The field of biology has entered a new era-one defined not solely by experimental ingenuity, but increasingly by the intelligent use of data. As biological datasets grow in size and complexity, traditional analytical methods are no longer sufficient to extract meaningful patterns, make accurate predictions, or generate novel hypotheses. This book,
AI and Generative AI for Biologists: From Data to Discovery with Artificial Intelligence, was conceived to bridge the widening gap between cutting-edge computational tools and the life sciences, offering biologists a comprehensive and data-driven guide to harnessing the power of artificial intelligence (AI) in their work.
Over the past decade, AI has transitioned from a niche discipline to a cornerstone of innovation across fields, including biology. From automating image analysis in microscopy to predicting protein structures, AI tools-especially generative models-are opening doors to possibilities once thought unattainable. Yet, for many in the biological sciences, the path to using these technologies remains unclear, burdened by technical jargon and a lack of discipline-specific resources. This book aims to demystify AI and generative AI by presenting concepts, methods, and applications in a way that is accessible, relevant, and actionable for biologists at all levels.
This text is not only a guide to understanding AI, but also a call to action. Whether you are a molecular biologist, ecologist, or biomedical researcher, the integration of AI into your scientific process is no longer optional-it is essential. Through real-world case examples, and a focus on biological relevance, we invite readers to reimagine their research pipelines. Our goal is to equip biologists with the conceptual clarity and practical tools needed to lead discoveries in the age of intelligent machines.
