AIMC Topic: Antineoplastic Agents
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Reconstructing cancer drug response networks using multitask learning.
BMC systems biology
Oct 10, 2017
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
Oct 7, 2017
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
Sep 17, 2017
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...
Finding disagreement pathway signatures and constructing an ensemble model for cancer classification.
Scientific reports
Aug 30, 2017
Cancer classification based on molecular level is a relatively routine research procedure with advances in high-throughput molecular profiling techniques. However, the number of genes typically far exceeds the number of the sample size in gene expres...
Reinforcement learning-based control of drug dosing for cancer chemotherapy treatment.
Mathematical biosciences
Aug 16, 2017
The increasing threat of cancer to human life and the improvement in survival rate of this disease due to effective treatment has promoted research in various related fields. This research has shaped clinical trials and emphasized the necessity to pr...
Drug Target Prediction by Multi-View Low Rank Embedding.
IEEE/ACM transactions on computational biology and bioinformatics
May 18, 2017
Drug repositioning has been a key problem in drug development, and heterogeneous data sources are used to predict drug-target interactions by different approaches. However, most of studies focus on a single representation of drugs or proteins. It has...
CATTLE (CAncer treatment treasury with linked evidence): An integrated knowledge base for personalized oncology research and practice.
CPT: pharmacometrics & systems pharmacology
Mar 13, 2017
Despite the existence of various databases cataloging cancer drugs, there is an emerging need to support the development and application of personalized therapies, where an integrated understanding of the clinical factors and drug mechanism of action...
Use of Model Predictive Control and Artificial Neural Networks to Optimize the Ultrasonic Release of a Model Drug From Liposomes.
IEEE transactions on nanobioscience
Jan 30, 2017
The use of echogenic liposomes to deliver chemotherapeutic agents for cancer treatment has gained wide recognition in the last 20 years. Cancerous cells can develop multiple drug resistance (MDR), in part, due to the drop in concentration of chemothe...
Simultaneous delivery of anti-miR21 with doxorubicin prodrug by mimetic lipoprotein nanoparticles for synergistic effect against drug resistance in cancer cells.
International journal of nanomedicine
Dec 30, 2016
The development of drug resistance in cancer cells is one of the major obstacles to achieving effective chemotherapy. We hypothesized that the combination of a doxorubicin (Dox) prodrug and microRNA (miR)21 inhibitor might show synergistic antitumor ...
Betulin-loaded PEDOT films for regional chemotherapy.
Materials science & engineering. C, Materials for biological applications
Dec 28, 2016
Chemotherapy is one of the most commonly used cancer treatments. Even so, it has significant adverse effects on healthy tissues. These effects can be avoided through the use of regional chemotherapy, an approach based on delivering the anti-cancer ag...
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