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Welcome to the intelligent drug sensitivity analysis platform, an online analysis platform for querying and predicting drug sensitivity. Drug sensitivity prediction is an important part of drug design and development process. Identifying therapeutic biomarkers that predict drug response is very important in individualized medicine. In particular, accurate prediction of specific cancer types and patient drug responses remains a challenging problem. In view of the molecular complexity and heterogeneity of cancer, the prediction of drugs has attracted more and more attention. The intelligent drug sensitivity analysis platform integrates a variety of models, such as elasticnet and ridge, to predict the sensitivity of drugs to cells, so as to help reduce the cost of developing and testing drugs.
To query the sensitivity of known drugs, click here.
To predict the sensitivity of new drugs, click here.
To collaboratively predict the sensitivity of dual anticancer drugs, click here.
If you have other questions, please feel free to contact us!
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Query known Medicine sensitivity.
In this function module, the sensitivity of drugs to specific cells can be queried by inputting cells and selecting drugs. The sensitivity of drugs to cells comes from CCLE database, which provides a wealth of information on drug sensitivity in different types of cancer cells. Our function module provides an efficient and user-friendly way to access this valuable information.
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Predict unknown Medicine sensitivity.
In this function module, we offer users the option to predict drug sensitivity in specific cells by uploading a drug description file and gene expression file. However, this may take some time to complete because it leverages machine learning algorithms such as neural networks to generate predictions based on the provided data. Please be patient while waiting for the results to be generated - the wait time will depend on the complexity of the model and the amount of data provided. If you have any questions, please contact us.
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Synergistic prediction of sensitivity of two Medicines.
Predict the synergy score of anticancer drugs, select a method, upload related files and put them into the back-end integration algorithm for prediction. However, this may take some time to complete because it leverages machine learning algorithms such as neural networks to generate predictions based on the provided data. Please be patient while waiting for the results to be generated - the wait time will depend on the complexity of the model and the amount of data provided.
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Rank the drug sensitivity of the same gene.
Enter the gene sequence of cancer cells, rank the sensitivity of different drugs, and display it in the results. Thus, it is convenient for doctors to prescribe drugs to the case, and it is conducive to doctors to develop targeted drugs for treatment, and it is also conducive to doctors to choose the most appropriate drugs for the disease. However, this may take some time to complete because it leverages machine learning algorithms such as neural networks to generate predictions based on the provided data. Please be patient while waiting for the results to be generated - the wait time will depend on the complexity of the model and the amount of data provided.
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CCLE dataset
The CCLE dataset is available from http://www.broadinstitute.org/ccle publicly. In order to characterize cancer cell lines in depth and accurately, The Cancer Cell Line Encyclopedia (CCLE) was completed in 2012 by teams from Broad Institute, Dana-Farber Cancer Institute and Novartis Biomedical Research Institute in the United States.
