AIMC Topic: Antineoplastic Agents
Clear Filters Showing 201 to 210 of 491 articles
Evaluation of external contamination on the vial surfaces of some hazardous drugs that commonly used in Chinese hospitals and comparison between environmental contamination generated during robotic compounding by IV: Dispensing robot vs. manual compounding in biological safety cabinet.
Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
Jun 24, 2021
OBJECTIVES: The aims of the study were to evaluate the external contamination of hazardous drug vials used in Chinese hospitals and to compare environmental contamination generated by a robotic intelligent dispensing system (WEINAS) and a manual comp...
The case for AI-driven cancer clinical trials - The efficacy arm in silico.
Biochimica et biophysica acta. Reviews on cancer
May 31, 2021
Pharmaceutical agents in oncology currently have high attrition rates from early to late phase clinical trials. Recent advances in computational methods, notably causal artificial intelligence, and availability of rich clinico-genomic databases have ...
Machine Learning Approaches to Predict Hepatotoxicity Risk in Patients Receiving Nilotinib.
Molecules (Basel, Switzerland)
May 31, 2021
Although nilotinib hepatotoxicity can cause severe clinical conditions and may alter treatment plans, risk factors affecting nilotinib-induced hepatotoxicity have not been investigated. This study aimed to elucidate the factors affecting nilotinib-i...
Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides.
International journal of molecular sciences
May 26, 2021
Recently, anticancer peptides (ACPs) have emerged as unique and promising therapeutic agents for cancer treatment compared with antibody and small molecule drugs. In addition to experimental methods of ACPs discovery, it is also necessary to develop ...
A computational method for drug sensitivity prediction of cancer cell lines based on various molecular information.
PloS one
Apr 29, 2021
Determining sensitive drugs for a patient is one of the most critical problems in precision medicine. Using genomic profiles of the tumor and drug information can help in tailoring the most efficient treatment for a patient. In this paper, we propose...
Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.
International journal of molecular sciences
Apr 22, 2021
Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in me...
Magnetic tri-bead microrobot assisted near-infrared triggered combined photothermal and chemotherapy of cancer cells.
Scientific reports
Apr 12, 2021
Magnetic micro/nanorobots attracted much attention in biomedical fields because of their precise movement, manipulation, and targeting abilities. However, there is a lack of research on intelligent micro/nanorobots with stimuli-responsive drug delive...
DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines.
IEEE/ACM transactions on computational biology and bioinformatics
Apr 6, 2021
High-throughput screening technologies have provided a large amount of drug sensitivity data for a panel of cancer cell lines and hundreds of compounds. Computational approaches to analyzing these data can benefit anticancer therapeutics by identifyi...
Graph of graphs analysis for multiplexed data with application to imaging mass cytometry.
PLoS computational biology
Mar 29, 2021
Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous prote...
Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs.
Nature communications
Mar 25, 2021
Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which use...
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 31037
- Neural Networks, Computer 29719
- Algorithms 26832
- Deep Learning 26346
- Artificial Intelligence 24295
- Robotics 13311
- Image Processing, Computer-Assisted 9521
- Retrospective Studies 9111
- Magnetic Resonance Imaging 6181
- Reproducibility of Results 5592
- Tomography, X-Ray Computed 4771
- Support Vector Machine 4686
- Computational Biology 4237
- Brain 4050
- Computer Simulation 3881
Recent Journals
- Scientific reports 5949
- Sensors (Basel, Switzerland) 4794
- PloS one 4015
- Neural networks : the official journal of the International Neural Network Society 2930
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1859
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1130
- Journal of chemical information and modeling 998
- Nature communications 944
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 853
- IEEE transactions on neural networks and learning systems 826