Data Analysis

Data science and AI are of existential importance to big pharma, and have a growing role in drug discovery and development. Pharma companies are looking for new ways to cut down the costs and coming up with innovative approaches to drug discovery to continue to be relevant and sustain their impact.

Data science has the potential to make an impact in all operational pipelines. All this information comes in different data types, including structured data, such as SQL database stores (tables of data with columns and rows); unstructured data, such as document files (satellite photos, for example); or streaming data from sensors. Graph learning and graph theory is being used in data science and deep learning. One of the promises of graph learning and deep learning is drug discovery.

Nowadays, a public cloud provider can store petabytes of data and scale up thousands of servers for as long as it is needed to accomplish the big data project. This is available for a reasonable price and can be utilized by any organization in the world. Big data analysis is undoubtedly providing much more hope than hype in drug R&D.

Databases and Softwares

Genotype to Phenotype Analysis and Gene expression regulation

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