A perfect course for Bachelors / Masters / PhD students who are getting started into Drug Discovery research. This course is specially designed keeping in view of beginner level knowledge on Artificial Intelligence, Machine learning and computational drug discovery applications for science students. By the end of this course participants will be equipped with the basic knowledge required to navigate their drug discovery project making use of the Artificial Intelligence and Machine learning based tools.Who this course is for:
Electricity disaggregation identifies individual appliances from one or more aggregate data streams and has immense potential to reduce residential and commercial electrical waste. Since supervised learning methods rely on meticulously labeled training samples that are expensive to obtain, unsupervised methods show the most promise for wide-spread application. However, unsupervised learning methods previously applied to electricity disaggregation suffer from critical limitations. This paper introduces the concept of iterative appliance discovery, a novel unsupervised disaggregation method that progressively identifies the "easiest to find" or "most likely" appliances first. Once these simpler appliances have been identified, the computational complexity of the search space can be significantly reduced, enabling iterative discovery to identify more complex appliances. We test iterative appliance discovery against an existing competitive unsupervised method using two publicly available datasets. Results using different sampling rates show iterative discovery has faster runtimes and produces better accuracy. Furthermore, iterative discovery does not require prior knowledge of appliance characteristics and demonstrates unprecedented scalability to identify long, overlapped sequences that other unsupervised learning algorithms cannot.
GDPR (EU General Data Protection Regulation) is around the corner and bigger companies are getting ready to adopt it as they already know what kind of penalties come from non-compliance. It replaces replaces the Data Protection Directive 95/46/EC and was designed to harmonize data privacy laws across Europe and it is the biggest change on data privacy regulation in 20 years for Europe. While GDPR main elements can be a little tricky to understand, one thing is clear as sensitive Data Discovery is mandatory, so you can find the Personal and sensitive information on your data repositories, that can be almost everything from databases to files. Basically, we focus our data discovery on three main areas: column discovery, data discovery and file discovery. Column discovery is easy to understand, based on specific keywords or sentences we find column names on databases and match it with possible sensitive data.
The 2019 edition of the State of the Discovery Nation report by the Medicines Discovery Catapult and the BIA reveals a thriving service and supply sector for the UK in addition to its R&D biotech's. It also highlights two breakthrough technologies set to influence the future of medicines discovery and maintain the UK's global competitiveness.
Medical research charity LifeArc, and the Milner Therapeutics Institute at the University of Cambridge, have partnered on a project to identify and validate new drug targets in immuno-oncology and respiratory diseases. This will involve combining the drug discovery expertise of LifeArc with the machine-learning and bioinformatics expertise of the Milner Therapeutics Institute to facilitate the identification and selection of novel targets for drug discovery. Data generated will enhance the design of key experiments to validate and prioritise targets. Scientists involved in this project will be co-located at the premises' of both partners to take full advantage of both environments.