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It is not simple to consider the world from only one side, but analyzing all sides can cloud comprehension without reaching deep insight found at the core. In a word as a whole, there is potential for telling the whole world in one word, i.e., data, leading to interpretations as phenomena and paradigms at the core of this review. The tug of war between the two sides explains that data represent the world, or vice versa, and present a fundamental view that systems or subsystems frame the world, even though they are encoded and composed of culture, rules, or approaches such as the threshold of democracy. When the COVID-19 pandemic posed a threat, human efforts contributed to finding potentially answers to questions presented by the world: what, who, where, when, why, and how (5 wh); a calling in the form of a challenge, where facts show something. All these questions resulted in research, education, and service activities, with their respective data frameworks producing results. This paper aims to reveal the meaning of the outcomes through an observation from an outside perspective. Therefore, like COVID-19 and its vaccines, the assertion of convexity and concave contradictions in the treatment of data leads to a mutually conjugate treatment of data. In this regard, statistics and artificial intelligence play separate and complementary roles. [ABSTRACT FROM AUTHOR]

Despite efforts to integrate research across different subdisciplines of biology, the scale of integration remains limited. We hypothesize that future generations of Artificial Intelligence (AI) technologies specifically adapted for biological sciences will help enable the reintegration of biology. AI technologies will allow us not only to collect, connect, and analyze data at unprecedented scales, but also to build comprehensive predictive models that span various subdisciplines. They will make possible both targeted (testing specific hypotheses) and untargeted discoveries. AI for biology will be the cross-cutting technology that will enhance our ability to do biological research at every scale. We expect AI to revolutionize biology in the 21st century much like statistics transformed biology in the 20th century. The difficulties, however, are many, including data curation and assembly, development of new science in the form of theories that connect the subdisciplines, and new predictive and interpretable AI models that are more suited to biology than existing machine learning and AI techniques. Development efforts will require strong collaborations between biological and computational scientists. This white paper provides a vision for AI for Biology and highlights some challenges. [ABSTRACT FROM AUTHOR]