Bioinformatics is one of the fastest growing fields in the twenty-first century. Over the last few decades, studies of biology have moved from low-throughput hands-on experiments to computational analyses of the increasingly complex tree of life. Alongside this change are multiple challenges. The first challenge exists in interdisciplinary collaboration. The current interdisciplinary collaboration model is still bounded by individual disciplines and is far from seamless. The second challenge is big data. How to extract the useful data from the haystack of big data? The third challenge is human infrastructure. We need to educate the next generation of scientists as early as possible to solve the interdisciplinary and complex biology problems with computational resources. To address these challenges, we propose a No-Boundary Thinking approach to teach the next generation of scientists. To explain it, we present three No-Boundary Thinking teaching and research models. All of them are embedded into undergraduate computer science curriculums.

The goal of this chapter is to explore and review the role of artificial intelligence (AI) in scientific discovery from data. Specifically, we present AI as a useful tool for advancing a No-Boundary Thinking (NBT) approach to bioinformatics and biomedical informatics. NBT is an agnostic methodology for scientific discovery and education that accesses, integrates, and synthesizes data, information, and knowledge from all disciplines to define important problems, leading to innovative and significant questions that can subsequently be addressed by individuals or collaborative teams with diverse expertise. Given this definition, AI is uniquely poised to advance NBT as it has the potential to employ data science for discovery by using information and knowledge from multiple disciplines. We present three recent AI approaches to data analysis that each contribute to a foundation for an NBT research strategy by either incorporating expert knowledge, automating machine learning, or both. We end with a vision for fully automating the discovery process while embracing NBT.

The rise of interdisciplinary and no-boundary engagement has created a need to train the next generation of No-Boundary Thinking (NBT) scholars and practitioners. So it is essential that students be provided with NBT experiences in the classroom and through group-based research experiences. Our no-boundary community has offered a first generation of classes to provide an environment where students can engage in no-boundary projects and exercises, and reflect upon the nature of this type of thinking and problem solving. The following five classes were first offered in fall 2015 through spring 2018 at four institutions for undergraduate and graduate students. The experience has been enriching for both students and faculty. In all cases the courses have been well received by the students and institutions, and most instructors plan to continue to provide the classes as permanent offerings. We describe the early offerings of each class.

Ideal healthcare should provide prevention and treatment strategies in the context of individual variability. The promise of genomics and big data for understanding the complex disease etiology and development of treatment strategies for translating research findings in a laboratory setting to the bedside requires a paradigm shift in how we conduct biomedical research. The take-home message from the Human Genome Sequencing Project is the need for a bold vision, even in the absence of a clear path. The No-Boundary Thinking (NBT) approach that advocates a scientific dialogue among individuals with varying expertise in a “discipline-free” manner at the problem definition stage is a pragmatic approach to leverage big data for precision medicine. Genomics big data as it pertains to understanding the molecular function of genes and proteins is discussed in this chapter. We also discuss the challenges in the adoption of NBT to genomics research.