Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival

Ramazzotti, Daniele; A, Lal; Wang, Bo; Batzoglou, Serafim; Sidow, Arend

doi:10.1038/s41467-018-06921-8

Cited by 135 publications

(100 citation statements)

References 42 publications

(71 reference statements)

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“…PINS addresses two challenges: the meaningful integration of several different data types and the discovery of molecular disease subtypes characterized by relevant clinical differences, such as survival [25]. Finally, CIMLR is a new cancer subtyping method that integrates multi-omic data to reveal molecular subtypes of cancer [26].…”

Section: Input Definition and Methodsmentioning

confidence: 99%

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Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

et al. 2020

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Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.

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Section: Input Definition and Methodsmentioning

confidence: 99%

“…Based on PINS, Nguyen et al identify known cancer subtypes and novel subgroups of patients with significantly different survival profiles [25]. Based on CIMLR, Ramazzotti et al extract biologically meaningful cancer subtypes from multi-omic data from 36 cancer types [26].…”

Section: Applicationsmentioning

confidence: 99%

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

et al. 2020

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“…We basically followed a pipeline and the previously published autoencoder hyper parameter settings [11]. As previously described, we implemented the autoencoder with three hidden layers (500, 100, and 500 nodes) with Keras (https://keras.io; version 2.2.4).…”

Section: Clustering To Obtain Inferred Labels From Luad Multi-omics Dmentioning

confidence: 99%

“…Chromosome 2,6,8,10,11,17,18,19,20, and 22 had a different copy numbers (p < 0.05, Mann-Whitney U-test).Biomolecules 2019, 9, x 8 of 19…”

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confidence: 99%

Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer

et al. 2020

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Lung cancer is one of the leading causes of death worldwide. Therefore, understanding the factors linked to patient survival is essential. Recently, multi-omics analysis has emerged, allowing for patient groups to be classified according to prognosis and at a more individual level, to support the use of precision medicine. Here, we combined RNA expression and miRNA expression with clinical information, to conduct a multi-omics analysis, using publicly available datasets (the cancer genome atlas (TCGA) focusing on lung adenocarcinoma (LUAD)). We were able to successfully subclass patients according to survival. The classifiers we developed, using inferred labels obtained from patient subtypes showed that a support vector machine (SVM), gave the best classification results, with an accuracy of 0.82 with the test dataset. Using these subtypes, we ranked genes based on RNA expression levels. The top 25 genes were investigated, to elucidate the mechanisms that underlie patient prognosis. Bioinformatics analyses showed that the expression levels of six out of 25 genes (ERO1B, DPY19L1, NCAM1, RET, MARCH1, and SLC7A8) were associated with LUAD patient survival (p < 0.05), and pathway analyses indicated that major cancer signaling was altered in the subtypes.

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“…Each of these molecular dimensions is correlated yet characterize the disease in their own unique way. In order to arrive at a comprehensive molecular portrait of the tumor, multiple groups have proposed statistical and computational algorithms to synthesize various channels of information including methods developed by us (iCluster 1,2 ) and others (PARADIGM 3 , CoCA 4 , SNF 5 , CIMLR 6 ) to stratify disease populations. However, the majority of the work has focused on unsupervised clustering, utilizing the molecular data alone.…”

Section: Introductionmentioning

confidence: 99%

Pan-cancer identification of clinically relevant genomic subtypes using outcome-weighted integrative clustering

Arora

Olshen

Seshan

et al. 2020

Preprint

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9 Molecular phenotypes of cancer are complex and influenced by a multitude of factors. Conventional 1 0 unsupervised clustering of heterogeneous cancer patient populations is inevitably driven by the dominant 1 1 variation from major factors such as cell-of-origin or histology. Drawing from ideas in supervised text 1 2 classification, we developed survClust, an outcome-weighted clustering algorithm for integrative patient 1 3 stratification. We show survClust outperforms unsupervised clustering in identifying cancer patient 1 4 subpopulations characterized by specific genomic phenotypes with more aggressive clinical behavior.

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Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival

Cited by 135 publications

References 42 publications

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer

Pan-cancer identification of clinically relevant genomic subtypes using outcome-weighted integrative clustering

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