標題: Titlebook: Machine Learning and Deep Learning in Computational Toxicology; Huixiao Hong Book 2023 This is a U.S. government work and not under copyri [打印本頁] 作者: 適婚女孩 時間: 2025-3-21 17:57
書目名稱Machine Learning and Deep Learning in Computational Toxicology影響因子(影響力)
書目名稱Machine Learning and Deep Learning in Computational Toxicology影響因子(影響力)學科排名
書目名稱Machine Learning and Deep Learning in Computational Toxicology網(wǎng)絡公開度
書目名稱Machine Learning and Deep Learning in Computational Toxicology網(wǎng)絡公開度學科排名
書目名稱Machine Learning and Deep Learning in Computational Toxicology被引頻次
書目名稱Machine Learning and Deep Learning in Computational Toxicology被引頻次學科排名
書目名稱Machine Learning and Deep Learning in Computational Toxicology年度引用
書目名稱Machine Learning and Deep Learning in Computational Toxicology年度引用學科排名
書目名稱Machine Learning and Deep Learning in Computational Toxicology讀者反饋
書目名稱Machine Learning and Deep Learning in Computational Toxicology讀者反饋學科排名
作者: follicle 時間: 2025-3-21 23:40
Huixiao HongCovers comprehensive view of the machine learning and deep learning algorithms, methods, and software tools.Provides many practical applications of machine learning and deep learning techniques in pre作者: 名字的誤用 時間: 2025-3-22 01:52 作者: Indent 時間: 2025-3-22 04:53
978-3-031-20732-7This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright pro作者: debase 時間: 2025-3-22 09:56 作者: LARK 時間: 2025-3-22 13:54
Machine Learning and Deep Learning Promote Computational Toxicology for Risk Assessment of Chemicalical reasoning from the human eye and linear experiments to artificial intelligence will improve computational toxicology for risk assessment by unearthing novel discoveries through making unexpected connections across data types, datasets, and toxicology disciplines.作者: 小淡水魚 時間: 2025-3-22 19:56 作者: 整理 時間: 2025-3-22 22:51
2662-4869 ions of machine learning and deep learning techniques in preThis 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 stat作者: Cholagogue 時間: 2025-3-23 04:21
Assessment of the Xenobiotics Toxicity Taking into Account Their Metabolismal effects. Herein, we propose the concept of integral toxicity that concomitantly reflects the overall biological activity of a pharmaceutical substance and its metabolites. The current possibilities and limitations of the multifaceted computational assessment of xenobiotics toxicity are discussed.作者: multiply 時間: 2025-3-23 09:10
Drug Effect Deep Learner Based on Graphical Convolutional Networkation of the drug. We found that DDEP can predict drug efficacy with accuracy far better than that achieved by simple drug/target classification, and the vector representations grasp well the comprehensive states of a cell.作者: 非實體 時間: 2025-3-23 12:32 作者: elucidate 時間: 2025-3-23 14:01 作者: 1分開 時間: 2025-3-23 19:04
Emerging Machine Learning Techniques in Predicting Adverse Drug Reactionsemerging machine learning models, including deep learning and graph-based models, as potential solutions to address this challenge were reviewed. As more data become available, it will become more feasible to make use of the complex data and emerging technologies to develop more accurate models to identify ADRs and protect patients from ADRs.作者: 終端 時間: 2025-3-24 01:53 作者: 未完成 時間: 2025-3-24 03:15 作者: 你正派 時間: 2025-3-24 08:06 作者: 缺乏 時間: 2025-3-24 12:23
Graph Kernel Learning for Predictive Toxicity Modelsons, challenges, and perspectives about the GKL techniques for toxicity-related problems. We hope this chapter could help better understand and guide applications of GKL in solving computational toxicity problems.作者: ligature 時間: 2025-3-24 15:52
Optimize and Strengthen Machine Learning Models Based on in Vitro Assays with Mechanistic Knowledge injury as an example. Another challenge for developing predictive models using in vitro assay data is the difficulty to corroborate the result with human data due to the scarcity of suitable datasets. We partially address this problem by taking advantage of real-world data. A novel statistical meth作者: Tartar 時間: 2025-3-24 22:54 作者: ablate 時間: 2025-3-25 02:17 作者: Corroborate 時間: 2025-3-25 03:57
Quantitative Target-specific Toxicity Prediction Modeling (QTTPM): Coupling Machine Learning with Dys employed to develop QTTPM models using dyPLIDs. Results indicate that dyPLID-based models outperformed those developed using conventional descriptors in predicting holdout test datasets. The QTTPM identified key dyPLIDs providing insights on ligand-induced protein structural changes that are impor作者: 災難 時間: 2025-3-25 11:28
Controlling for Confounding in Complex Survey Machine Learning Models to Assess Drug Safety and Riskpling weights. A viable approach for controlling confounding in complex observational surveys could open a new frontier for machine learning models and analysis in toxicological and medication studies with NHANES and other complex survey data.作者: JIBE 時間: 2025-3-25 14:55
Multivariate Curve Resolution for Analysis of Heterogeneous System in Toxicogenomicsivariate curve resolution (MCR) model transfers a mixed system into a bilinear model of pure component contributions, which can be useful in untangling heterogeneous systems such as TGx. In this chapter, the main goal of applying MCR to TGx is to reduce the effect of heterogeneous data on the expres作者: 可用 時間: 2025-3-25 17:21
Book 2023gy. 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作者: convert 時間: 2025-3-25 21:52 作者: 隱士 時間: 2025-3-26 00:33
Dmitry Filimonov,Alexander Dmitriev,Anastassia Rudik,Vladimir Poroikov作者: 證實 時間: 2025-3-26 07:44 作者: monochromatic 時間: 2025-3-26 11:59 作者: 哺乳動物 時間: 2025-3-26 13:50 作者: Radiculopathy 時間: 2025-3-26 20:36 作者: Vo2-Max 時間: 2025-3-26 23:22 作者: 牙齒 時間: 2025-3-27 05:08 作者: jeopardize 時間: 2025-3-27 07:26
Huixiao Hong,Jie Liu,Weigong Ge,Sugunadevi Sakkiah,Wenjing Guo,Gokhan Yavas,Chaoyang Zhang,Ping Gong作者: Dendritic-Cells 時間: 2025-3-27 10:22 作者: 清唱劇 時間: 2025-3-27 15:29 作者: 大溝 時間: 2025-3-27 21:02
Assessment of the Xenobiotics Toxicity Taking into Account Their Metabolismutical drug R&D, experimental metabolite structure is often not yet available. To increase the safety profile of novel pharmaceutical agents, a computer-aided assessment of toxicity should be performed based on the structural formulae of both parent compounds and their metabolites. In this chapter, 作者: 指派 時間: 2025-3-27 23:08
Emerging Machine Learning Techniques in Predicting Adverse Drug Reactionsne learning models have been developed to characterize, predict and prevent ADRs. However, it is a challenge for the models to effectively extract features and make predictions based on multiple sources of heterogeneous and complex data. In this chapter, different types of drug-related features and 作者: stroke 時間: 2025-3-28 05:09
Drug Effect Deep Learner Based on Graphical Convolutional Networkmulti-source information such as the gene interaction networks of human cells, the structure of drug molecules, and the gene expressions induced by drugs. In the model, genes, cells, even drug effects are all represent by 1024-dimensional vectors. Based on the vector representations, we develop a de作者: 值得贊賞 時間: 2025-3-28 08:30
AOP-Based Machine Learning for Toxicity Prediction imperative to evaluate the toxic effects of these compounds. However, the cost and time required to obtain toxicity data through traditional in vivo experimental methods are high. The promise of obtaining toxicity data through virtual screening, especially machine learning (ML) algorithms, has attr作者: 放肆的我 時間: 2025-3-28 11:00 作者: 黃油沒有 時間: 2025-3-28 15:17 作者: inveigh 時間: 2025-3-28 19:56
Multitask Learning for Quantitative Structure–Activity Relationships: A Tutorialonships and computational toxicology, multitask learning is gaining more and more interest, owed to its potential to improve the predictive performance of underrepresented tasks and to predict the multi-property profile of molecules. In this chapter, after introducing the multitask problem formulati作者: legacy 時間: 2025-3-28 23:07 作者: Limousine 時間: 2025-3-29 04:41
ED Profiler: Machine Learning Tool for Screening Potential Endocrine-Disrupting Chemicalserious effects of EDCs on the endocrine system of living organisms, we should identify and screen potential EDCs from the current myriad of commercially used chemicals. Computational models and software have been increasingly recognized as a valuable, effective, and powerful high-throughput virtual 作者: 攀登 時間: 2025-3-29 09:16
Quantitative Target-specific Toxicity Prediction Modeling (QTTPM): Coupling Machine Learning with Dyere we proposed a new concept of dynamic protein–ligand interaction descriptors (dyPLIDs) derived from molecular dynamics (MD) simulations and developed a novel quantitative target-specific toxicity prediction modeling (QTTPM) approach by integrating dyPLIDs with machine learning. In this proof-of-c作者: 山頂可休息 時間: 2025-3-29 13:53
Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predictingxicity of chemicals. Mold2 is a software tool developed in C++ for fast calculating molecular descriptors from two-dimensional structures. Mold2 descriptors contain rich information and can be used to build high-performance models in computational toxicology. Multiple studies have compared Mold2 des作者: 獨白 時間: 2025-3-29 16:59
Applicability Domain Characterization for Machine Learning QSAR Modelsedented success in developing “high-performance” QSAR models with various machine learning algorithms. Nonetheless, QSAR models are intrinsically data-driven models, in which patterns or rules are learned from training samples and thus can only be valid within limited applicability domains (AD). In 作者: IDEAS 時間: 2025-3-29 22:57
Controlling for Confounding in Complex Survey Machine Learning Models to Assess Drug Safety and Risk to complex survey data remain virtually unknown, which discourages analysis on survey data more complex than that of prevalence. The National Health and Nutrition Examination Survey (NHANES) is a long-running, complex survey designed to assess the health and well-being of adults and children in the作者: CODE 時間: 2025-3-30 00:39 作者: 我不明白 時間: 2025-3-30 06:31 作者: TOM 時間: 2025-3-30 08:31
Désiré Miessein,Norman J. M. Horing,Godfrey Gumbs,Harry Lenzinge a critical component of successful decomposition methods is a mechanism to model the interactions between subproblems. Ideally, this mechanism allows the constraints imposed on the unsolved subproblems by the solved ones to be propagated as the procedure progresses, ensuring feasible, high-quality作者: amorphous 時間: 2025-3-30 16:07
Application of Fuzzy Logic for Evaluating Student Learning Outcomes in E-Learning,abilities, interests and learning needs. The application of fuzzy logic allows for a more objective evaluation of student learning outcomes and contributes to improving the quality of education. #COMESYSO1120.作者: GUISE 時間: 2025-3-30 18:01
Jesse M. Thon,Robert W. Regenhardt,Joshua P. Kleinnts of the power spectral density matrix of the multidimensional stochastic process. The assumptions and the procedure of determination of basic relationships of the spectral method according to stress and stra978-3-642-09304-3978-3-540-73823-7Series ISSN 1613-7736 Series E-ISSN 1860-0816 作者: 刀鋒 時間: 2025-3-30 22:51
Asian Qualitative Research in TourismDer zweite Teil von WM behandelt die Geschichte der Hermeneutik und stellt danach die ?Grundzüge einer Theorie der hermeneutischen Erfahrung? vor. Wie im vorangegangenen Kapitel folge ich Gadamers geschichtlichen Ausführungen und rekonstruiere anschlie?end die Grundelemente seiner Theorie der hermeneutischen Erfahrung.作者: 典型 時間: 2025-3-31 04:52 作者: Longitude 時間: 2025-3-31 06:34
