Machine Learning Advanced Dynamic Omics Data Analysis for Precision Medicine - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning Advanced Dynamic Omics Data Analysis for Precision Medicine write by Tao Zeng. This book was released on 2020-03-30. Machine Learning Advanced Dynamic Omics Data Analysis for Precision Medicine available in PDF, EPUB and Kindle.
Computational Methods for Multi-Omics Data Analysis in Cancer Precision Medicine
Computational Methods for Multi-Omics Data Analysis in Cancer Precision Medicine - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Computational Methods for Multi-Omics Data Analysis in Cancer Precision Medicine write by Ehsan Nazemalhosseini-Mojarad . This book was released on 2023-08-02. Computational Methods for Multi-Omics Data Analysis in Cancer Precision Medicine available in PDF, EPUB and Kindle. Cancer is a complex and heterogeneous disease often caused by different alterations. The development of human cancer is due to the accumulation of genetic and epigenetic modifications that could affect the structure and function of the genome. High-throughput methods (e.g., microarray and next-generation sequencing) can investigate a tumor at multiple levels: i) DNA with genome-wide association studies (GWAS), ii) epigenetic modifications such as DNA methylation, histone changes and microRNAs (miRNAs) iii) mRNA. The availability of public datasets from different multi-omics data has been growing rapidly and could facilitate better knowledge of the biological processes of cancer. Computational approaches are essential for the analysis of big data and the identification of potential biomarkers for early and differential diagnosis, and prognosis.
Machine Learning for Clinical Trials and Precision Medicine
Machine Learning for Clinical Trials and Precision Medicine - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning for Clinical Trials and Precision Medicine write by Ruishan Liu. This book was released on 2022. Machine Learning for Clinical Trials and Precision Medicine available in PDF, EPUB and Kindle. Machine learning (ML) has been wildly applied in biomedicine and healthcare. The growing abundance of medical data and the advance of biological technologies (e.g. next-generation sequencing) have offered great opportunities for using ML in computational biology and health. In this thesis, I present my works contributing to this emerging field in three aspects -- using large-scale datasets to advance medical studies, developing algorithms to solve biological challenges, and building analysis tools for new technologies. In the first part, I present two works of applying ML on large-scale real-world data: one for clinical trial design and one for precision medicine. Overly restrictive eligibility criteria has been a key barrier for clinical trials. In the thesis, I introduce a powerful computational framework, Trial Pathfinder, which enables inclusive criteria and data valuation for clinical trials. A critical goal for precision medicine is to characterize how patients with specific genetic mutations respond to therapies. In the thesis, I present systematic pan-cancer analysis of mutation-treatment interactions using large real-world clinico-genomics data. In the second part, I introduce my work on developing algorithms to solve biological challenge -- aligning multiple datasets with subset correspondence information. In many biological and medical applications, we have multiple related datasets from different sources or domains, and learning efficient computational mappings between these datasets is an important problem. In the thesis, I present an end-to-end optimal transport framework that effectively leverages side information to align datasets. Finally, I present my work on developing analysis tools for new technologies -- spatial transcriptomics and RNA velocity. Recently high-throughput image-based transcriptomic methods were developed and enabled researchers to spatially resolve gene expression variation at the molecular level for the first time. In the thesis, I describe a general analysis tool to quantitatively study the spatial correlations of gene expression in fixed tissue sections. Recent development in inferring RNA velocity from single-cell RNA-seq opens up exciting new vista into developmental lineage and cellular dynamics. In the thesis, I introduce a principled computational framework that extends RNA velocity to quantify systems level dynamics and improve single-cell data analysis.
Big Data in Omics and Imaging
Big Data in Omics and Imaging - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Big Data in Omics and Imaging write by Momiao Xiong. This book was released on 2017-12-01. Big Data in Omics and Imaging available in PDF, EPUB and Kindle. Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data. FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection Provides real-world examples and case studies Will have an accompanying website with R code The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.
Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine
Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine write by Michael R. Kosorok. This book was released on 2015-12-08. Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine available in PDF, EPUB and Kindle. Personalized medicine is a medical paradigm that emphasizes systematic use of individual patient information to optimize that patient's health care, particularly in managing chronic conditions and treating cancer. In the statistical literature, sequential decision making is known as an adaptive treatment strategy (ATS) or a dynamic treatment regime (DTR). The field of DTRs emerges at the interface of statistics, machine learning, and biomedical science to provide a data-driven framework for precision medicine. The authors provide a learning-by-seeing approach to the development of ATSs, aimed at a broad audience of health researchers. All estimation procedures used are described in sufficient heuristic and technical detail so that less quantitative readers can understand the broad principles underlying the approaches. At the same time, more quantitative readers can implement these practices. This book provides the most up-to-date summary of the current state of the statistical research in personalized medicine; contains chapters by leaders in the area from both the statistics and computer sciences fields; and also contains a range of practical advice, introductory and expository materials, and case studies.
