AIMC Topic: 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...
Female Aged Aged, 80 and over Cross-Sectional Studies Male Humans Neural Networks, Computer Tomography, Optical Coherence Macular Degeneration Retinal Neovascularization Choroidal Neovascularization Polyploidy Middle Aged Case-Control Studies Cell Proliferation Diagnosis, Differential
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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...
Leukemia Cytarabine Prognosis Humans Neoplasms Machine Learning Antineoplastic Agents Cell Line, Tumor Computational Biology 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...
Humans Female Reproducibility of Results Breast Neoplasms Deep Learning 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...
Animals Cell Proliferation Carbon Epithelial Cells Zonula Occludens-1 Protein Machine Learning Tight Junctions Dendrimers Cell Nucleus Actin Cytoskeleton Cell Nucleus Shape Gold Metal Nanoparticles Dogs Madin Darby Canine Kidney Cells
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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 Thailand Image Interpretation, Computer-Assisted Severity of Illness Index Photography Deep Learning Aged National Health Programs Incidence Male Humans Cell Proliferation Middle Aged Diabetic Retinopathy Mass Screening Prevalence Female Longitudinal Studies Reproducibility of Results Predictive Value of Tests Fundus Oculi
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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 ...
Ubiquitin-Protein Ligases Gene Ontology RNA, Circular Humans Neural Networks, Computer Cell Line, Tumor Protein Interaction Maps Cell Proliferation DNA Helicases Cell Movement Genetic Loci
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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...
Humans Artificial Intelligence Female Molecular Imaging Xenograft Model Antitumor Assays Cell Proliferation Mucin-1 Animals Mice, Nude Tumor Cells, Cultured Antineoplastic Agents Deep Learning Apoptosis Mice Ovarian Neoplasms Docetaxel
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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...
Oligonucleotide Array Sequence Analysis Drug Screening Assays, Antitumor Bayes Theorem High-Throughput Nucleotide Sequencing Datasets as Topic Drug Resistance, Neoplasm Deep Learning Cell Line, Tumor Image Processing, Computer-Assisted Cell Proliferation Humans Biomarkers, Tumor Neoplasms Antineoplastic Agents Gene Expression Profiling
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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...
Rats Cell Proliferation Epithelial Cells Mammary Glands, Animal Swine, Miniature Oviducts Artificial Intelligence Female Animals Swine
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Hypothesis-free deep survival learning applied to the tumour microenvironment in gastric cancer.

The journal of pathology. Clinical research Jun 27, 2020
The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we p...
Humans Risk Factors Stomach Neoplasms Tumor Microenvironment CD8 Antigens Biomarkers, Tumor Predictive Value of Tests Antigens, CD20 Antigens, CD Ki-67 Antigen Neoplasm Staging Tissue Array Analysis Image Interpretation, Computer-Assisted Antigens, Differentiation, Myelomonocytic Immunohistochemistry Risk Assessment Deep Learning Time Factors Cell Proliferation
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