Machine Learning and Mathematical Models for Single-Cell Data Analysis
Author | : Le Ou-Yang |
Publisher | : Frontiers Media SA |
Total Pages | : 118 |
Release | : 2022-11-29 |
Genre | : Science |
ISBN | : 2832501842 |
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Author | : Le Ou-Yang |
Publisher | : Frontiers Media SA |
Total Pages | : 118 |
Release | : 2022-11-29 |
Genre | : Science |
ISBN | : 2832501842 |
Author | : William L. William L. Hamilton |
Publisher | : Springer Nature |
Total Pages | : 141 |
Release | : 2022-06-01 |
Genre | : Computers |
ISBN | : 3031015886 |
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Author | : Alessandro Minelli |
Publisher | : Oxford University Press |
Total Pages | : 299 |
Release | : 2014 |
Genre | : Science |
ISBN | : 0199671427 |
This volume explores the foundations of ontogeny by asking how the development of living things should be understood. It explores key concepts of developmental biology, asks whether general principles of development can be discovered, and what the role of models and theories is in developmental biology.
Author | : Johan H. J. Leveau |
Publisher | : Frontiers Media SA |
Total Pages | : 187 |
Release | : 2019-02-19 |
Genre | : |
ISBN | : 2889457494 |
Recent technological advances in single-cell microbiology, using flow cytometry, microfluidics, x-ray fluorescence microprobes, and single-cell -omics, allow for the observation of individuals within populations. Simultaneously, individual-based models (or more generally agent-based models) allow for individual microbes to be simulated. Bridging these techniques forms the foundation of individual-based ecology of microbes (µIBE). µIBE has elucidated genetic and phenotypic heterogeneity that has important consequences for a number of human interests, including antibiotic or biocide resistance, the productivity and stability of industrial fermentations, the efficacy of food preservatives, and the potential of pathogens to cause disease. Individual-based models can help us to understand how these sets of traits of individual microbes influence the above. This eBook compiles all publications from a recent Research Topic in Frontiers in Microbiology. It features recent research where individual observational and/or modelling techniques are applied to gain unique insights into the ecology of microorganisms. The Research Topic “The Individual Microbe: Single-Cell Analysis and Agent-Based Modelling” arose from the 2016 @ASM conference of the same name hosted by the American Society for Microbiology at its headquarters in Washington, D.C. We are grateful to ASM for funding and hosting this conference.
Author | : Pawan Raghav |
Publisher | : Elsevier |
Total Pages | : 568 |
Release | : 2024-01-12 |
Genre | : Science |
ISBN | : 0443132216 |
Computational Biology for Stem Cell Research is an invaluable guide for researchers as they explore HSCs and MSCs in computational biology. With the growing advancement of technology in the field of biomedical sciences, computational approaches have reduced the financial and experimental burden of the experimental process. In the shortest span, it has established itself as an integral component of any biological research activity. HSC informatics (in silico) techniques such as machine learning, genome network analysis, data mining, complex genome structures, docking, system biology, mathematical modeling, programming (R, Python, Perl, etc.) help to analyze, visualize, network constructions, and protein-ligand or protein-protein interactions. This book is aimed at beginners with an exact correlation between the biomedical sciences and in silico computational methods for HSCs transplantation and translational research and provides insights into methods targeting HSCs properties like proliferation, self-renewal, differentiation, and apoptosis. - Modeling Stem Cell Behavior: Explore stem cell behavior through animal models, bridging laboratory studies to real-world clinical allogeneic HSC transplantation (HSCT) scenarios. - Bioinformatics-Driven Translational Research: Navigate a path from bench to bedside with cutting-edge bioinformatics approaches, translating computational insights into tangible advancements in stem cell research and medical applications. - Interdisciplinary Resource: Discover a single comprehensive resource catering to biomedical sciences, life sciences, and chemistry fields, offering essential insights into computational tools vital for modern research.
Author | : Xu-jie Zhou |
Publisher | : Frontiers Media SA |
Total Pages | : 227 |
Release | : 2022-08-16 |
Genre | : Medical |
ISBN | : 2889765830 |
Topic Editor Dr. MacLeod is employed by Janssen. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Author | : Robert Lanza |
Publisher | : Academic Press |
Total Pages | : 1679 |
Release | : 2020-03-26 |
Genre | : Science |
ISBN | : 0128214015 |
Now in its fifth edition, Principles of Tissue Engineering has been the definite resource in the field of tissue engineering for more than a decade. The fifth edition provides an update on this rapidly progressing field, combining the prerequisites for a general understanding of tissue growth and development, the tools and theoretical information needed to design tissues and organs, as well as a presentation by the world's experts of what is currently known about each specific organ system. As in previous editions, this book creates a comprehensive work that strikes a balance among the diversity of subjects that are related to tissue engineering, including biology, chemistry, material science, and engineering, among others, while also emphasizing those research areas that are likely to be of clinical value in the future. This edition includes greatly expanded focus on stem cells, including induced pluripotent stem (iPS) cells, stem cell niches, and blood components from stem cells. This research has already produced applications in disease modeling, toxicity testing, drug development, and clinical therapies. This up-to-date coverage of stem cell biology and the application of tissue-engineering techniques for food production – is complemented by a series of new and updated chapters on recent clinical experience in applying tissue engineering, as well as a new section on the emerging technologies in the field. - Organized into twenty-three parts, covering the basics of tissue growth and development, approaches to tissue and organ design, and a summary of current knowledge by organ system - Introduces a new section and chapters on emerging technologies in the field - Full-color presentation throughout
Author | : Dongya Jia |
Publisher | : Frontiers Media SA |
Total Pages | : 153 |
Release | : 2023-06-21 |
Genre | : Medical |
ISBN | : 2832526969 |
Author | : Zhike Zi |
Publisher | : Frontiers Media SA |
Total Pages | : 224 |
Release | : 2022-02-17 |
Genre | : Science |
ISBN | : 2889742814 |
Topic Editor Prof. Xing is in collaboration with ATCC (https://www.atcc.org/) on testing some of their cell lines in research. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Author | : Khalid Raza |
Publisher | : Springer Nature |
Total Pages | : 474 |
Release | : 2022-03-01 |
Genre | : Technology & Engineering |
ISBN | : 9811692211 |
This book encapsulates recent applications of CI methods in the field of computational oncology, especially cancer diagnosis, prognosis, and its optimized therapeutics. The cancer has been known as a heterogeneous disease categorized in several different subtypes. According to WHO’s recent report, cancer is a leading cause of death worldwide, accounting for over 10 million deaths in the year 2020. Therefore, its early diagnosis, prognosis, and classification to a subtype have become necessary as it facilitates the subsequent clinical management and therapeutics plan. Computational intelligence (CI) methods, including artificial neural networks (ANNs), fuzzy logic, evolutionary computations, various machine learning and deep learning, and nature-inspired algorithms, have been widely utilized in various aspects of oncology research, viz. diagnosis, prognosis, therapeutics, and optimized clinical management. Appreciable progress has been made toward the understanding the hallmarks of cancer development, progression, and its effective therapeutics. However, notwithstanding the extrinsic and intrinsic factors which lead to drastic increment in incidence cases, the detection, diagnosis, prognosis, and therapeutics remain an apex challenge for the medical fraternity. With the advent in CI-based approaches, including nature-inspired techniques, and availability of clinical data from various high-throughput experiments, medical consultants, researchers, and oncologists have seen a hope to devise and employ CI in various aspects of oncology. The main aim of the book is to occupy state-of-the-art applications of CI methods which have been derived from core computer sciences to back medical oncology. This edited book covers artificial neural networks, fuzzy logic and fuzzy inference systems, evolutionary algorithms, various nature-inspired algorithms, and hybrid intelligent systems which are widely appreciated for the diagnosis, prognosis, and optimization of therapeutics of various cancers. Besides, this book also covers multi-omics exploration, gene expression analysis, gene signature identification of cancers, genomic characterization of tumors, anti-cancer drug design and discovery, drug response prediction by means of CI, and applications of IoT, IoMT, and blockchain technology in cancer research.