This chapter explores the ethical status of a presumed artificially intelligent machine (AI) and the grounds for such status from the perspective of ethical philosophy. The chapter explicitly repudiates anthropocentric claims for an AI or assumptions about its capabilities or motivations for its existence or actions. It also avoids any speculation about whether or when AI capabilities may come about. Instead, it provides a framework for building the vocabulary and concepts for analysis of important ethical issues for AI from first principles. The philosophical foundations of ethics considered here are nihilism, divine command, consequentialism, deontology, and virtues ethics.

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.

Pharmacogenomics is the study of genetic factors that influence drug response. Pharmacogenomics combines pharmacology and genomics to identify genetic predictors of variability in drug response that can be used to maximize drug efficacy while minimizing drug toxicity in order to tailor drug therapy for patients, thus improving patient care and reducing healthcare costs. In this chapter we review the field of pharmacogenomics in its current state and clinical practice. Recent research, methods, and resources for pharmacogenomics are reviewed in detail. We discuss the advantages and challenges in pharmacogenomic studies. We elaborate on the barriers to clinical translation of pharmacogenetic discoveries and the efforts of various institutions and consortia to mitigate these barriers. We also discuss applications and clinical translation of pharmacogenomic research moving forward, along with social, ethical, and economic issues that require attention. We conclude by previewing the use of big data, multi-omics data, advanced computing technology, and statistical methods by scientists across disciplinary boundaries along with the efforts of government organizations, clinicians, and patients that could lead to successful and clinically translatable pharmacogenomic discoveries, ushering in an era of precision medicine.

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.

One fundamental project of biology is to determine which groups of organisms are “the same” and which are different, for taxonomic purposes but more fundamentally so that one can make general, meaningful claims about specific groups. Traditional bifurcating taxonomies have been and remain useful. However, what was designed to name unchanging “natural kinds” of relatively large organisms with distinct morphologies is not adequate for grouping and dividing very small organisms, reticulated histories, endosymbiosis, horizontal gene transfer, asexual reproduction, or ecosystems. Biological explanations need to be flexible enough to account for hierarchically embedded processes and structures at vastly different scales, from the molecular to the global. Modern biology has moved beyond naming things, and biological explanations now require more sophisticated ontologies.

This is a reflection on the international computing community’s efforts to define itself as a discipline and inject computing into the core education of everyone. Their aim is to prepare us for the technology-based society in which we work and live. Scholars who aim to explore the nature of No-Boundary Thinking can learn from the self-reflection of the computing discipline, and No-Boundary Thinking might be an essential solution for the effective integration of computing into the core body of knowledge needed by every educated individual. These connections might also assuage the recent frictions between STEM and liberal arts scholar educators.

What is No-Boundary Thinking (NBT)? Is it a philosophy term or a science term? Why do we need it? Since 2013, the NBT national network has had many discussions and today wants to have a book to include some of the NBT group members’ thoughts. Some may affect NBT, some may not. Still, we would like to put it all together.