Identification Of Immune Related Biomarkers For Cancer Diagnosis Based On Multi Omics Data
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Identification of immune related biomarkers for cancer diagnosis based on multi omics data
Author | : Liang Cheng,Xin Zhang,Chuan-Xing Li,Rui Guo |
Publsiher | : Frontiers Media SA |
Total Pages | : 349 |
Release | : 2023-02-02 |
Genre | : Medical |
ISBN | : 9782832513149 |
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Identification of Multi Biomarker for Cancer Diagnosis and Prognosis based on Network Model and Multi omics Data
Author | : Chunquan Li,Dechao Bu,Dechen Lin Lin,Sun Liang,Masaharu Hazawa |
Publsiher | : Frontiers Media SA |
Total Pages | : 272 |
Release | : 2023-03-02 |
Genre | : Science |
ISBN | : 9782832516249 |
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Multi omic Data Integration in Oncology
![Multi omic Data Integration in Oncology](https://youbookinc.com/wp-content/uploads/2024/06/cover.jpg)
Author | : Chiara Romualdi,Enrica Calura,Davide Risso,Sampsa Hautaniemi,Francesca Finotello |
Publsiher | : Unknown |
Total Pages | : 0 |
Release | : 2020 |
Genre | : Electronic Book |
ISBN | : OCLC:1368448508 |
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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.
Bioinformatics Analysis of Omics Data for Biomarker Identification in Clinical Research Volume II
Author | : Lixin Cheng,Hongwei Wang,Shibiao Wan |
Publsiher | : Frontiers Media SA |
Total Pages | : 757 |
Release | : 2023-09-05 |
Genre | : Science |
ISBN | : 9782832531754 |
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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.
Systematic identification of novel diagnostic and prognostic tumor biomarkers based on multi omics data analysis of solid tumors
Author | : Ming Jun Zheng,Marina Pinheiro,Mingming Deng |
Publsiher | : Frontiers Media SA |
Total Pages | : 342 |
Release | : 2024-01-08 |
Genre | : Science |
ISBN | : 9782832542569 |
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Data Mining and Statistical Methods for Knowledge Discovery in Diseases Based on Multimodal Omics
Author | : Jiajie Peng,Tao Wang,Miguel E. Renteria |
Publsiher | : Frontiers Media SA |
Total Pages | : 160 |
Release | : 2022-06-06 |
Genre | : Science |
ISBN | : 9782889761746 |
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Application of Bioinformatics in Cancers
Author | : Chad Brenner |
Publsiher | : MDPI |
Total Pages | : 418 |
Release | : 2019-11-20 |
Genre | : Medical |
ISBN | : 9783039217885 |
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This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible. Accordingly, the series presented here bring forward a wide range of artificial intelligence approaches and statistical methods that can be applied to imaging and genomics data sets to identify previously unrecognized features that are critical for cancer. Our hope is that these articles will serve as a foundation for future research as the field of cancer biology transitions to integrating electronic health record, imaging, genomics and other complex datasets in order to develop new strategies that improve the overall health of individual patients.
Omics Data Integration towards Mining of Phenotype Specific Biomarkers in Cancer Volume II
Author | : Liang Cheng,Lei Deng,Chuan-Xing Li,Yan Zhang,Mingxiang Teng |
Publsiher | : Frontiers Media SA |
Total Pages | : 793 |
Release | : 2022-11-29 |
Genre | : Science |
ISBN | : 9782832507384 |
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