Evidence map›Paper›PMID 34745946›Full record

ArticleFrontiers in oncology2021

SMRT: Randomized Data Transformation for Cancer Subtyping and Big Data Analysis.

Hung Nguyen, Duc Tran, Bang Tran, Monikrishna Roy, Adam Cassell, Sergiu Dascalu, Sorin Draghici, Tin Nguyen

Abstract read
In one paragraph

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. CSIE: cancer subtyping via inference and ensemble.Briefings in bioinformatics · 2026
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Hung NguyenDepartment of Computer Science and Engineering, University of Nevada Reno, Reno, NV, United States.
Duc TranDepartment of Computer Science and Engineering, University of Nevada Reno, Reno, NV, United States.
Bang TranDepartment of Computer Science and Engineering, University of Nevada Reno, Reno, NV, United States.
Monikrishna RoyDepartment of Computer Science and Engineering, University of Nevada Reno, Reno, NV, United States.
Adam CassellDepartment of Computer Science and Engineering, University of Nevada Reno, Reno, NV, United States.
Sergiu DascaluDepartment of Computer Science and Engineering, University of Nevada Reno, Reno, NV, United States.
Sorin DraghiciDepartment of Computer Science, Wayne State University, Detroit, MI, United States.
Tin NguyenDepartment of Computer Science and Engineering, University of Nevada Reno, Reno, NV, United States.

Funding

Outreach/Diversity/Pipeline ProjectP20GM103440 · NIGMS · UNIVERSITY OF NEVADA RENO · PI Julia Petereit · 2012 to 2026
$58.0M
NIGMS NIH HHS P20 GM103440
6 · The paper itself

Abstract

Cancer is an umbrella term that includes a range of disorders, from those that are fast-growing and lethal to indolent lesions with low or delayed potential for progression to death. The treatment options, as well as treatment success, are highly dependent on the correct subtyping of individual patients. With the advancement of high-throughput platforms, we have the opportunity to differentiate among cancer subtypes from a holistic perspective that takes into consideration phenomena at different molecular levels (mRNA, methylation, etc.). This demands powerful integrative methods to leverage large multi-omics datasets for a better subtyping. Here we introduce Subtyping Multi-omics using a Randomized Transformation (SMRT), a new method for multi-omics integration and cancer subtyping. SMRT offers the following advantages over existing approaches: (i) the scalable analysis pipeline allows researchers to integrate multi-omics data and analyze hundreds of thousands of samples in minutes, (ii) the ability to integrate data types with different numbers of patients, (iii) the ability to analyze un-matched data of different types, and (iv) the ability to offer users a convenient data analysis pipeline through a web application. We also improve the efficiency of our ensemble-based, perturbation clustering to support analysis on machines with memory constraints. In an extensive analysis, we compare SMRT with eight state-of-the-art subtyping methods using 37 TCGA and two METABRIC datasets comprising a total of almost 12,000 patient samples from 28 different types of cancer. We also performed a number of simulation studies. We demonstrate that SMRT outperforms other methods in identifying subtypes with significantly different survival profiles. In addition, SMRT is extremely fast, being able to analyze hundreds of thousands of samples in minutes. The web application is available at http://SMRT.tinnguyen-lab.com. The R package will be deposited to CRAN as part of our PINSPlus software suite.

Indexed as

cancer subtypingCRAN packagemulti-omics integrationsurvival analysisweb application

Identifiers

PMID34745946
PMCPMC8563705

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.