Due to its capability of handling very complex problems and its high flexibility in adapting to different algorithms, the kernel machine plays a crucial role in machine learning. Bio-Kernel Machines and Applications will introduce a new type of kernel machine for the exploration and modeling between the genotypic inherent structures of short protein sequences or nucleic sequences and the phenotypic biological properties or functions of proteins or nucleotides. The book seeks to establish the fundamentals of the bio-kernel machines by presenting the basic principle and theory of the kernel machine and the various formats of kernel machines, such as string kernel machines adapted for biological applications. The book will also introduce several biological applications of the mutation matrices, demonstrating how mutation matrices can enhance the efficiency and biological relevance of machine learning models applied in specific biological problems. Through analyzing current applications of bio-kernel machines, readers will delve into the advantages of the bio-kernel machines and explore how bio-kernel machines can be further enhanced to tackle a wide spectrum of biological challenges and pave the way for future advancements.