Cambridge Healthtech Institute’s 19th Annual
Bio-IT World Conference & Expo
April 21-23, 2020
SPEAKER PROPOSALS NOW BEING ACCEPTED
The future of precision medicine will be driven by the questions we ask and powered by the technologies that help us answer them.
Bio-IT World Conference & Expo, one of the top conferences in the life sciences industry, is accepting submissions for excellent speakers. Do you have innovative life sciences research to share? Are you developing novel approaches to working with
data? Are you identifying new approaches to address critical questions in medicine and in biology? Our speakers give engaging and informative podium and panel presentations describing technology and analytic approaches that solve problems, accelerate science, and drive the future of precision medicine.
In life sciences research, our capacity to create, analyze, and apply data is exploding. While the application opportunities are vast and thrilling, we run the risk of creating dangerous divisions between data generators and data analyzers, scientists
and data scientists, subject matter experts and “tech people”, and the questions and the answers.
At Bio-IT World, these aren’t new ideas; we have always aimed to tear down those walls and support a cohesive community. But the challenge is only growing as we push our industry to the edge of discovery, with important implications for how we educate,
hire, and train our growing workforce.
We are excited that the tracks at the 19th Annual Bio-IT World Conference & Expo will focus on tackling these challenges. Across 250 speaking slots—panel discussions, joint partner talks, and podium presentations—in 16 tracks and 24 workshops,
our speakers will identify the next big problems for precision medicine and share how they are using technologies and analytic approaches to support the mission.
When the community of 3,200+ life sciences, pharmaceutical, clinical, healthcare, and IT professional attendees from 40+ countries come together at the 2020 Bio-IT World Conference & Expo, our conversation will focus on how we are using technologies
and analytic approaches to solve problems, accelerate science, and drive the future of precision medicine.
We need speakers who are asking the right questions and exploring what comes next. Join us.
TRACKS (taking place Wednesday, April 22 – Thursday, April 23)
Track 1: Data Storage and Transport:
Storage and High-Speed Data Transfer Solutions to Enable Productivity and Foster Collaboration
Is the burden of managing your data growing larger every day? Do you have a scalable and robust data management infrastructure in place to store, process, analyze, and transfer vast quantities of data according to your organization policies? Are you optimizing
storage for performance vs. capacity, ideally with seamless automation, interoperability, and redundancy in multi-tier solutions? Is your organization using new tools and analytical processes such as AI and deep learning that stress your supporting
IT infrastructure beyond the expectations of system designers? Managing data has become a prevalent issue in the life sciences industry. Organizations are spending millions on systems and platforms to manage, store, and transfer many types of data
(e.g., experimental, operational, clinical) from many different disparate sources. The role of data engineering is critical in orchestrating, configuring, managing, and scaling solutions to manage the data bloat problem. The Data Storage and Transport track presents in-depth case studies from leading life science organizations who are implementing solutions to address data storage and transfer problems and challenges. These include where to store data (cloud, local, mixture), what is the optimal
configuration regarding price vs. access, estimating data storage costs and making financial models, understanding and planning for costs in the cloud, what to do with large third-party databases (inter-pharma collaborations, genomic/expression datasets),
what to do with imaging collaboration that produces 100 TB, "rehydrating" a data archive (from tape) for re-analysis, determining if you're storing the right stuff, figuring out the best way to deliver data products to customers/collaborators, and
more. How are you developing technologies to deal with influx of digital data from digital health devices?
Track 2 - NEW: Data and Metadata Management:
Manage Workflows and Administer Effective Data Processes to Satisfy Increased Computing Power Demands
With the increased demand in computing power from life science researchers and scientists tackling big data issues, storage and infrastructure must be able to scale to handle billions of data points and files efficiently. The problem is administration
of data to ensure information can be integrated, accessed, shared, linked, analyzed, and maintained to best effect across the organization. The Data and Metadata Management track presents in-depth case studies from leading
life science organizations who are implementing solutions to address data and metadata management problems and challenges. These include how to manage workflows with data and metadata without rerunning everything, but with the ability to handle data
updates and new versions of the software. We will also explore how to associate the processed data and features with the raw data for analysis purposes. Other problems we address include picking the correct metadata framework for lab/science data,
harmonizing data from disparate sources, determining who is accountable for data correctness, explore existing standards and if they are sufficient in meeting organizational needs, and more. How are you handling with and anticipating the regulatory challenges that will accompany continuous data flow, including provenance, ownership and access?
Track 3 - NEW: Data Science and Analytics Technologies:
Transform Data into Fast Answers, at Scale
The practice of data science requires the use of data analytics tools and technologies like Python, R, SQL, and Tensorflow and approaches like graph databases and column stores to help data professionals gain extra insights and value from data. The Data Science and Analytics Technologies track will explore popular analytics tools, technologies, languages, and approaches to managing highly complex data that data scientists are using. Most importantly, presentations will explore what problems data science and analytics technologies
are solving within the field, specific methods that are being applied, how to assess the value add against cost, and how to set up team structure within the organization. How do you involve the end-user in defining the requirements necessary to make the results of these analytics easily actionable?
Track 4: Software Applications and Services:
Drive Data Manipulation and Scientific Decision-Making by Leveraging Software Tools
As data generation increases, there is a need for workflows that are reproducible across infrastructures and empower scientists and researchers to apply cutting-edge analysis methods. One problem is scientific data are not centralized or standardized
and are fragmented—from instrumentation to clinical research to legacy software. A second problem is critical validation of algorithms is commonly overlooked. There are real-world differences in data equivalence for integration and analysis,
without adequate contextual information. A third problem is navigating vendor silos and deciding between all-in-one solutions or niche providers. The Software Applications and Services track explores these problems and how biopharma
companies implementing solutions to drive data strategies and scientific decision-making by leveraging software tools and data platforms. Case studies will be presented on software tools to facilitate data manipulation, data analytics approaches,
data methods and standards approaches, integration strategies, transparency, efficiency, security, and cost-effective solutions. What are critical approaches to validation of complex software development that will have to deal with missing/incomplete data, conflicting data, sparse data, etc? These will become increasingly important as regulatory agencies evolve procedures and rules for implementation.
Track 5: Data Security and Compliance:
Adopt Data-Centric Approaches to Reduce Costs, Mitigate Risks and Meet Compliance
Data security is defined by the processes and mechanisms in place that prevent data misuse and identify threat risks. However, many biopharmaceutical research data sources, from IP to genomic to mobile, require different levels of security. The reality
is that it matters not where your data exist, but the ways in which data are accessed. Are you doing enough for information security? How do you demonstrate that you are compliant? How do you handle an executive of the company who is the worst offender
on information security practices? The Data Security and Compliance presents in-depth case studies from leading life science organizations who are implementing solutions to address these data security and compliance problems and challenges.
Presentations will also address security services from private to cloud-based systems for academic, government, clinical, biomedical, and pharmaceutical networks. Learn how to adopt a data-centric approach to help you reduce costs, mitigate risk,
and meet compliance. Data standards for compliance and security need to deal with global communities, and where guidelines and requirements may differ, e.g. GDPR. How do you incorporate this especially when considering the use of real world data?
Track 6: Cloud Computing:
Apply Cloud for Expanding Applications to Accelerate Research and Facilitate Collaboration
Cloud computing has become the platform enterprises utilize for their application of analyzing, storing, processing, exploring, and sharing dynamic data. These data-intensive life scientists from biological researchers to biopharmaceutical organizations
demand this practicality and necessity. Thus, adoption has been greater than anyone expected, and users continue to expand applications. Through case studies, the Cloud Computing track explores the rapid growth and progressive maturation
of cloud as well as evolving provider and user experiences and challenges. Some of these challenges include how to handle new equipment pumping data into cloud when most internal tools run locally, reproduce processes and new workflows, accelerate
research and find new ways to collaborate, remove data storage and processing bottlenecks, and make significant business impact across R&D in enabling large-scale modeling and simulation. How will these activities been impacted by the evolution of quantum computing methods and eventually computers?
Track 7: AI for Drug Discovery:
Harness the Power of AI and Machine Learning to Maximize and Accelerate Drug Discovery Efforts
The use of artificial intelligence in biopharma has redefined how scientists develop new drugs, tackle disease, and more. The AI for Drug Discovery track will discuss problems that biopharma organizations are experiencing in harnessing
the power of AI and machine learning technologies to maximize and accelerate drug discovery efforts from early stage to adoption to practical application. Speakers will explore the role of AI in developing new drugs, tackling diseases previously deemed
too difficult to take on, the R&D process, chemical synthesis optimization, drug repositioning, making sense of clinical data, predicting clinical trial outcomes, finding correct patients for clinical trials, analyzing real-world evidence, making
sense of complex medical data, and avoiding "brittle" results based on too little/biased data. How are you moving beyond correlative analysis towards causal analysis so that these approaches can become generally effective? How do you integrate the challenges that exist in the real world practice of medicine…and with real patients?
Track 8 - NEW: Emerging AI Technologies:
Accelerate Timelines and Optimize Results by Leveraging Advanced Analytics Tools
AI technologies are essential to the advancement of precision medicine. There is no shortage of examples of how they can be leveraged from R&D to translational research, clinical trials, and real-world data. However, the current round of solutions
can be expensive and complex to deploy and manage. The Emerging AI Technologies track will explore currently deployed use cases of AI technology, what problems have been addressed successfully, where there are some challenges, and
what future technology developments to keep an eye on such as machine vision, emotion gestures/affective computing, NLP, NLG, knowledge graphs, deep learning, reinforcement learning, quantum computing, synthetic data, and more. Hear from pharma industry
use cases as well as technology users and emerging technology solution providers. How can these emerging AI technologies move from correlation to causality analysis?
Track 9 - NEW: AI: Business Value Outcomes:
Accelerate Business Value through AI
Investment and application of AI in the pharmaceutical industry is rapidly gaining momentum. The AI: Business Value Outcomes track will bring together CEOs, CDOs, CIOs, CTOs and Global AI, IT, and informatics experts from leading pharmaceutical
and technology companies to give strategic talks from a business perspective—allowing you to assess the value of investing in and implementing AI.
Track 10: Data Visualization Tools:
Explore Complex Data through Visualization Platforms to Gain Insights
With a sharp increase in the volume and complexity of big datasets for research and drug discovery labs, data visualization is needed to clearly express the complex patterns. It is more important than ever to develop data visualization and exploration
tools alongside the rest of the analytics, as opposed to later in the game. The Data Visualization Tools track will discuss new visualization tools, dashboards, and platforms, as well as ways that these tools can help solve problems,
validate, and interpret the data science and artificial intelligence insights. How can your visualization methods be validated as to their ability to provide full analytic results that may extend well beyond 2 or 3 dimensions?
Track 11: Bioinformatics:
Turn Big Data into Smart Data with Computational Resources and Tools
The Bioinformatics track assembles thought leaders who will present case studies using computational resources and tools that discuss the problems and challenges of taking data from multiple -omics sources and aligning it with clinical
action. Turning big data into smart data can lead to real-time assistance in disease prevention, prognosis, diagnostics, and therapeutics. With the ever-increasing volume of information generated for curing or treating diseases and cancers, bioinformatics
technologies, tools, and techniques play a critical role in turning data into actionable knowledge to meet unstated and unmet medical needs. Case studies will be presented on addressing these problems and challenges including making the jump
from prototyping to production code, defining what a "validated" informatics pipeline means, how to balance agility needs with requirements to be consistent/compliant, pipeline and workflow frameworks, containerization for reproducibility, and more. How do your approaches deal with inconsistencies in definitions and meta-data across the multiple datasets that form the basis of big data?
Track 12: Pharmaceutical R&D Informatics:
Drive Precision Medicine through the Digitalization of Pharma R&D
Pharmaceutical R&D departments are at a crossroads – we have more technology and data than ever before, priming us for novel discoveries, yet there are still many challenges informatics strategies must address. The digitalization of the lab
is at the forefront, and it necessitates quality data as well as knowledge management strategies, especially in the search for effective, real-world uses of AI and machine learning. We must also address how these new technologies are transforming
day-to-day workflow and knowledge exchange, and what change management, investment, and regulatory strategies must be employed to make them successful. The Pharmaceutical R&D Informatics track will explore real-world projects
related to digitalization, FAIR data, knowledge management systems, and artificial intelligence development and implementation, and how such initiatives are driving precision medicine.
Track 13: Genome Informatics:
Understand the Biology of Genomes through Computational Approaches for Accurate Disease Diagnosis
Biological information from genome sequences is derived by the application of computer and statistical techniques. Additionally, protein sequence and structure can be predicted by analyzing DNA sequence information. Tremendous advancements have been made
to broaden sequencing applications from research to the clinic, especially as genomics becomes more integrated with precision medicine and AI initiatives. In spite of this, enormous problems still exist with data integration and analysis pipelines
and sensitivity to accuracy in diagnosis and/or disease stratification. What is the role of computer science in modeling cells, analyzing and mapping data networks, and incorporating clinical and pathological data to determine how diseases arise from
mutations? How do Bio-IT approaches help relate SNPs, expression, and disease? What is the right format for reads and variants? Do you really need to keep the BCL/FASTQ/BAM files forever? What is the role of AI in data curation techniques, text mining
approaches, and statistical analytics to discover disease or drug response pathways to identify personalized and focused treatments and cures? Presentations in the Genome Informatics track will explore these problems and challenges
and how organizations and research teams are implementing computational approaches to understand the biology of genomes. Complex diseases, e.g. Parkinson’s, are thought to be only 30% based on genomics, how do your methods enable the further analysis, diagnosis, etc by including lifestyle and environment?
Track 14: Clinical Research and Translational Informatics:
Accelerate Clinical Outcomes by Transforming Raw Data into Actionable Insights
Advancing clinical trials and translational research requires transforming biological insights and raw research data into clean, actionable data for integration, visualization, and analysis. The Clinical Research and Translational Informatics track explores new and innovative tools and techniques—including big data analytics, machine learning, and artificial intelligence—and how they can be leveraged to address specific challenges faced across the drug discovery spectrum to
accelerate the translation of scientific discoveries from the bench to medical care. Gain practical recommendations and real-world insights from case studies across pharma and academia. Actionable insights require making the results of complex analysis readily convertible into the common workflows of the clinician and researcher. How do you approach this problem?
Track 15: Cancer Informatics:
Apply Computational Biology to Advance Cancer Research and Care
The Cancer Informatics track explores the important technology and informatics trends and challenges of applying computational biology to cancer research and care. Themes that will be covered in expert-led presentations include collaboration
and network models, data access/management/integration strategies, and applications of biological interpretation to aid in research at the bench side or care at the bedside. Most clinical diagnoses involve the use of clinical testing, much of which is not standardized, locally/nationally/internationally. How do your approaches address this reality?
Track 16 - NEW: Open Access and Collaborations:
Accelerate Translational and Clinical Research by Harnessing Collaborative Technologies and Methodologies
The Open Access and Collaborations track presents case studies on collaborative technologies and methodologies used to aggregate and harmonize data from heterogeneous sources to accelerate translational and clinical research. Speakers
will show novel approaches of key drivers, technology innovations, collaboration platforms, open-source frameworks, legal considerations, and other factors that are managing data and empowering transformative changes through translation. Additional
themes that will be covered include emerging security, analytic, semantic capabilities, FAIR data practices and applications, data commons, implications of Europe's Plan S on publishing in the United States, and large collaborative data sets. How do you incorporate the heterogeneity of standards, both in methodologies and in labeling, necessary to guarantee that equivalencing of data fields, clinical parameters, etc are appropriately being managed?
If you would like to submit a proposal to give a presentation at this meeting, please click here
The deadline for priority consideration is October 7, 2019.
All proposals are subject to review by the Scientific Advisory Committee to ensure the overall quality of the conference program. In your proposal submission, please indicate names and/or organizations of all speakers you intend to participate in the final presentation. Additionally, as per Cambridge Healthtech Institute policy, a select number of vendors and consultants who provide products and services will be offered opportunities for podium presentation slots based on a variety of Corporate Sponsorships.
For more details on the conference, please contact:
Cindy Crowninshield, RDN, LDN
Executive Event Director
Cambridge Healthtech Institute
Phone: (+1) 781-247-6258
For partnering and sponsorship information, please contact:
Katelin Fitzgerald (A-K)
Senior Business Development Manager
Cambridge Healthtech Institute
Phone: (+1) 781-972-5458
Joseph Vacca, M.S. (L-Z)
Director, Business Development
Cambridge Healthtech Institute
Phone: (+1) 781-972-5431
For marketing information, please contact:
Director of Product Marketing
Cambridge Healthtech Institute
Phone: (+1) 781-972-5419