The Lord Maior of Londons letter to the King at Yorke, Iune, 22 [[electronic resource] ] : In behalfe of the aldermen sheriffes, the master and wardens of each severall company in answer to His Maiesites letter. The Parliaments resolution, concerning the Kings most excellent Maiestie, and the Lords and Commons which have absented themselues from the said Houses, and are now at Yorke attending on his Maiesty. Like wise the grounds and reasons why they are enforceed [sic] to take arms, with the severall reasons to prove that every man is bound to uphold the Parliament against all opposers whatsoever

• Record Nr.: UNISA996393157703316
• Autore: Gurney Richard, Sir, <1577-1647.>
• Pubbl/distr/stampa: [London, : s.n., 1642]
• Descrizione fisica: 1 sheet ([1] p.)
• Altri autori (Persone): Charles, King of England, <1600-1649.>
• Soggetti: Great Britain History Civil War, 1642-1649 Early works to 1800; Great Britain Politics and government 1642-1649 Early works to 1800
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Place and date of publication from Wing.; "Ordered by the Lords and Commons that this be printed and published. Ic. Bro. Cler. par. Hen. Elsing Cler. parl."; Reproduction of the original in the British Library.
• Sommario/riassunto: eebo-0018

Overview of reduction methods and their implementation Into finite-element local-to-global techniques [[electronic resource] /] / Michael A. Minnicino II and David A. Hopkins

• Record Nr.: UNINA9910697110803321
• Autore: Minnicino Michael A
• Pubbl/distr/stampa: Aberdeen Proving Ground, MD : , : Army Research Laboratory, , [2004]
• Descrizione fisica: 1 online resource (iv, 34 pages) : color illustrations
• Collana: ARL-TR ; ; 3340
• Altri autori (Persone): HopkinsDavid A
• Soggetti: Data reduction; Finite element method - Data processing
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Title from PDF title screen (viewed on Aug. 9, 2010).; "September 2004."
• Nota di bibliografia: Includes bibliographical references (page 19).

Machine Learning and Deep Learning in Computational Toxicology / / edited by Huixiao Hong

• Record Nr.: UNINA9910659493003321
• Autore: Hong Huixiao
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
• ISBN: 9783031207303; 9783031207297
• Edizione: [1st ed. 2023.]
• Descrizione fisica: 1 online resource (654 pages)
• Collana: Computational Methods in Engineering & the Sciences, , 2662-4877
• Disciplina: 016.34951249; 615.900285631
• Soggetti: Toxicology; Machine learning; Artificial intelligence; Machine Learning; Artificial Intelligence
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Machine Learning and Deep Learning Promotes Predictive Toxicology for Risk Assessment of Chemicals -- Multi-Modal Deep Learning Approaches for Molecular Toxicity prediction -- Emerging Machine Learning Techniques in Predicting Adverse Drug Reactions -- Drug Effect Deep Learner Based on Graphical Convolutional Network -- AOP Based Machine Learning for Toxicity Prediction -- Graph Kernel Learning for Predictive Toxicity Models -- Optimize and Strengthen Machine Learning Models Based on in vitro Assays with Mecha-nistic Knowledge and Real-World Data -- Multitask Learning for Quantitative Structure-Activity Relationships: A Tutorial -- Isalos Predictive Analytics Platform: Cheminformatics, Nanoinformatics and Data Mining Applications -- ED Profiler: Machine Learning Tool for Screening Potential Endocrine Disrupting Chemicals -- Quantitative Target-specific Toxicity Prediction Modeling (QTTPM): Coupling Machine Learning with Dynamic Protein-Ligand Interaction Descriptors (dyPLIDs) to Predict Androgen Receptor-mediated Toxicity -- Mold2 Descriptors ; Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals -- Applicability Domain Characterization for Machine Learning QSAR Models -- Controlling for Confounding in Complex Survey Machine Learning Models to Assess Drug Safety and Risk. .
• Sommario/riassunto: This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. One of the major features is the case studies of applications of machine learning and deep learning in toxicological research that serve as examples for readers to learn how to apply machine learning and deep learning techniques in predictive toxicology. This book is expected to provide a reference for practical applications of machine learning anddeep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students. The main benefit for the readers is understanding the widely used machine learning and deep learning techniques and gaining practical procedures for applying machine learning and deep learning in predictive toxicology. .