AIMC Topic: Cell Line, Tumor
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Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning.
Scientific reports
Sep 30, 2021
Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. ...
Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images.
Cells
Sep 29, 2021
In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived fro...
Time-resolved in vivo ubiquitinome profiling by DIA-MS reveals USP7 targets on a proteome-wide scale.
Nature communications
Sep 13, 2021
Mass spectrometry (MS)-based ubiquitinomics provides system-level understanding of ubiquitin signaling. Here we present a scalable workflow for deep and precise in vivo ubiquitinome profiling, coupling an improved sample preparation protocol with dat...
Predicting drug sensitivity of cancer cells based on DNA methylation levels.
PloS one
Sep 10, 2021
Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular featur...
Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality.
Molecular cancer
Aug 28, 2021
BACKGROUND: Synthetic lethality describes a genetic interaction between two perturbations, leading to cell death, whereas neither event alone has a significant effect on cell viability. This concept can be exploited to specifically target tumor cells...
A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer.
Scientific reports
Aug 19, 2021
Ovarian cancer is associated with poor prognosis. Platinum resistance contributes significantly to the high rate of tumour recurrence. We aimed to identify a set of molecular markers for predicting platinum sensitivity. A signature predicting cisplat...
Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.
PLoS computational biology
Jul 23, 2021
Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitating the metastatic cascade. Metastatic lesions often exhibit a similar epithelial-like state to that ...
Adaptation, Physiological
Biophysical Phenomena
Cell Adhesion
Tumor Microenvironment
Cell Shape
Vimentin
Biomarkers, Tumor
Cell Count
Cell Line, Tumor
Humans
Cadherins
Triple Negative Breast Neoplasms
Neoplasm Metastasis
Computational Biology
Epithelial-Mesenchymal Transition
Machine Learning
Extracellular Matrix
Models, Biological
Cell Proliferation
Female
Antigens, CD
Synthesis, docking, machine learning and antiproliferative activity of the 6-ferrocene/heterocycle-2-aminopyrimidine and 5-ferrocene-1H-Pyrazole derivatives obtained by microwave-assisted Atwal reaction as potential anticancer agents.
Bioorganic & medicinal chemistry letters
Jul 2, 2021
A simple and fast methodology under microwave irradiation for the synthesis of 2-aminopyrimidine and pyrazole derivatives using Atwal reaction is reported. After the optimization of the reaction conditions, eight 2-aminolpyrimidines containing ferroc...
Optical tissue clearing and machine learning can precisely characterize extravasation and blood vessel architecture in brain tumors.
Communications biology
Jul 1, 2021
Precise methods for quantifying drug accumulation in brain tissue are currently very limited, challenging the development of new therapeutics for brain disorders. Transcardial perfusion is instrumental for removing the intravascular fraction of an in...
Converting tabular data into images for deep learning with convolutional neural networks.
Scientific reports
May 31, 2021
Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relation...
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