AIMC Topic: Cell Line, Tumor
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Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects.
Nature communications
Dec 1, 2020
We present comboFM, a machine learning framework for predicting the responses of drug combinations in pre-clinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through...
Image-based phenotyping of disaggregated cells using deep learning.
Communications biology
Nov 13, 2020
The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undes...
DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network features.
BMC bioinformatics
Nov 12, 2020
BACKGROUND: Circular RNAs (circRNAs) are special noncoding RNA molecules with closed loop structures. Compared with the traditional linear RNA, circRNA is more stable and not easily degraded. Many studies have shown that circRNAs are involved in the ...
Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction.
BMC bioinformatics
Nov 12, 2020
BACKGROUND: Protein kinases are a large family of druggable proteins that are genomically and proteomically altered in many human cancers. Kinase-targeted drugs are emerging as promising avenues for personalized medicine because of the differential r...
H-RACS: a handy tool to rank anti-cancer synergistic drugs.
Aging
Nov 10, 2020
Though promising, identifying synergistic combinations from a large pool of candidate drugs remains challenging for cancer treatment. Due to unclear mechanism and limited confirmed cases, only a few computational algorithms are able to predict drug s...
Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.
Nature communications
Oct 30, 2020
Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational...
A Machine Learning-Assisted Nanoparticle-Printed Biochip for Real-Time Single Cancer Cell Analysis.
Advanced biosystems
Oct 7, 2020
Cancers are a complex conglomerate of heterogeneous cell populations with varying genotypes and phenotypes. The intercellular heterogeneity within the same tumor and intratumor heterogeneity within various tumors are the leading causes of resistance ...
Myricitrin inhibits vascular endothelial growth factor-induced angiogenesis of human umbilical vein endothelial cells and mice.
Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
Sep 17, 2020
In the present study, the protective effects of myricitrin against vascular endothelial growth factor (VEGF)-induced angiogenesis of vascular endothelial cells were characterized. Cells were induced with 50 ng/mL VEGF in the presence or absence of va...
Vascular Endothelial Growth Factors
Apoptosis
Cell Line, Tumor
Xenograft Model Antitumor Assays
Caspases
Mice
Humans
Flavonoids
Biomarkers
Angiogenesis Inhibitors
Human Umbilical Vein Endothelial Cells
Matrix Metalloproteinases
Animals
Disease Models, Animal
Neovascularization, Pathologic
Neovascularization, Physiologic
A new diarylhexane and two new diarylpropanols from the roots of .
Natural product research
Sep 16, 2020
A new diarylhexane, kneglobularone B () and two new diarylpropanols, kneglobularols A - B () along with seven known compounds () were isolated and characterized from the roots of It is the first time to find arylpropyl quinone () and isoflavone () i...
Shape-to-graph mapping method for efficient characterization and classification of complex geometries in biological images.
PLoS computational biology
Sep 3, 2020
With the ever-increasing quality and quantity of imaging data in biomedical research comes the demand for computational methodologies that enable efficient and reliable automated extraction of the quantitative information contained within these image...
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