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HeLa Cells

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Volatile constituents and in vitro activity of Syzygium aromaticum flower buds (clove) against human cancer cell lines.

Pakistan journal of pharmaceutical sciences
The methanolic extract (SA-EXT) of Syzygium aromaticum flower buds and its fractions tested against three human cancer cell lines viz uterine cervix (HeLa), breast (MCF-7) and lung NCI (H-460) using sulforhodamine-B assay. The ethyl acetate soluble s...

Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy.

PloS one
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...

Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia.

Nature neuroscience
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...

sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.

Plant molecular biology
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...

Deep learning the collisional cross sections of the peptide universe from a million experimental values.

Nature communications
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...

Deep diversification of an AAV capsid protein by machine learning.

Nature biotechnology
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...

A machine learning-based framework for modeling transcription elongation.

Proceedings of the National Academy of Sciences of the United States of America
RNA polymerase II (Pol II) generally pauses at certain positions along gene bodies, thereby interrupting the transcription elongation process, which is often coupled with various important biological functions, such as precursor mRNA splicing and gen...

HSM6AP: a high-precision predictor for the Homo N6-methyladenosine (m^6 A) based on multiple weights and feature stitching.

RNA biology
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 ...

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

Scientific reports
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...

Deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations.

Traffic (Copenhagen, Denmark)
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...