Bioinformation Processing

This book shows how mathematics, computer science and science can be usefully and seamlessly intertwined. It begins with a general model of cognitive processes in a network of computational nodes, such as neurons, using a variety of tools from mathematics, computational science and neurobiology. It then moves on to solve the diffusion model from a low-level random walk point of view. It also demonstrates how this idea can be used in a new approach to solving the cable equation, in order to better understand the neural computation approximations. It introduces specialized data for emotional content, which allows a brain model to be built using MatLab tools, and also highlights a simple model of cognitive dysfunction.

This book sets out to define and consolidate the field of bioinformation studies in its transnational and global dimensions, drawing on debates in science and technology studies, anthropology and sociology. It provides situated analyses of bioinformation journeys across domains and spheres of interpretation. As unprecedented amounts of data relating to biological processes and lives are collected, aggregated, traded and exchanged, infrastructural systems and machine learners produce real consequences as they turn indeterminate data into actionable decisions for states, companies, scientific researchers and consumers. Bioinformation accrues multiple values as it transverses multiple registers and domains, and as it is transformed from bodies to becoming a subject of analysis tied to particular social relations, promises, desires and futures. The volume harnesses the anthropological sensibility for situated, fine-grained, ethnographically grounded analysis to develop an interdisciplinary dialogue on the conceptual, political, social and ethical dimensions posed by bioinformation.

Information processing and information flow occur in the course of an organism's development and throughout its lifespan. Organisms do not exist in isolation, but interact with each other constantly within a complex ecosystem. The relationships between organisms, such as those between prey or predator, host and parasite, and between mating partners, are complex and multidimensional. In all cases, there is constant communication and information flow at many levels.This book focuses on information processing by life forms and the use of information technology in understanding them. Readers are first given a comprehensive overview of biocomputing before navigating the complex terrain of natural processing of biological information using physiological and analogous computing models. The remainder of the book deals with ?artificial? processing of biological information as a human endeavor in order to derive new knowledge and gain insight into life forms and their functioning. Specific innovative applications and tools for biological discovery are provided as the link and complement to biocomputing.Since ?artificial? processing of biological information is complementary to natural processing, a better understanding of the former helps us improve the latter. Consequently, readers are exposed to both domains and, when dealing with biological problems of their interest, will be better equipped to grasp relevant ideas.

This book constitutes the papers presented at the Evaluation Track of the 8th China Conference on Health Information Processing, CHIP 2022, held in Hangzhou, China during October 21–23, 2022. The 20 full papers included in this book were carefully reviewed and selected from 20 submissions. They were organized in topical sections as follows: text mining for gene-disease association semantic; medical causal entity and relation extraction; medical decision tree extraction from unstructured text; OCR of electronic medical document; clinical diagnostic coding.

When microelectronic devices replaced vacuum tubes, it marked a revolution in electronics that opened the way to the computer age. We are on the verge of witnessing another equally profound shift. As molecular devices replace semiconductors, we will achieve new levels of performance, functionality and capability that will hugely impact electronics, as well as signal processing and computing. Molecular Electronics, Circuits, and Processing Platforms guides you confidently into this emerging field. Helping you to forge into the molecular frontier, this book examines the various concepts, methods and technologies used to approach and solve a wide variety of problems. The author works from new devices to systems and platforms. He also covers device-level physics, system-level design, analysis, and advanced fabrication technologies. Explore the latest and emerging molecular, biomolecular, and nanoscale processing platforms for building the next generation of circuits, memories and computations. By examining both solved and open issues, this book thoroughly develops the basic theory and shows you how to apply this knowledge toward new developments and practical hardware implementation. Don’t fall behind. Let Molecular Electronics, Circuits, and Processing Platforms take you to the next level of electronics design and applications.

Living beings require constant information processing for survival. In cells, information is being processed and propagated at various levels, from the gene regulatory network to chemical pathways, to the interaction with the environment. How this is achieved and how information is coded is still poorly understood. For example, what a cell interprets as information in the temporal level of an mRNA and what is interpreted as noise remains an open question. Recently, information theoretical methods and other tools, developed in the context of engineering and natural sciences, have been applied to study diverse biological processes. This book covers the latest findings on how information is processed in various biological processes, ranging from information processing and propagation in gene regulatory networks to information processing in natural language. An overview is presented of the state-of-the-art in information processing in biological systems and the opinion of current leaders in this research field on future research directions.

Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.