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Artificial Intelligence And Molecular Biology

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Artificial Intelligence And Molecular Biology

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Author : Lawrence Hunter
language : en
Publisher:
Release Date : 1993

Artificial Intelligence And Molecular Biology written by Lawrence Hunter and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Computers categories.

These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. The enormous amount of data generated by the Human Genome Project and other large-scale biological research has created a rich and challenging domain for research in artificial intelligence. These original contributions provide a current sampling of AI approaches to problems of biological significance; they are the first to treat the computational needs of the biology community hand-in-hand with appropriate advances in artificial intelligence. Focusing on novel technologies and approaches, rather than on proven applications, they cover genetic sequence analysis, protein structure representation and prediction, automated data analysis aids, and simulation of biological systems. A brief introductory primer on molecular biology and Al gives computer scientists sufficient background to understand much of the biology discussed in the book. Lawrence Hunter is Director of the Machine Learning Project at the National Library of Medicine, National Institutes of Health.

Artificial Intelligence For Molecular Biology

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Author : Muhammad Nabeel Asim
language : en
Publisher: Springer
Release Date : 2025-07-01

Artificial Intelligence For Molecular Biology written by Muhammad Nabeel Asim and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-01 with Computers categories.

Molecular biology is at the forefront of scientific discovery, unraveling the intricacies of life at the most fundamental level. As biological systems become increasingly complex and data-rich, artificial intelligence (AI) has emerged as a pivotal tool for unlocking new insights and enhancing our understanding of these systems. This first volume focuses on the core principles of molecular biology while introducing AI-driven approaches to genomic and proteomic sequence analysis. It serves as a foundation for integrating computational methodologies into the study of biological systems. The chapters in this volume are structured to provide a comprehensive overview of the essential concepts, tools, and methodologies in molecular biology, enriched by the latest advancements in AI: Fundamentals of Molecular Biology: This chapter delves into the foundational elements of molecular biology, exploring the central dogma, gene expression regulation, cellular organization, and the evolution of genome studies. It also highlights the role of computational biology in complementing traditional experimental techniques. DNA, RNA, & Protein Structures: Understanding the structural intricacies of DNA, RNA, and proteins is crucial for comprehending their functions. This chapter outlines their fundamental properties and sets the stage for discussing AI-driven sequence analysis. Exploration of AI-Driven Genomic and Proteomic Sequence Analysis Landscape: This section provides an in-depth look at how AI is reshaping the field of sequence analysis. Topics include representation learning, feature engineering, predictive modeling, and an evaluation of performance metrics for AI-driven pipelines. Insights of Biological Databases: Biological data is the backbone of molecular biology research. This chapter discusses the structure, organization, and utilization of key databases, emphasizing data formats, redundancy issues, and retrieval systems. DNA & RNA Sequence Representation Learning Methods: Representing nucleotide sequences in ways that AI models can process effectively is a critical challenge. This chapter explores various encoding methods, from nucleotide distributions to Fourier transformations, providing a robust toolkit for researchers. Protein Sequence Representation Learning Methods: Similar to nucleic acid sequences, encoding protein sequences requires sophisticated techniques. This section details diverse methodologies, including physicochemical properties, z-scales, and context-aware encodings. CRISPR System and AI Applications: CRISPR technology has revolutionized genetic editing, and AI is accelerating its potential. This chapter examines AI-driven approaches to CRISPR-related tasks, from predictive modeling to dataset development, emphasizing the synergy between these transformative technologies. Through this volume, readers will gain a solid understanding of molecular biology and its convergence with AI. The interdisciplinary approach ensures that the biological complexities are complemented by computational rigor, laying the groundwork for the second volume, which delves deeper into advanced AI applications in molecular biology.

Artificial Intelligence For Molecular Biology

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Author : Muhammad Nabeel Asim
language : en
Publisher: Springer
Release Date : 2025-07-01

The integration of artificial intelligence (AI) into molecular biology has brought about a paradigm shift, enabling researchers to tackle some of the most challenging problems in life sciences. This second volume builds upon the foundational principles explored in Volume I, delving into advanced AI methodologies and their applications in understanding biological sequences at a granular level. From word embeddings to language models, this volume examines the state-of-the-art techniques driving progress in molecular biology. The chapters in this volume are structured to provide an in-depth exploration of AI methods and their transformative impact on DNA, RNA, protein, and peptide analysis: Word Embedding Methods: This chapter explores the evolution of word embedding techniques, including foundational models like Word2Vec, FastText, and GloVe, as well as advanced graph-based embeddings such as DeepWalk, Node2Vec, and Struc2Vec. These embeddings have revolutionized sequence representation, providing powerful tools for analyzing biological data. Large Language Models: Language models have reshaped the landscape of computational biology. This chapter examines models like ULMFiT, BERT, and cutting-edge tools like AlphaFold and RNAFormer, which have set new benchmarks in structure prediction and sequence analysis. AI-Driven Insights into DNA Sequence Analysis Landscape: AI has unlocked new possibilities in DNA analysis. This chapter reviews methodologies, datasets, and predictive pipelines, offering insights into the performance and distribution of research across various benchmarks. AI-Driven Insights into RNA Sequence Analysis Landscape: RNA, with its unique roles and complexities, benefits significantly from AI approaches. This chapter investigates datasets, predictive pipelines, and performance metrics specific to RNA analysis. AI-Driven Insights into Protein Sequence Analysis Landscape: Proteins, central to numerous biological processes, are analyzed using AI-driven techniques. This chapter discusses embedding-based and language model-based methods, as well as the resources and benchmarks available for protein analysis. AI-Driven Revolution in Peptide Classification Landscape: Peptides, due to their diverse biological roles, pose unique challenges. This chapter provides a thorough examination of peptide classification, exploring AI methodologies, datasets, evaluation strategies, and the state-of-the-art performance of predictive models. Volume II provides a detailed narrative of how advanced AI methodologies are transforming the study of molecular biology. Each chapter bridges the gap between theoretical advancements and practical applications, equipping researchers and practitioners with the knowledge needed to drive innovation in this interdisciplinary field.

Artificial Intelligence And Molecular Biology

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Author :
language : en
Publisher:
Release Date : 1990

Artificial Intelligence And Molecular Biology written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Artificial intelligence categories.

Artificial Neural Networks

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Author : David J. Livingstone
language : en
Publisher: Humana Press
Release Date : 2011-10-09

Artificial Neural Networks written by David J. Livingstone and has been published by Humana Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-09 with Computers categories.

In this book, international experts report the history of the application of ANN to chemical and biological problems, provide a guide to network architectures, training and the extraction of rules from trained networks, and cover many cutting-edge examples of the application of ANN to chemistry and biology. Methods involving the mapping and interpretation of Infra Red spectra and modelling environmental toxicology are included. This book is an excellent guide to this exciting field.

Deep Learning In Biology And Medicine

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Author : Davide Bacciu
language : en
Publisher: World Scientific
Release Date : 2022-01-17

Deep Learning In Biology And Medicine written by Davide Bacciu and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-17 with Computers categories.

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.

Artificial Intelligence And Molecular Biology

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Author :
language : en
Publisher:
Release Date : 1990*

Introduction To Molecular Biology

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Author : Oksana Ableitner
language : en
Publisher: Springer Nature
Release Date : 2022-01-07

Introduction To Molecular Biology written by Oksana Ableitner and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-07 with Science categories.

Oksana Ableitner offers a practical, clearly structured and easy to understand introduction to complicated definitions and structures in chemistry and molecular biology for work in the molecular biology laboratory. The author is guided by her experience in working with students and uses many illustrations to visualize abstract knowledge. An understanding of this matter is an essential basis for successful work with DNA and RNA in order to ensure high quality results. For responsible activities in application - such as genetic research or the determination of various pathogens - it is essential to be confident in dealing with the basics of these sensitive, fast and specific analytical methods. This Springer essential is a translation of the original German 2nd edition essentials, Einführung in die Molekularbiologie by Oksana Ableitner, published by Springer Fachmedien Wiesbaden GmbH, part of Springer Nature in 2018. The translation was done with the help of artificial intelligence (machine translation by the serviceDeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.

Machine Learning In Chemistry

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Author : Hugh M. Cartwright
language : en
Publisher: Royal Society of Chemistry
Release Date : 2020-07-15

Machine Learning In Chemistry written by Hugh M. Cartwright and has been published by Royal Society of Chemistry this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-15 with Science categories.

Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field.

Artificial Intelligence And Molecular Biology

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