AIMC Topic: HeLa Cells

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Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides.

Scientific reports May 20, 2021
Cell-penetrating peptides have important therapeutic applications in drug delivery, but the variety of known cell-penetrating peptides is still limited. With a promise to accelerate peptide development, artificial intelligence (AI) techniques includi...
Molecular Dynamics Simulation Amino Acid Sequence HeLa Cells Cell Membrane Artificial Intelligence Reproducibility of Results Cell Survival Humans Cell-Penetrating Peptides
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Deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations.

Traffic (Copenhagen, Denmark) May 16, 2021
Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resul...
HeLa Cells Microscopy, Electron Nuclear Envelope Volunteers Humans Deep Learning
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Deep learning the collisional cross sections of the peptide universe from a million experimental values.

Nature communications Feb 19, 2021
The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million dat...
Workflow Amino Acid Sequence Tandem Mass Spectrometry Ions Proteomics Proteome Escherichia coli Caenorhabditis elegans HeLa Cells Saccharomyces cerevisiae Drosophila melanogaster Humans Peptides Deep Learning Neural Networks, Computer Animals
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HSM6AP: a high-precision predictor for the Homo N6-methyladenosine (m^6 A) based on multiple weights and feature stitching.

RNA biology Feb 12, 2021
Recent studies have shown that RNA methylation modification can affect RNA transcription, metabolism, splicing and stability. In addition, RNA methylation modification has been associated with cancer, obesity and other diseases. Based on information ...
A549 Cells HeLa Cells Adenosine HEK293 Cells Algorithms Methylation Humans Gene Expression Regulation Computational Biology Machine Learning
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Deep diversification of an AAV capsid protein by machine learning.

Nature biotechnology Feb 11, 2021
Modern experimental technologies can assay large numbers of biological sequences, but engineered protein libraries rarely exceed the sequence diversity of natural protein families. Machine learning (ML) models trained directly on experimental data wi...
Humans Machine Learning HeLa Cells Genetic Vectors Capsid Proteins Dependovirus
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Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia.

Nature neuroscience Jan 11, 2021
Retrotransposons can cause somatic genome variation in the human nervous system, which is hypothesized to have relevance to brain development and neuropsychiatric disease. However, the detection of individual somatic mobile element insertions present...
Adult Long Interspersed Nucleotide Elements Adaptor Proteins, Signal Transducing High-Throughput Nucleotide Sequencing HeLa Cells Neurons Schizophrenia Machine Learning Mental Disorders Pregnancy Female Humans Embryonic Development Genome Neuroglia Cation Transport Proteins Retroelements Mutagenesis, Insertional
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sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.

Plant molecular biology Jan 1, 2021
We proposed an ensemble convolutional neural network model to identify sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for sequence encoding. As an important component of the CRISPR/Cas9 system, single-guide RNA (s...
Glycine max RNA, Guide, CRISPR-Cas Systems HEK293 Cells Sorghum Gene Editing HeLa Cells Crops, Agricultural HCT116 Cells Neural Networks, Computer Algorithms Computational Biology Triticum Internet Zea mays CRISPR-Cas Systems Humans
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Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy.

PloS one Dec 8, 2020
The accurate segmentation and tracking of cells in microscopy image sequences is an important task in biomedical research, e.g., for studying the development of tissues, organs or entire organisms. However, the segmentation of touching cells in image...
HeLa Cells Optical Imaging Cell Tracking Deep Learning Image Processing, Computer-Assisted Humans Microscopy Neural Networks, Computer Algorithms
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Analyzing protein dynamics from fluorescence intensity traces using unsupervised deep learning network.

Communications biology Nov 12, 2020
We propose an unsupervised deep learning network to analyze the dynamics of membrane proteins from the fluorescence intensity traces. This system was trained in an unsupervised manner with the raw experimental time traces and synthesized ones, so nei...
HeLa Cells Carbocyanines Humans Deep Learning MCF-7 Cells Microscopy, Fluorescence Membrane Proteins Fluorescent Dyes Unsupervised Machine Learning Diffusion
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NuSeT: A deep learning tool for reliably separating and analyzing crowded cells.

PLoS computational biology Sep 14, 2020
Segmenting cell nuclei within microscopy images is a ubiquitous task in biological research and clinical applications. Unfortunately, segmenting low-contrast overlapping objects that may be tightly packed is a major bottleneck in standard deep learni...
HeLa Cells Artifacts Cell Nucleus Image Processing, Computer-Assisted Computational Biology Humans Algorithms Microscopy Software Deep Learning
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