AIMC Topic: Antineoplastic Agents

Clear Filters Showing 401 to 410 of 592 articles

Salinomycin-loaded lipid-polymer nanoparticles with anti-CD20 aptamers selectively suppress human CD20+ melanoma stem cells.

Acta pharmacologica Sinica
Melanoma is the deadliest type of skin cancer. CD20+ melanoma stem cells (CSCs) are pivotal for metastasis and initiation of melanoma. Therefore, selective elimination of CD20+ melanoma CSCs represents an effective treatment to eradicate melanoma. Sa...

Commercial glucometer as signal transducer for simple evaluation of DNA methyltransferase activity and inhibitors screening.

Analytica chimica acta
DNA methyltransferase (MTase) plays an important role in many biological processes and has been recognized as a predictive cancer biomarker far before other signs of malignancy and a therapeutic target in cancer treatment. Thus simple and sensitive d...

Selective cytotoxic and genotoxic activities of 5-(2-bromo-5-methoxybenzylidene)-thiazolidine-2,4-dione against NCI-H292 human lung carcinoma cells.

Pharmacological reports : PR
BACKGROUND: Thiazolidine-2,4-dione ring system is used as a pharmacophore to build various heterocyclic compounds aimed to interact with biological targets. In the present study, benzylidene-2,4-thiazolidinedione derivatives (compounds 2-5) were synt...

Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

PloS one
Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal...

Reconstructing cancer drug response networks using multitask learning.

BMC systems biology
BACKGROUND: Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific respon...

Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.

Biochemical and biophysical research communications
Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) ...

Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology.

Disease markers
Using kernel machine learning (ML) and random optimization (RO) techniques, we recently developed a set of venous thromboembolism (VTE) risk predictors, which could be useful to devise a web interface for VTE risk stratification in chemotherapy-treat...