Identification of Multi-Biomarker for Cancer Diagnosis and Prognosis based on Network Model and Multi-omics Data PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Identification of Multi-Biomarker for Cancer Diagnosis and Prognosis based on Network Model and Multi-omics Data PDF full book. Access full book title Identification of Multi-Biomarker for Cancer Diagnosis and Prognosis based on Network Model and Multi-omics Data by Chunquan Li. Download full books in PDF and EPUB format.
Author: Chunquan Li
Publisher: Frontiers Media SA
ISBN: 2832516246
Category : Science
Languages : en
Pages : 272
Get Book Here
Book Description
Author: Chunquan Li
Publisher: Frontiers Media SA
ISBN: 2832516246
Category : Science
Languages : en
Pages : 272
Get Book Here
Book Description
Author: Liang Cheng
Publisher: Frontiers Media SA
ISBN: 283251314X
Category : Medical
Languages : en
Pages : 349
Get Book Here
Book Description
Author: Ming Jun Zheng
Publisher: Frontiers Media SA
ISBN: 2832542565
Category : Science
Languages : en
Pages : 342
Get Book Here
Book Description
Author: Ehsan Nazemalhosseini-Mojarad
Publisher: Frontiers Media SA
ISBN: 2832530389
Category : Science
Languages : en
Pages : 433
Get Book Here
Book Description
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.
Author: Israel Tojal Da Silva
Publisher: Frontiers Media SA
ISBN: 283250020X
Category : Science
Languages : en
Pages : 149
Get Book Here
Book Description
Author: Chiara Romualdi
Publisher: Frontiers Media SA
ISBN: 2889661512
Category : Medical
Languages : en
Pages : 187
Get Book Here
Book Description
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Author: Debmalya Barh
Publisher: CRC Press
ISBN: 1466584289
Category : Medical
Languages : en
Pages : 992
Get Book Here
Book Description
Gleaning information from more than 100 experts in the field of cancer diagnosis, prognosis, and therapy worldwide, Cancer Biomarkers: Non-Invasive Early Diagnosis and Prognosis determines the significance of clinical validation approaches for several markers. This book examines the use of noninvasive or minimally invasive molecular cancer markers that are under development or currently in use. It deals with a majority of commonly prevalent cancers and can help anyone working in the health-care industry to recommend or develop early diagnostics, at-risk tests, and prognostic biomarkers for various cancers. It explores the practice of determining biomarkers by their characteristics and relative methodologies, and presents the most recent data as well as a number of current and upcoming early diagnostic noninvasive molecular markers for many common cancers. It also considers the sensitivity and specificity of markers, biomarker market, test providers, and patent information. Approximately 30-35 Cancer Specific Noninvasive Molecular Diagnostic Markers in a Single Volume The book details the general and technical aspects of noninvasive cancer markers. It covers imaging, cutting-edge molecular technologies for biomarker development, and noninvasive or minimally invasive sources of molecular markers, as well as quality control and ethical issues in cancer biomarker discovery. It also provides a detailed account of brain, head and neck, and oral cancer markers, and provides information on a number of gastrointestinal cancers, lung cancer, and mesothelioma markers. Emphasizes the Importance of Volatile Markers in Early Cancer Diagnosis Presents noninvasive early molecular markers in urological cancers Describes gynecological and endocrine cancer markers Details noninvasive markers of breast, ovarian, cervical, and thyroid cancers Addresses hematological malignancies Contains information on noninvasive molecular markers in myelodysplastic syndromes, acute myeloid leukemia, Hodgkin’s lymphoma, and multiple myeloma Provides comprehensive information on diagnostic and prognostic biomarkers in cutaneous melanoma This text considers molecular technologies for biomarker development, noninvasive or minimally invasive sources of molecular markers, and quality control and ethical issues in cancer biomarker discovery.
Author: Farnoosh Koleini
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
This thesis begins with a thorough review of research trends from 2015 to 2022, examining the challenges and issues related to biomarker discovery in multi-omics datasets. The review covers areas of application, proposed methodologies, evaluation criteria used to assess performance, as well as limitations and drawbacks that require further investigation and improvement. This comprehensive overview serves to provide a deeper understanding of the current state of research in this field and the opportunities for future research. It will be particularly useful for those who are interested in this area of study and seeking to expand their knowledge. In the second part of this thesis, a novel methodology is proposed for the identification of significant biomarkers in a multi-omics colon cancer dataset. The integration of clinical features with biomarker discovery has the potential to facilitate the early identification of mortality risk and the development of personalized therapies for a range of diseases, including cancer and stroke. Recent advancements in "omics" technologies have opened up new avenues for researchers to identify disease biomarkers through system-level analysis. Machine learning methods, particularly those based on tensor decomposition techniques, have gained popularity due to the challenges associated with integrative analysis of multi-omics data owing to the complexity of biological systems. Despite extensive efforts towards discovering disease-associated biomolecules by analyzing data from various "omics" experiments, such as genomics, transcriptomics, and metabolomics, the poor integration of diverse forms of 'omics' data has made the integrative analysis of multi-omics data a daunting task. Our research includes ANOVA simultaneous component analysis (ASCA) and Tucker3 modeling to analyze a multivariate dataset with an underlying experimental design. By comparing the spaces spanned by different model components we showed how the two methods can be used for confirmatory analysis and provide complementary information. we demonstrated the novel use of ASCA to analyze the residuals of Tucker3 models to find the optimum one. Increasing the model complexity to more factors removed the last remaining ASCA detectable structure in the residuals. Bootstrap analysis of the core matrix values of the Tucker3 models used to check that additional triads of eigenvectors were needed to describe the remaining structure in the residuals. Also, we developed a new simple, novel strategy for aligning Tucker3 bootstrap models with the Tucker3 model of the original data so that eigenvectors of the three modes, the order of the values in the core matrix, and their algebraic signs match the original Tucker3 model without the need for complicated bookkeeping strategies or performing rotational transformations. Additionally, to avoid getting an overparameterized Tucker3 model, we used the bootstrap method to determine 95% confidence intervals of the loadings and core values. Also, important variables for classification were identified by inspection of loading confidence intervals. The experimental results obtained using the colon cancer dataset demonstrate that our proposed methodology is effective in improving the performance of biomarker discovery in a multi-omics cancer dataset. Overall, our study highlights the potential of integrating multi-omics data with machine learning methods to gain deeper insights into the complex biological mechanisms underlying cancer and other diseases. The experimental results using NIH colon cancer dataset demonstrate that the successful application of our proposed methodology in cancer subtype classification provides a foundation for further investigation into its utility in other disease areas.
Author: Fengfeng Zhou
Publisher: Frontiers Media SA
ISBN: 2889765709
Category : Science
Languages : en
Pages : 246
Get Book Here
Book Description
Author: Lixin Cheng
Publisher: Frontiers Media SA
ISBN: 283253175X
Category : Science
Languages : en
Pages : 757
Get Book Here
Book Description
This Research Topic is part of a series with, "Bioinformatics Analysis of Omics Data for Biomarker Identification in Clinical Research - Volume I" (https://www.frontiersin.org/research-topics/13816/bioinformatics-analysis-of-omics-data-for-biomarker-identification-in-clinical-research) The advances and the decreasing cost of omics data enable profiling of disease molecular features at different levels, including bulk tissues, animal models, and single cells. Large volumes of omics data enhance the ability to search for information for preclinical study and provide the opportunity to leverage them to understand disease mechanisms, identify molecular targets for therapy, and detect biomarkers of treatment response. Identification of stable, predictive, and interpretable biomarkers is a significant step towards personalized medicine and therapy. Omics data from genomics, transcriptomics, proteomics, epigenomics, metagenomics, and metabolomics help to determine biomarkers for prognostic and diagnostic applications. Preprocessing of omics data is of vital importance as it aims to eliminate systematic experimental bias and technical variation while preserving biological variation. Dozens of normalization methods for correcting experimental variation and bias in omics data have been developed during the last two decades, while only a few consider the skewness between different sample states, such as the extensive over-repression of genes in cancers. The choice of normalization methods determines the fate of identified biomarkers or molecular signatures. From these considerations, the development of appropriate normalization methods or preprocessing strategies may promote biomarker identification and facilitate clinical decision-making.
