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As such, storage is becoming a major cost element in the genomic IT world where organizations are spending millions on systems and platforms. The role of data engineering is critical in orchestrating, configuring, managing, and monitoring solutions to manage the data bloat problem. Presentations will focus on people, process and technology issues related to storage platforms, integration and migration plans, architectures, governance, and scalability.
Click here for detailed information. We are in the midst of major legal changes affecting data collection, storage, transfer, and use. For example, in the U. In the European Union, a new General Data Protection Regulation will take effect in , with major implications for both collecting health and research data and transferring it to the U.
This presentation will review these developments and then discuss how Bio-IT companies and institutions should respond. The most fundamental questions are: With so many types of data - from experimental, to operational, to clinical, and more - from many different disparate sources, managing data has become a prevalent issue in the industry.
The companies hit the hardest are the small, growing biotechs who attempt to rapidly scale innovative science but lack the formal infrastructure to get past these logistical hurdles. This presentation will address these issues and provide a case study on how Third Rock Ventures, a veritable expert on launching biotech startups, is addressing this common problem. Leveraging Distributed Resources to Speed Discovery. This session will discuss the infrastructure underlying collaborations that use private, academic, and public resources — including commercial cloud and supercomputing centers storage and processing - to maximize options and speed discovery.
Research has become increasingly compute intensive. While new tools and analytical processes such as AI and deep learning hold great promise, they stress the supporting IT infrastructure beyond the expectations of system designers. Learn how today's storage systems leverage software to deliver the performance, scale, and cost efficiencies for applications.
We will cover the Data challenges in both Genomics and BioImaging, including data growth and scale, the need for both collaboration and security, and the hybrid cloud processing requirements. We will describe best practices for cloud scale storage solutions to address these challenges, with example architectures from real customers in Genomics and BioImaging research.
BWA indexing of the human genome was performed for multiple simultaneous indexes and varying numbers of CPUs. Scientific instrumentation generates vast quantities of data that must be processed, analyzed, and stored according to organization policies. The burden of managing this data grows larger every day, increasing exponentially with each scientific breakthrough and technological innovation.
How can a lab, core facility, or large corporation keep up with this pace? This talk will demonstrate how organizations can leverage the features of iRODS to setup automated bioinformatics pipelines, optimize data storage mediums and access patterns, share and collaborate on data, and provide intelligent insight via data visualizations. Scalable and robust data management infrastructure is now table stakes for life sciences researchers that wish to remain competitive in a data-intensive world.
The Globus service supports over 80, investigators in multiple disciplines, who depend on its reliable, secure, file transfer, sharing, and data publication capabilities to streamline research workflows and simplify collaboration. We present use cases from genomics, imaging, and other biomedical research fields, and describe how recent enhancements to the service make Globus suitable for use in protected data environments.
This session features in-depth case studies of leading life sciences organizations that are leveraging high-scale data solutions for genomics, imaging and simulation workflows.
These focus on implemented solutions including: There is great interest in using machine learning to enhance human diagnostic ability across many areas of healthcare. The common denominator in all successful implementations of this technology is the training of models with robust and abundant annotated data. In this session we will discuss how IT infrastructure can support the timely and efficient training of these models.
Omics data increasingly influences clinical decision-making. Well-designed and highly integrated informatics platforms become essential for supporting structured data capturing, integration and analytics to enable effective drug development.
This talk presents principles and key learnings in designing such a platform, and contrasts our current approach to previous approaches in biomedical informatics. Finally, I will provide insights into the implementation of such a platform at Roche. Implementation of a new Clinical Sciences Data Flow process was initiated to streamline processes and allow the integration of a new clinical information environment.
This will help us to increase data quality and shorten turnaround times significantly. Instead of a big bang change we have introduced continuous improvement approach based on agile principles, a microservices based architecture and a lean validation approach.
Incoming data is automatically quality checked, unified and reconciled within an embedded data curation environment. Also, we are making use of Out-of-the-box ETL and message routing capabilities. We would like to share our experience how this approach helped us decreasing software release cycles by a significant factor. Scalable Economy of Secure Information and Services. This project demonstrates a unique framework that enables digital transformation of healthcare at a scale that was not possible before.
Healthcare Data Exchange Framework has a potential to liberate data, empower patient ownership of data and create a free market where data assetization and securitization might serve as incentives for data sharing. Sensitive patient data, financial data of the entity and insurance information are just some of the data that needs to be protected.
In the paper we will research some of the underlying layers of Cybersecurity that pertain to Healthcare. This research hopes to provide a concise framework for healthcare providers to use as a guideline for incorporating their own cybersecurity and to help in engaging cybersecurity third-party companies for assistance.
The five layers of the NIST framework, Identify, Protect, Detect, Respond and Recover, leave healthcare organizations with a large amount of inhouse examinations in order to protect the data of the organization. This document will attempt to build and expound on the NIST framework to provide additional guidance to healthcare providers.
Life sciences research places heavy demands on file storage. The storage system must scale to accommodate an ever-growing volume of data. It must handle billions of files efficiently. Researchers must be able to access the data from anywhere in the world. Learn how universal-scale file storage lets you store and manage massive, globally distributed file sets with ease. The intent of the talk was to deliver a candid and occasionally blunt assessment of the best, the worthwhile, and the most overhyped information technologies IT for life sciences.
The presentation tried to recap the prior year by discussing what has changed or not around infrastructure, storage, computing, and networks. This presentation has helped scientists, leadership, and IT professionals understand the basic topics involved in supporting data intensive science. Come prepared with your questions and commentary for this informative and lively session. View All Media Partners. Download Brochure Workshops Tuesday, May 15 7: An Intro to Blockchain in Life Sciences