AIMC Topic: HeLa Cells

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Integrated bioinformatics combined with machine learning to analyze shared biomarkers and pathways in psoriasis and cervical squamous cell carcinoma.

Frontiers in immunology May 28, 2024
BACKGROUND: Psoriasis extends beyond its dermatological inflammatory manifestations, encompassing systemic inflammation. Existing studies have indicated a potential risk of cervical cancer among patients with psoriasis, suggesting a potential mechani...
Female Carcinoma, Squamous Cell Biomarkers, Tumor Uterine Cervical Neoplasms Gene Expression Profiling Transcriptome Humans Machine Learning Psoriasis MicroRNAs Gene Expression Regulation, Neoplastic HeLa Cells Protein Interaction Maps Signal Transduction Gene Regulatory Networks Databases, Genetic Computational Biology
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Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets.

RNA biology May 17, 2024
2´-O-methylation (Nm) is one of the most abundant modifications found in both mRNAs and noncoding RNAs. It contributes to many biological processes, such as the normal functioning of tRNA, the protection of mRNA against degradation by the decapping a...
Methylation HEK293 Cells Nanopores RNA Processing, Post-Transcriptional Nanopore Sequencing RNA, Messenger Sequence Analysis, RNA HeLa Cells Transcriptome Computational Biology Humans Machine Learning Software
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Predicting Transcription Factor Binding Sites with Deep Learning.

International journal of molecular sciences May 3, 2024
Prediction of binding sites for transcription factors is important to understand how the latter regulate gene expression and how this regulation can be modulated for therapeutic purposes. A consistent number of references address this issue with diff...
Humans Deep Learning Protein Binding HeLa Cells Binding Sites Chromatin Immunoprecipitation Sequencing Computational Biology Transcription Factors Cell Line
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Refinement of paramagnetic bead-based digestion protocol for automatic sample preparation using an artificial neural network.

Talanta Mar 23, 2024
Despite technological advances in the proteomics field, sample preparation still represents the main bottleneck in mass spectrometry (MS) analysis. Bead-based protein aggregation techniques have recently emerged as an efficient, reproducible, and hig...
Mice Rats Proteomics Humans Neural Networks, Computer Animals HeLa Cells Trypsin Automation
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Automated segmentation of cell organelles in volume electron microscopy using deep learning.

Microscopy research and technique Mar 19, 2024
Recent advances in computing power triggered the use of artificial intelligence in image analysis in life sciences. To train these algorithms, a large enough set of certified labeled data is required. The trained neural network is then capable of pro...
Saccharomyces cerevisiae Organelles Volume Electron Microscopy Algorithms Deep Learning Imaging, Three-Dimensional Image Processing, Computer-Assisted HeLa Cells Microscopy, Electron Humans Neural Networks, Computer
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Mass spectrometry-based proteomics data from thousands of HeLa control samples.

Scientific data Jan 23, 2024
Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable am...
Mass Spectrometry HeLa Cells Metadata Machine Learning Proteomics Humans
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Quantification of golgi dispersal and classification using machine learning models.

Micron (Oxford, England : 1993) Oct 1, 2023
The Golgi body is a critical organelle in eukaryotic cells responsible for processing and modifying proteins and lipids. Under certain conditions, such as stress, disease, or ageing, the Golgi structure alters. Therefore, understanding the mechanisms...
HeLa Cells Bayes Theorem Random Forest Humans Machine Learning Aging
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Real-time fluorescence imaging flow cytometry enabled by motion deblurring and deep learning algorithms.

Lab on a chip Aug 8, 2023
Fluorescence imaging flow cytometry (IFC) has been demonstrated as a crucial biomedical technique for analyzing specific cell subpopulations from heterogeneous cellular populations. However, the high-speed flow of fluorescent cells leads to motion bl...
Algorithms Deep Learning Image Processing, Computer-Assisted Humans Flow Cytometry HeLa Cells Optical Imaging
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Analytical chemistry May 1, 2023
Four-dimensional (4D) data-independent acquisition (DIA)-based proteomics is a promising technology. However, its full performance is restricted by the time-consuming building and limited coverage of a project-specific experimental library. Herein, w...
HeLa Cells Humans Peptides Deep Learning Proteomics Proteome
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Moving perfusion culture and live-cell imaging from lab to disc: proof of concept toxicity assay with AI-based image analysis.

Lab on a chip Mar 14, 2023
, cell-based assays are essential in diagnostics and drug development. There are ongoing efforts to establish new technologies that enable real-time detection of cell-drug interaction during culture under flow conditions. Our compact (10 × 10 × 8.5 c...
HeLa Cells Image Processing, Computer-Assisted Humans Microscopy Artificial Intelligence Perfusion
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