AIMC Topic: HeLa Cells

Clear Filters Showing 41 to 50 of 100 articles

Identification of the human DPR core promoter element using machine learning.

Nature Sep 9, 2020
The RNA polymerase II (Pol II) core promoter is the strategic site of convergence of the signals that lead to the initiation of DNA transcription, but the downstream core promoter in humans has been difficult to understand. Here we analyse the human ...
High-Throughput Nucleotide Sequencing HeLa Cells Datasets as Topic Gene Expression Regulation Humans Computer Simulation Models, Genetic Support Vector Machine Cells RNA Polymerase II Consensus Sequence TATA Box Base Sequence Promoter Regions, Genetic Mutagenesis Transcription, Genetic
View on PubMed DOI

A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes.

Nature communications Aug 21, 2020
Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identif...
Humans Machine Learning Cell Survival Proteolysis Ligands Drug Delivery Systems Binding Sites Computational Biology HeLa Cells Drug Discovery Phosphotransferases Saccharomyces cerevisiae Proteomics Protein Binding Botrytis Proteome Mass Spectrometry
View on PubMed DOI

Accurate prediction of species-specific 2-hydroxyisobutyrylation sites based on machine learning frameworks.

Analytical biochemistry May 28, 2020
Lysine 2-hydroxyisobutyrylation (K) is a newly discovered post-translational modification (PTM) across eukaryotes and prokaryotes in recent years, which plays a significant role in diverse cellular functions. Accurate prediction of K sites is a first...
Machine Learning Seeds Protein Processing, Post-Translational Oryza Humans Lysine Hydroxybutyrates HeLa Cells Bryopsida Saccharomyces cerevisiae Species Specificity
View on PubMed DOI

Robust classification of cell cycle phase and biological feature extraction by image-based deep learning.

Molecular biology of the cell Apr 22, 2020
Across the cell cycle, the subcellular organization undergoes major spatiotemporal changes that could in principle contain biological features that could potentially represent cell cycle phase. We applied convolutional neural network-based classifier...
Cell Cycle Golgi Apparatus Image Processing, Computer-Assisted Mice Cell Line Microscopy, Fluorescence HeLa Cells Microtubules Cell Nucleus NIH 3T3 Cells Humans Animals Deep Learning
View on PubMed DOI

A Novel System for Functional Determination of Variants of Uncertain Significance using Deep Convolutional Neural Networks.

Scientific reports Mar 6, 2020
Many drugs are developed for commonly occurring, well studied cancer drivers such as vemurafenib for BRAF V600E and erlotinib for EGFR exon 19 mutations. However, most tumors also harbor mutations which have an uncertain role in disease formation, co...
Datasets as Topic Neural Networks, Computer Humans Mutation HeLa Cells
View on PubMed DOI

In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics.

Nature communications Jan 9, 2020
Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by data-dependent acquisition (DDA) experiments are required prior to DIA ...
Humans Computer Simulation Computational Biology Proteomics Proteome Data Science Mass Spectrometry Databases, Protein Cell Line, Tumor HeLa Cells Mice Serum Animals Peptide Library Peptides Deep Learning
View on PubMed DOI

MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks.

BMC genomics Dec 24, 2019
BACKGROUND: Tandem mass spectrometry allows biologists to identify and quantify protein samples in the form of digested peptide sequences. When performing peptide identification, spectral library search is more sensitive than traditional database sea...
Peptides Deep Learning Proteins Tandem Mass Spectrometry Humans HeLa Cells Neural Networks, Computer Sequence Analysis, Protein
View on PubMed DOI

Graphene quantum dots as singlet oxygen producer or radical quencher - The matter of functionalization with urea/thiourea.

Materials science & engineering. C, Materials for biological applications Dec 12, 2019
Due to their low cost and possible green synthesis, high stability and resistance to photobleaching, graphene quantum dots (GQDs) can be considered as one of the class of carbon nanomaterials which may have great potential as an agent for photosensit...
Singlet Oxygen HeLa Cells Humans Graphite Thiourea Neoplasms Photochemotherapy Quantum Dots Photosensitizing Agents
View on PubMed DOI

DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput.

Nature methods Nov 25, 2019
We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves t...
High-Throughput Screening Assays Species Specificity Mass Spectrometry Zea mays HeLa Cells Neural Networks, Computer Software Proteomics Proteome Humans
View on PubMed DOI

Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning.

Nature communications Sep 19, 2019
Highly specific Cas9 nucleases derived from SpCas9 are valuable tools for genome editing, but their wide applications are hampered by a lack of knowledge governing guide RNA (gRNA) activity. Here, we perform a genome-scale screen to measure gRNA acti...
Base Sequence Humans Clustered Regularly Interspaced Short Palindromic Repeats Animals Gene Editing Deep Learning Endonucleases Internet Promoter Regions, Genetic Cell Line, Tumor INDEL Mutation HeLa Cells Mice RNA, Guide, CRISPR-Cas Systems HEK293 Cells CRISPR-Cas Systems
View on PubMed DOI
Popular Topics
Recent Journals