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MarkLogic Launches Pharma Research Hub to Accelerate Drug Research and Results

dataversity | May 17, 2019

MarkLogic Launches Pharma Research Hub to Accelerate Drug Research and Results
According to a recent press release, “MarkLogic Corporation, the next generation data platform provider for simplifying data integration, today announced the MarkLogic® Pharma Research Hub to enable pharmaceutical companies to lower drug trial costs and accelerate research. This is achieved by helping researchers quickly and easily find, synthesize and share high-quality data – including, genetic, proteomic, drug, textual, binary and clinical trial data – within a single cloud service. The MarkLogic Pharma Research Hub uses machine learning and other advanced technologies including semantics, fuzzy matching, relevance ranking and rich metadata to manage, organize and retrieve information. As a fully managed cloud service, the Pharma Research Hub can be set up in minutes and ingests data 10x faster than custom-developed solutions – with zero IT management burden.” The release goes on, “By working with five of the largest global pharmaceutical companies and other leaders in the healthcare ecosystem, MarkLogic has long used its Data Hub technology to help solve pharmaceutical companies’ business and data challenges by: (1) Enabling search and visualization of relationships: View, navigate, and search the graph of connections in data by leveraging all inherent relationships. Visualizing these relationships in data, such as how a researcher is connected to institutions and peers, or how a gene, drug target and metabolic pathway are related, can lead to faster discoveries. (2) Leveraging machine learning: Users get better search results on higher quality data. MarkLogic’s Smart Mastering feature uses machine learning to find and consolidate related and duplicate items and to construct a knowledge graph of all data. Data quality rules are applied as data is loaded. This high-quality, mastered data dramatically improves search results and enables better downstream bioinformatics and AI analysis.”
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