Download or read book Computational Methods for Precision Oncology written by Alessandro Laganà and published by Springer Nature. This book was released on 2022-03-01 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: Precision medicine holds great promise for the treatment of cancer and represents a unique opportunity for accelerated development and application of novel and repurposed therapeutic approaches. Current studies and clinical trials demonstrate the benefits of genomic profiling for patients whose cancer is driven by specific, targetable alterations. However, precision oncologists continue to be challenged by the widespread heterogeneity of cancer genomes and drug responses in designing personalized treatments. Chapters provide a comprehensive overview of the computational approaches, methods, and tools that enable precision oncology, as well as related biological concepts. Covered topics include genome sequencing, the architecture of a precision oncology workflow, and introduces cutting-edge research topics in the field of precision oncology. This book is intended for computational biologists, bioinformaticians, biostatisticians and computational pathologists working in precision oncology and related fields, including cancer genomics, systems biology, and immuno-oncology.
Download or read book Improving Cancer Diagnosis and Care written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-08-15 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hallmark of high-quality cancer care is the delivery of the right treatment to the right patient at the right time. Precision oncology therapies, which target specific genetic changes in a patient's cancer, are changing the nature of cancer treatment by allowing clinicians to select therapies that are most likely to benefit individual patients. In current clinical practice, oncologists are increasingly formulating cancer treatment plans using results from complex laboratory and imaging tests that characterize the molecular underpinnings of an individual patient's cancer. These molecular fingerprints can be quite complex and heterogeneous, even within a single patient. To enable these molecular tumor characterizations to effectively and safely inform cancer care, the cancer community is working to develop and validate multiparameter omics tests and imaging tests as well as software and computational methods for interpretation of the resulting datasets. To examine opportunities to improve cancer diagnosis and care in the new precision oncology era, the National Cancer Policy Forum developed a two-workshop series. The first workshop focused on patient access to expertise and technologies in oncologic imaging and pathology and was held in February 2018. The second workshop, conducted in collaboration with the Board on Mathematical Sciences and Analytics, was held in October 2018 to examine the use of multidimensional data derived from patients with cancer, and the computational methods that analyze these data to inform cancer treatment decisions. This publication summarizes the presentations and discussions from the second workshop.
Download or read book Advanced Computational Methods for Oncological Image Analysis written by Leonardo Rundo and published by Mdpi AG. This book was released on 2021-12-06 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians' unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations-such as segmentation, co-registration, classification, and dimensionality reduction-and multi-omics data integration.
Download or read book Essentials of Cancer Genomic Computational Approaches and Precision Medicine written by Nosheen Masood and published by Springer Nature. This book was released on 2020-03-20 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book concisely describes the role of omics in precision medicine for cancer therapies. It outlines our current understanding of cancer genomics, shares insights into the process of oncogenesis, and discusses emerging technologies and clinical applications of cancer genomics in prognosis and precision-medicine treatment strategies. It then elaborates on recent advances concerning transcriptomics and translational genomics in cancer diagnosis, clinical applications, and personalized medicine in oncology. Importantly, it also explains the importance of high-performance analytics, predictive modeling, and system biology in cancer research. Lastly, the book discusses current and potential future applications of pharmacogenomics in clinical cancer therapy and cancer drug development.
Download or read book Computational Approaches in Drug Discovery and Precision Medicine written by Zunnan Huang and published by Frontiers Media SA. This book was released on 2021-03-15 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computational Approaches to Improve Precision Oncology written by Andrea Garofoli and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computational Methods in Drug Discovery and Repurposing for Cancer Therapy written by Ganji Purnachandra Nagaraju and published by Elsevier. This book was released on 2023-03-22 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Methods in Drug Discovery and Repurposing for Cancer Therapy provides knowledge about ongoing research as well as computational approaches for drug discovery and repurposing for cancer therapy. The book also provides detailed descriptions about target molecules, pathways, and their inhibitors for easy understanding and applicability. The book discusses tools and techniques such as integrated bioinformatics approaches, systems biology tools, molecular docking, computational chemistry, artificial intelligence, machine learning, structure-based virtual screening, biomarkers, and transcriptome; those are discussed in the context of different cancer types, such as colon, pancreatic, glioblastoma, endometrial, and retinoblastoma, among others. This book is a valuable resource for researchers, students, and members of the biomedical and medical fields who want to learn more about the use of computational modeling to better tailor the treatment for cancer patients. Discusses in silico remodeling of effective phytochemical compounds for discovering improved anticancer agents for substantial/significant cancer therapy Covers potential tools of bioinformatics that are applied toward discovering new targets by drug repurposing and strategies to cure different types of cancers Demonstrates the significance of computational and artificial intelligence approaches in anticancer drug discovery Explores how these various advances can be integrated into a precision and personalized medicine approach that can eventually enhance patient care
Download or read book Mathematical and Computational Oncology written by George Bebis and published by Springer Nature. This book was released on 2021-12-11 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Symposium on Mathematical and Computational Oncology, ISMCO 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 3 full papers and 4 short papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in topical sections named: statistical and machine learning methods for cancer research; mathematical modeling for cancer research; spatio-temporal tumor modeling and simulation; general cancer computational biology; mathematical modeling for cancer research; computational methods for anticancer drug development.
Download or read book Computational Methods for Multi Omics Data Analysis in Cancer Precision Medicine written by Ehsan Nazemalhosseini-Mojarad and published by Frontiers Media SA. This book was released on 2023-08-02 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Precision Medicine in Oncology written by Bulent Aydogan and published by John Wiley & Sons. This book was released on 2020-11-02 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: A FRESH EXAMINATION OF PRECISION MEDICINE'S INCREASINGLY PROMINENT ROLE IN THE FIELD OF ONCOLOGY Precision medicine takes into account each patient's specific characteristics and requirements to arrive at treatment plans that are optimized towards the best possible outcome. As the field of oncology continues to advance, this tailored approach is becoming more and more prevalent, channelling data on genomics, proteomics, metabolomics and other areas into new and innovative methods of practice. Precision Medicine in Oncology draws together the essential research driving the field forward, providing oncology clinicians and trainees alike with an illuminating overview of the technology and thinking behind the breakthroughs currently being made. Topics covered include: Biologically-guided radiation therapy Informatics for precision medicine Molecular imaging Biomarkers for treatment assessment Big data Nanoplatforms Casting a spotlight on this emerging knowledge base and its impact upon the management of tumors, Precision Medicine in Oncology opens up new possibilities and ways of working – not only for oncologists, but also for molecular biologists, radiologists, medical geneticists, and others.
Download or read book Computational Systems Biology Approaches in Cancer Research written by Inna Kuperstein and published by CRC Press. This book was released on 2019-09-09 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Computational Systems BiologyApproaches in Cancer Research: "Complex concepts are written clearly and with informative illustrations and useful links. The book is enjoyable to read yet provides sufficient depth to serve as a valuable resource for both students and faculty." — Trey Ideker, Professor of Medicine, UC Xan Diego, School of Medicine "This volume is attractive because it addresses important and timely topics for research and teaching on computational methods in cancer research. It covers a broad variety of approaches, exposes recent innovations in computational methods, and provides acces to source code and to dedicated interactive web sites." — Yves Moreau, Department of Electrical Engineering, SysBioSys Centre for Computational Systems Biology, University of Leuven With the availability of massive amounts of data in biology, the need for advanced computational tools and techniques is becoming increasingly important and key in understanding biology in disease and healthy states. This book focuses on computational systems biology approaches, with a particular lens on tackling one of the most challenging diseases - cancer. The book provides an important reference and teaching material in the field of computational biology in general and cancer systems biology in particular. The book presents a list of modern approaches in systems biology with application to cancer research and beyond. It is structured in a didactic form such that the idea of each approach can easily be grasped from the short text and self-explanatory figures. The coverage of topics is diverse: from pathway resources, through methods for data analysis and single data analysis to drug response predictors, classifiers and image analysis using machine learning and artificial intelligence approaches. Features Up to date using a wide range of approaches Applicationexample in each chapter Online resources with useful applications’
Download or read book Computational Methods in Inferring Cancer Tissue of Origin and Cancer Molecular Classification Volume I written by Min Tang and published by Frontiers Media SA. This book was released on 2021-04-01 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Clinical Precision Medicine written by Judy S. Crabtree and published by Academic Press. This book was released on 2019-11-15 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clinical Precision Medicine: A Primer offers clinicians, researchers and students a practical, up-to-date resource on precision medicine, its evolving technologies, and pathways towards clinical implementation. Early chapters address the fundamentals of molecular biology and gene regulation as they relate to precision medicine, as well as the foundations of heredity and epigenetics. Oncology, an early adopter of precision approaches, is considered with its relationship to genetic variation in drug metabolism, along with tumor immunology and the impact of DNA variation in clinical care. Contributions by Stephanie Kramer, a Clinical Genetic Counselor, also provide current information on prenatal diagnostics and adult genetics that highlight the critical role of genetic counselors in the era of precision medicine. Includes applied discussions of chromosomes and chromosomal abnormalities, molecular genetics, epigenetic regulation, heredity, clinical genetics, pharmacogenomics and immunogenomics Features chapter contributions from leaders in the field Consolidates fundamental concepts and current practices of precision medicine in one convenient resource
Download or read book Computational Approaches for Cancer Precision Medicine written by Lindsay C. Stetson and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many types of cancer have no proven means of prevention or effective therapies. Precision medicine is an emerging approach for disease treatment that takes into account the biology of the patient in an effort to improve therapeutic outcome. While significant advances have been made in precision medicine when it comes to select cancers such as breast and lung, precision medicine is still not used by clinicians when initiating treatment for most cancer patients. Advances in DNA sequencing and large-scale studies such as The Cancer Genome Atlas (TCGA) have led to a better understanding of the molecular initiators and drivers of cancer, but challenges remain in bringing precision medicine from the bench to the bedside. A major impediment to achieving personalized therapy is the small number of drugs developed to target the proteins encoded by potential driver genes. Additionally, the large numbers of genetic aberrations in cancer discovered through next-generation sequencing have not always translated into actionable drug targets. In this dissertation these two challenges are addressed. First, we demonstrate that data from large-scale pharmacogenomic studies can be computationally mined to create omic signatures of drug response. The benefit of this study is the ability to rapidly and cost-effectively identify drugs and research compounds that can be repositioned or repurposed for use in different cancer types. Additionally, we demonstrate that this approach can successfully identify the precise subgroup of cancer patients that will benefit from a drug treatment based on their unique tumor biology. We then show that proteomics studies completed as part of TCGA can be computationally mined to create protein models predictive of patient survival. The resulting protein signatures can be used by clinicians to identify those patients that are high-risk and should be treated more aggressively or referred to clinical trial. Additionally, proteins that are correlated to patient survival are potential actionable drug targets. Both drug development and clinical trials are expensive; computational approaches such as those described in this dissertation are critical to the cost-effective and timely development of precision medicines.
Download or read book Precision Medicine Oncology written by Lorna Rodriguez-Rodriguez and published by Rutgers University Press. This book was released on 2019-09-13 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: Precision medicine is rapidly becoming the standard-of-care for the treatment of cancer patients. This is made possible, in part, by the ready availability and reasonable costs of comprehensive DNA and RNA sequencing assays. However, precision medicine is complex and incorporates entirely new types of data and treatment paradigms that are outside of the training of most oncologists in practice today. Precision Medicine Oncology: A Primer is a concise review of the fundamental principles and applications of precision medicine, intended for clinicians, particularly those working in oncology. It provides an accessible introduction to the technological advances in DNA and RNA sequencing, gives a detailed overview of approaches to the interpretation of molecular test results and their point-of-care implementation for individual patients, and describes innovative clinical trial designs in oncology as well as characteristics of the computational infrastructures through which massive quantities of data are collected, stored, and used in precision medicine oncology.
Download or read book Deep Transfer Learning for Drug Response Prediction written by Hossein Sharifi Noghabi and published by . This book was released on 2021 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of precision oncology is to make accurate predictions for cancer patients via some omics data types of individual patients. Major challenges of computational methods for drug response prediction are that labeled clinical data is very limited, not publicly available, or has drug response for one or two drugs. These challenges have been addressed by generating large-scale pre-clinical datasets such as cancer cell lines or patient-derived xenografts (PDX). These pre-clinical datasets have multi-omics characterization of samples and are often screened with hundreds of drugs which makes them viable resources for precision oncology. However, they raise new questions: how can we integrate different data types? how can we handle data discrepancy between pre-clinical and clinical datasets that exist due to basic biological differences? and how can we make the best use of unlabeled samples in drug response prediction where labeling is extra challenging? In this thesis, we propose methods based on deep neural networks to answer these questions. First, we propose a method of multi-omics integration. Second, we propose a transfer learning method to address data discrepancy between cell lines, patients, and PDX models in the input and output space. Finally, we proposed a semi-supervised method of out-of-distribution generalization to predict drug response using labeled and unlabeled samples. The proposed methods have promising performance when compared to the state-of-the-art and may guide precision oncology more accurately.
Download or read book Computational Biology written by Tuan Pham and published by Springer Science & Business Media. This book was released on 2009-09-23 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume covers techniques in computational biology and their applications in oncology. It details advanced statistical methods, heuristic algorithms, cluster analysis, data modeling, and image and pattern analysis applied to cancer research.
