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    Date submitted
  • 30-Aug-2017

AI for Bio-NLP

Abstract

We empower life sciences organizations to create actionable data through data wrangling and data visualization. We reverse engineer the knowledge in unstructured format to structured data by semi-automation to enable the researchers on making better decisions. With the increased generation of data from a wide number of sources, including R&D, clinical, translational, genomic and personal data, pharma and biotech must effectively manage and integrate data from all stages of the pharmaceutical value chain to enable more informed decisions. We apply modern machine learning methods, such as deep learning, to leverage the big data sets for finding hidden structure within them, and use natural language processing (NLP) to derive insight for biomedical corpus.

Video

Original Slideshare URL: Open

Introduction Video

Additional Questions

Who is your customer?

small, medium, and large Pharma and Biotech and start up AI companies

What problem does this idea/product solve or what market need does it serve?

Right now, the pharmaceutical industry is facing a major hurdle to efficiency: when it comes to looking at the knowledge generated by a drug or disease state, it is extremely hard to see the full picture. Our product helps to connect the dots and bring a complete picture using deep learning and natural language processing.

What attributes will make this idea/product successful? Why do you believe that those features will create success?

Recent break through algorithms and deep learning and user friendly tools to work with those algorithms as well as the availability of open source databases, the convergence of these areas will bring the success.

Explain how you (your team) will execute to make this idea/product successful? What gives you (your team) an advantage over others already in the market or new to this market?

Our team has the experienced leaders in AI (deep learning and machine learning scientist from Argonne National Lab, Chicago) and Pharma (cancer research from Takeda, Boston) as Advisers. The company is found by an experienced Scientist and Entrepreneur who is successful in Informatics business for more than 15 years. Our technical team is headed by a professional who has worked in Sun Microsystem (Bay area) for 10 years and has extensive knowledge in cloud computing, big data. Our software development team is headed by experienced professional who has 10 years experience in software development worked earlier in several organization. Our enthusiastic team has the right mix of drug discovery, machine learning, python programming and software development. And we have the ability to quickly scale up based on the need. These factors will enable us to succeed in our project.