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From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology
Published in Shaker A. Mousa, Raj Bawa, Gerald F. Audette, The Road from Nanomedicine to Precision Medicine, 2020
Maria Eugenia Gallo Cantafio, Katia Grillone, Daniele Caracciolo, Francesca Scionti, Mariamena Arbitrio, Vito Barbieri, Licia Pensabene, Pietro Hiram Guzzi, Maria Teresa Di Martino
Among these datasets, TCGA is the most extensive, and includes multi-omics data deposited from many centers involved in the TCGA research network, as well as patient’s clinical metadata prospectively collected. TCGA data currently refers to 33 cancer types from more than 11,000 patients that have been obtained through different high-throughput technologies such as DNA-seq, SNP-based platforms, array-based DNA methylation-seq, microRNA-seq, RNA-seq, and RPPA by providing a comprehensive view of the human cancer molecular bases. Each platform produces data that are informative about DNA mutational status, SNP, methylation, loss of heterozygosity (LOH), copy number variation, miRNA expression, gene expression, and protein expression.
From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology
Published in Shaker A. Mousa, Raj Bawa, Gerald F. Audette, The Road from Nanomedicine to Precision Medicine, 2019
Maria Eugenia Gallo Cantafio, Katia Grillone, Daniele Caracciolo, Francesca Scionti, Mariamena Arbitrio, Vito Barbieri, Licia Pensabene, Pietro Hiram Guzzi, Maria Teresa Di Martino
Among these datasets, TCGA is the most extensive, and includes multi-omics data deposited from many centers involved in the TCGA research network, as well as patient’s clinical metadata prospectively collected. TCGA data currently refers to 33 cancer types from more than 11,000 patients that have been obtained through different high-throughput technologies such as DNA-seq, SNP-based platforms, array-based DNA methylation-seq, microRNA-seq, RNA-seq, and RPPA by providing a comprehensive view of the human cancer molecular bases. Each platform produces data that are informative about DNA mutational status, SNP, methylation, loss of heterozygosity (LOH), copy number variation, miRNA expression, gene expression, and protein expression.
Applications of Machine Learning in Cancer Prediction and Prognosis
Published in Meenu Gupta, Rachna Jain, Arun Solanki, Fadi Al-Turjman, Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective, 2021
Geetika Sharma, Chander Prabha
For cancer prediction, many physicians use a large amount of topological, clinical, and population-based data [28,29] and along with it many features like weight, age, diet, bad habits, family history, and any exposure to environmental health hazards; these altogether play a major role in the prediction of cancer [30–32]. However, for making our decisions more robust, we require much more information apart from the above macro scale information. With the tremendous use of genomic, proteomic, and various imaging technologies, a unique type of molecular information was delivered. Few factors play a major role in cancer prediction like cellular parameters, gene expressions, and molecular biomarkers. The most challenging task for any physician is to predict any disease outcome more accurately. For this purpose, ML techniques have become an important and popular technique among many researchers. These ML techniques can predict the future outcome of any cancer type very effectively by discovering and identifying patterns from any complex dataset. Apart from this, many feature selection techniques and their applications are published in the literature survey of many research papers [33–35]. Nowadays, based on various genetic defects and different clinical outcomes, a single cancer has been divided into many subgroups. Since genetic defects have different treatment approaches, one needs to identify less costly and effectively small groups of patients. The Cancer Genome Atlas Research Network (TCGA) is a community resource project that provides a large amount of genomic data about specific tumor types and also provides the ability for a better understanding of cancer with the use of high throughput genome technologies.
A Novel Multi-Neural Ensemble Approach for Cancer Diagnosis
Published in Applied Artificial Intelligence, 2022
Surbhi Gupta, Manoj Kumar Gupta, Rakesh Kumar
Leukemia: In 2018, a research study (Mei et al. 2018) applied neural Learning to predict acute myeloid leukemia (AML). The dataset used in the study was taken from TCGA (The Cancer Genome Atlas) database. The implementation used stacked Autoencoders to formulate a categorized DL model. The model implemented in R language attained exceptional correctness of 83% in forecasting prognosis. A review article published in 2019 (Salah et al. 2019) emphasized the utilization of ML models to predict leukemia diagnosis. A total of 58 research studies were revised. A significant factor observed in this study was that none of the articles applied ML models in real‐world scenarios. More than 90% of articles utilized small and homogenous samples. A research study was done in 2019 (Shouval et al. 2019) worked on predicting the survival of leukemia patients after the Autologous Stem Cell Transplantation. A recent research study 2020 (Maria, Devi, and Ravi 2020) employed ML to predict diagnosis. The respective research presented a comparative study of SVM, KNN, Neural Networks, and NB for the classification of leukemia into its subtypes.
Multi-platform analysis of methylation-regulated genes in human lung adenocarcinoma
Published in Journal of Toxicology and Environmental Health, Part A, 2019
Jin Wang, Xiao-fan Yu, Nan OUYang, Qiu-lin Luo, Shi-yu Zhao, Xi-fei Guan, Tao Chen, Jian-xiang Li
The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository that archives and makes available information on DNA methylation, mRNA expression, and single-nucleotide variations (Barrett and Edgar 2006; Clough and Barrett 2016). In addition, The Cancer Genome Atlas (TCGA) is a large-scale cancer genome project with multi-dimensional maps of key genomic and pathological information for 33 types of cancers (http://cancergenome.nih.gov/) (Lee 2016). Both the GEO and TCGA databases contain information necessary to understand the molecular mechanism(s) involved in carcinogenesis. Based on a vast amount of pathological information, a combination of GEO with TCGA analysis may provide a more comprehensive, reliable indicator in the development of human LUAD and the potential role of DNA methylation.