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?

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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.

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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 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 issues and how organizations and research teams are implementing computational approaches to understand the biology of genomes.

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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?

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The Cancer Informatics track explores the important technology, 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 benchside 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?

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