Download Free Moving Towards Interpretable Mechanisms In Human Systems Biology Book in PDF and EPUB Free Download. You can read online Moving Towards Interpretable Mechanisms In Human Systems Biology and write the review.

A detailed understanding of biomolecular mechanisms enables predictive modeling in biological systems. In the late 1990's, whole-genome sequencing and the development of various high-throughput technologies led to the emergence of systems biology, primarily in simple model organisms such as bacteria and yeast. Mechanisms between biological components and processes were cataloged and placed in mathematical frameworks to explain the role of genotype and environmental factors on phenotypes. Some modeling formalisms, such as constraints-based modeling, have been shown to accurately recapitulate biological findings, and provided new insights for applications ranging from metabolic engineering to evolutionary landscapes. Recently, systems biology of human cells, with the same aim of characterizing mechanisms, has been employed to study drug off-target effects, host-pathogen interactions, cancer metabolism, and multicellular interactions between brain cell types. However, mechanism-based systems biology of human cells is still in its infancy and has not achieved the level of adoption as systems biology in unicellular organisms. Therefore, a broad, mechanism-centric approach to human systems biology is expounded in this dissertation, and was used to address open problems concerning blood platelets and cancer cells to make inroads in the study of disease, longevity, and phenotypic diversity with regard to human cells. Mechanisms were cataloged into a computable database for blood platelet metabolism. This systems-level assessment of the platelet was used to study the effects of aspirin resistance and delineate pathway utilization during platelet storage. Computational methods were developed to handle the scale of information in these systems biology applications with the motivation of reporting digestible results. To this end, BioNetView was developed as a clustering and visualization tool to utilize structural information to build interpretable, data-influenced pathway maps. Discovery of new mechanisms for future systems biology applications was also explored. Representing an initial foray towards large-scale mechanistic discovery in human cells, a novel bioinformatics pipeline was developed and deployed for processing and scoring genetic interactions in cancer cell lines via gene knockout screens utilizing the unprecedented precision of CRISPR/Cas9 genome editing. Therefore, in an effort to contextualize and understand mechanisms, several aspects are presented, geared towards a comprehensive, interpretable systems-level perspective of human biology.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
An overview of the current systems biology-based knowledge and the experimental approaches for deciphering the biological basis of cancer.
The Exposome: A Primer is the first book dedicated to exposomics, detailing the purpose and scope of this emerging field of study, its practical applications and how it complements a broad range of disciplines. Genetic causes account for up to a third of all complex diseases. (As genomic approaches improve, this is likely to rise.) Environmental factors also influence human disease but, unlike with genetics, there is no standard or systematic way to measure the influence of environmental exposures. The exposome is an emerging concept that hopes to address this, measuring the effects of life-long environmental exposures on health and how these exposures can influence disease. This systematic introduction considers topics of managing and integrating exposome data (including maps, models, computation, and systems biology), "-omics"-based technologies, and more. Both students and scientists in disciplines including toxicology, environmental health, epidemiology, and public health will benefit from this rigorous yet readable overview.
Biology, medicine and biochemistry have become data-centric fields for which Deep Learning methods are delivering groundbreaking results. Addressing high impact challenges, Deep Learning in Biology and Medicine provides an accessible and organic collection of Deep Learning essays on bioinformatics and medicine. It caters for a wide readership, ranging from machine learning practitioners and data scientists seeking methodological knowledge to address biomedical applications, to life science specialists in search of a gentle reference for advanced data analytics.With contributions from internationally renowned experts, the book covers foundational methodologies in a wide spectrum of life sciences applications, including electronic health record processing, diagnostic imaging, text processing, as well as omics-data processing. This survey of consolidated problems is complemented by a selection of advanced applications, including cheminformatics and biomedical interaction network analysis. A modern and mindful approach to the use of data-driven methodologies in the life sciences also requires careful consideration of the associated societal, ethical, legal and transparency challenges, which are covered in the concluding chapters of this book.
Scientific Frontiers in Developmental Toxicology and Risk Assessment reviews advances made during the last 10-15 years in fields such as developmental biology, molecular biology, and genetics. It describes a novel approach for how these advances might be used in combination with existing methodologies to further the understanding of mechanisms of developmental toxicity, to improve the assessment of chemicals for their ability to cause developmental toxicity, and to improve risk assessment for developmental defects. For example, based on the recent advances, even the smallest, simplest laboratory animals such as the fruit fly, roundworm, and zebrafish might be able to serve as developmental toxicological models for human biological systems. Use of such organisms might allow for rapid and inexpensive testing of large numbers of chemicals for their potential to cause developmental toxicity; presently, there are little or no developmental toxicity data available for the majority of natural and manufactured chemicals in use. This new approach to developmental toxicology and risk assessment will require simultaneous research on several fronts by experts from multiple scientific disciplines, including developmental toxicologists, developmental biologists, geneticists, epidemiologists, and biostatisticians.
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
A detailed overview of current research in kernel methods and their application to computational biology.
This thorough volume explores predicting one-dimensional functional properties, functional sites in particular, from protein sequences, an area which is getting more and more attention. Beginning with secondary structure prediction based on sequence only, the book continues by exploring secondary structure prediction based on evolution information, prediction of solvent accessible surface areas and backbone torsion angles, model building, global structural properties, functional properties, as well as visualizing interior and protruding regions in proteins. Written for the highly successful Methods in Molecular Biology series, the chapters include the kind of detail and implementation advice to ensure success in the laboratory. Practical and authoritative, Prediction of Protein Secondary Structure serves as a vital guide to numerous state-of-the-art techniques that are useful for computational and experimental biologists.
The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.