We have updated the Drug Name Recogniser Google Sheets™ plugin to cover molecular mass, chemical formula and SMILES strings.
Link to install the tool: https://fastdatascience.com/ai-in-pharma/drug-name-recogniser
Natural language processing
We have a Python library available which you can find on PyPI and on Github. It’s fully open source with MIT License.
You can install the Python library by typing in the command line:
pip install drug-named-entity-recognition
You can also try the library in your browser on Fast Data Science.
Fast Data Science provides NLP consulting for the pharma industries. Our flagship product is the Clinical Trial Risk Tool https://clinicaltrialrisk.org.
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This free A/B test calculator will help you compare two variants of your website, A and B, and tell you the probability that B is better. You can read more about A/B testing in our earlier blog post on the subject. You may also be interested in our Chi-Squared sample size calculator which will help you calculate the minimum sample size needed to run a Chi-Squared test, given an expected standardised effect size.
See also: Fast Data Science A/B test Calculator (Bayesian) A/B testing is a way you can test two things, Thing A, and Thing B, to see which is better. You most commonly hear about A/B testing in the context of commercial websites, but A/B testing can be done in a number of different contexts, including offline marketing, and testing prices.
Explainable AI for Businesses Guest post by Vidhya Sudani Introduction AI is moving rapidly and it can be hard to understand how an AI model works and what decisions it makes. Businesses are increasingly turning to Explainable AI (XAI) to demystify the “black box” nature of traditional machine learning models.
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