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Big Data in Omics and Imaging: Integrated Analysis and Causal Inference

Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic;

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Big Data in Omics and Imaging: Integrated Analysis and Causal Inference

Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic;

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Big Data in Omics and Imaging, Two Volume Set

to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference;

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Big Data in Omics and Imaging: Association Analysis

Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both;

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Big Data in Omics and Imaging: Association Analysis

Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both;

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Causation in Population Health Informatics and Data Science

Marketing text: This book covers the overlap between informatics, computer science, philosophy of causation, and causal inference;

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Fundamentals of Advanced Omics Technologies: From Genes to Metabolites

aspects related to chemometrics, bioinformatics, data treatment, data integration and systems biology, deep-sequencing data analysis, statistical;

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Big Data Analysis for Bioinformatics and Biomedical Discoveries

Demystifies Biomedical and Biological Big Data Analyses Big Data Analysis for Bioinformatics and Biomedical Discoveries provides a;

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Big Data Analysis for Bioinformatics and Biomedical Discoveries

Demystifies Biomedical and Biological Big Data Analyses Big Data Analysis for Bioinformatics and Biomedical Discoveries provides a;

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The SAGE Handbook of Regression Analysis and Causal Inference

longitudinal data with an emphasis on causal analysis and provides readers with an introduction to and exploration of a large range of techniques.;

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Refining the Concept of Scientific Inference When Working with Big Data

applied, analysis of big data may result in misleading correlations and false discoveries, which can potentially undermine confidence;

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Combining Human Genetics and Causal Inference to Understand Human Disease and Development

In human genetics, causal inference methods leverage large omics data sets and phenotypic information to decipher various cause-and-effect;

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Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies

This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big;

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Analysis of Integrated Data

of integrated data. Identifies the important themes that will define future research and teaching in the statistical analysis of integrated data;

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Analysis of Integrated Data

research and teaching in the statistical analysis of integrated data. Analysis of Integrated Data is aimed primarily at researchers;

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Multimethod Research, Causal Mechanisms, and Case Studies

are the central goals of multimethod and case study research, and provides a logic for connecting case studies and causal mechanism analysis;

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Multimethod Research, Causal Mechanisms, and Case Studies

are the central goals of multimethod and case study research, and provides a logic for connecting case studies and causal mechanism analysis;

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Counterfactuals and Causal Inference

counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative;

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Counterfactuals & Causal Inference

counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative;

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Application of Omics, AI and Blockchain in Bioinformatics Research

in this volume include: clinical bioinformatics, omics-based data analysis, Artificial Intelligence (AI), blockchain, big data analytics, drug;

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Systems Analytics and Integration of Big Omics Data

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Causality and Probability in the Sciences

its careful analysis, "Causality and Probability in the Sciences" heralds the transition of causal inference from an art to a science.;

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Elements of Causal Inference

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The;

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Data Analysis for Omic Sciences: Methods and Applications

Data Analysis for Omic Sciences: Methods and Applications, Volume 82, shows how these types of challenging datasets can be analyzed;

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Agent-based Models and Causal Inference

inference, and agent-based computation models to help show that causality means different things within different methods for causal analysis, and;

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Missing Data Analysis in Practice

error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference.;

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Missing Data Analysis in Practice

error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference.;

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