computer Diagnosis, Differential Aged, 80 and over Male Cross-Sectional Studies Middle Aged
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Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs.

Nature communications Mar 25, 2021
Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which use...
Prognosis Neoplasms Antineoplastic Agents Cell Line, Tumor Computational Biology Humans Machine Learning Leukemia Cytarabine Proteomics Cell Proliferation Hep G2 Cells Drug Screening Assays, Antitumor
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A deep learning approach for mitosis detection: Application in tumor proliferation prediction from whole slide images.

Artificial intelligence in medicine Mar 6, 2021
The tumor proliferation, which is correlated with tumor grade, is a crucial biomarker indicative of breast cancer patients' prognosis. The most commonly used method in predicting tumor proliferation speed is the counting of mitotic figures in Hematox...
Female Reproducibility of Results Breast Neoplasms Deep Learning Humans Cell Proliferation Mitosis
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Machine-Learning-Based Approach to Decode the Influence of Nanomaterial Properties on Their Interaction with Cells.

ACS applied materials & interfaces Dec 29, 2020
In an nanotoxicity system, cell-nanoparticle (NP) interaction leads to the surface adsorption, uptake, and changes into nuclei/cell phenotype and chemistry, as an indicator of oxidative stress, genotoxicity, and carcinogenicity. Different types of n...
Cell Nucleus Actin Cytoskeleton Cell Nucleus Shape Carbon Epithelial Cells Zonula Occludens-1 Protein Gold Madin Darby Canine Kidney Cells Tight Junctions Dendrimers Metal Nanoparticles Dogs Machine Learning Animals Cell Proliferation
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Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human Graders.

Journal of diabetes research Dec 15, 2020
OBJECTIVE: To evaluate diabetic retinopathy (DR) screening via deep learning (DL) and trained human graders (HG) in a longitudinal cohort, as case spectrum shifts based on treatment referral and new-onset DR.
Macular Edema Image Interpretation, Computer-Assisted Photography National Health Programs Thailand Aged Longitudinal Studies Severity of Illness Index Predictive Value of Tests Male Deep Learning Fundus Oculi Middle Aged Incidence Cell Proliferation Prevalence Humans Mass Screening Diabetic Retinopathy Female Reproducibility of Results
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DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network features.

BMC bioinformatics Nov 12, 2020
BACKGROUND: Circular RNAs (circRNAs) are special noncoding RNA molecules with closed loop structures. Compared with the traditional linear RNA, circRNA is more stable and not easily degraded. Many studies have shown that circRNAs are involved in the ...
RNA, Circular Cell Movement Genetic Loci Ubiquitin-Protein Ligases Gene Ontology Humans Protein Interaction Maps Cell Proliferation DNA Helicases Neural Networks, Computer Cell Line, Tumor
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Molecular imaging and deep learning analysis of uMUC1 expression in response to chemotherapy in an orthotopic model of ovarian cancer.

Scientific reports Sep 10, 2020
Artificial Intelligence (AI) algorithms including deep learning have recently demonstrated remarkable progress in image-recognition tasks. Here, we utilized AI for monitoring the expression of underglycosylated mucin 1 (uMUC1) tumor antigen, a biomar...
Docetaxel Molecular Imaging Xenograft Model Antitumor Assays Mucin-1 Tumor Cells, Cultured Cell Proliferation Mice, Nude Apoptosis Ovarian Neoplasms Animals Antineoplastic Agents Deep Learning Mice Humans Artificial Intelligence Female
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Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks.

Nature communications Sep 1, 2020
Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation ap...
Biomarkers, Tumor Neoplasms Antineoplastic Agents Humans Datasets as Topic Drug Resistance, Neoplasm Oligonucleotide Array Sequence Analysis Cell Proliferation Drug Screening Assays, Antitumor Gene Expression Profiling Bayes Theorem Deep Learning High-Throughput Nucleotide Sequencing Cell Line, Tumor Image Processing, Computer-Assisted
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Artificial Intelligence-Based Quantification of Epithelial Proliferation in Mammary Glands of Rats and Oviducts of Göttingen Minipigs.

Toxicologic pathology Aug 25, 2020
Quantitative assessment of proliferation can be an important endpoint in toxicologic pathology. Traditionally, cell proliferation is quantified by labor-intensive manual counting of positive and negative cells after immunohistochemical staining for p...
Artificial Intelligence Swine, Miniature Female Oviducts Animals Swine Rats Cell Proliferation Epithelial Cells Mammary Glands, Animal
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